You searched for subject:(Hidden Markov Model)
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Texas A&M University
1.
Wang, Xiduo.
A State Dependent Lane-Changing Model for Urban Arterials with Hidden Markov Model Method.
Degree: MS, Civil Engineering, 2015, Texas A&M University
URL: http://hdl.handle.net/1969.1/156266
► The inherent intention and decision process of lane changes are complex and unobservable. Though the external environment and traffic conditions are changing along the traveling…
(more)
▼ The inherent intention and decision process of lane changes are complex and unobservable. Though the external environment and traffic conditions are changing along the traveling direction, the drivers’ characteristics and preferences may lead to persistence of preferable lane choices.
Hidden Markov Model (HMM) method is used to
model the system that involves unobservable factors, such as speech recognition and biological sequence problems. The
hidden process are assumed to associate with observable outcomes.
In this study, HMM is integrated into a two-stage lane-changing
model to better represent the mandatory lane-changing behaviors on arterials. The lane-changing decision process is separated into two steps: decision to target a lane as the desire lane and acceptance of available gaps in the chosen direction. The outcome of the first step is unobservable and treated as the latent state in HMM. The second step, gap acceptance
model, relates the outcome of the first step to observed vehicle trajectories.
The proposed
model is estimated and validated using detail Next Generation Simulation (NGSIM) vehicle trajectory data from Lankershim Boulevard. Comparison between generated lane position sequences and original trajectories validated the model’s capability of representing mandatory lane changes. There is an average 17% difference on predicted lane change locations compared to observed locations; while lane change locations to left turn lane and right turn lane show 10% and 13% difference respectively. The generated lane changes show a late tendency of movements among through lanes. The results show that the
model is fit for the purpose of representing mandatory lane change behaviors on arterials. The research highlights some future improvements of proposed lane-changing
model on arterials.
Advisors/Committee Members: Zhang, Yunlong (advisor), Wang, Xiubin Bruce (committee member), Spiegelman, Clifford (committee member).
Subjects/Keywords: Lane Changing; Hidden Markov Model
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Chicago ·
MLA ·
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APA (6th Edition):
Wang, X. (2015). A State Dependent Lane-Changing Model for Urban Arterials with Hidden Markov Model Method. (Masters Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/156266
Chicago Manual of Style (16th Edition):
Wang, Xiduo. “A State Dependent Lane-Changing Model for Urban Arterials with Hidden Markov Model Method.” 2015. Masters Thesis, Texas A&M University. Accessed March 01, 2021.
http://hdl.handle.net/1969.1/156266.
MLA Handbook (7th Edition):
Wang, Xiduo. “A State Dependent Lane-Changing Model for Urban Arterials with Hidden Markov Model Method.” 2015. Web. 01 Mar 2021.
Vancouver:
Wang X. A State Dependent Lane-Changing Model for Urban Arterials with Hidden Markov Model Method. [Internet] [Masters thesis]. Texas A&M University; 2015. [cited 2021 Mar 01].
Available from: http://hdl.handle.net/1969.1/156266.
Council of Science Editors:
Wang X. A State Dependent Lane-Changing Model for Urban Arterials with Hidden Markov Model Method. [Masters Thesis]. Texas A&M University; 2015. Available from: http://hdl.handle.net/1969.1/156266

Rice University
2.
Ackermann, Etienne Rudolph.
Latent variable models for hippocampal sequence analysis.
Degree: PhD, Engineering, 2019, Rice University
URL: http://hdl.handle.net/1911/106148
► Place cell activity of hippocampal pyramidal cells has been described as the cognitive substrate of spatial memory. Indeed, the activity of ensembles of neurons within…
(more)
▼ Place cell activity of hippocampal pyramidal cells has been described as the cognitive substrate of spatial memory. Indeed, the activity of ensembles of neurons within the hippocampus is thought to enable memory formation, storage, recall, and even decision making. Replay is observed during hippocampal sharp-wave-ripple-associated population burst events (PBEs) and is critical for consolidation and recall-guided behaviors. Notably, these PBEs occur during times of inactivity, so that their representations cannot easily be matched with observable animal behavior.
In my thesis, I present an approach to uncover temporal structure within hippocampal output patterns during PBEs. More specifically, I use
hidden Markov models (HMMs) to study PBEs observed in rats during exploration of both linear tracks and open fields, and I demonstrate that estimated models are consistent with a spatial map of the environment. Moreover, I demonstrate how the
model can be used to identify hippocampal replay without recourse to the place code. These results suggest that downstream regions may rely on PBEs to provide a substrate for memory. Moreover, by forming models independent of animal behavior, I lay the groundwork for studies of non-spatial memory.
Next, I present a new
model, the "clusterless" switching Poisson
hidden Markov model, which extends my work on HMMs of PBEs to the case where we only have multiunit (unsorted) spikes. Indeed, spike sorting is challenging, time-consuming, often subjective (not reproducible), and throws away potentially valuable information from unsorted spikes, as well as our certainty about the cluster assignments. It has previously been shown that we can often do just as well, or in some cases even better, if we forego the spike sorting process altogether, and work directly with the unsorted data. Consequently, my clusterless HMM will enable us to combine the benefits of unsupervised learning for internally generated neural activity, with the benefits of clusterless approaches (more data leading to higher fidelity, especially at fine temporal scales, and additional probabilistic / soft information to exploit). I demonstrate the
model's ability to recover
model parameters for simulated data, and show that it is able to learn a spatially-consistent representation of the environment from real experimental data.
Advisors/Committee Members: Kemere, Caleb (advisor).
Subjects/Keywords: hippocampal replay; hidden Markov model
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Ackermann, E. R. (2019). Latent variable models for hippocampal sequence analysis. (Doctoral Dissertation). Rice University. Retrieved from http://hdl.handle.net/1911/106148
Chicago Manual of Style (16th Edition):
Ackermann, Etienne Rudolph. “Latent variable models for hippocampal sequence analysis.” 2019. Doctoral Dissertation, Rice University. Accessed March 01, 2021.
http://hdl.handle.net/1911/106148.
MLA Handbook (7th Edition):
Ackermann, Etienne Rudolph. “Latent variable models for hippocampal sequence analysis.” 2019. Web. 01 Mar 2021.
Vancouver:
Ackermann ER. Latent variable models for hippocampal sequence analysis. [Internet] [Doctoral dissertation]. Rice University; 2019. [cited 2021 Mar 01].
Available from: http://hdl.handle.net/1911/106148.
Council of Science Editors:
Ackermann ER. Latent variable models for hippocampal sequence analysis. [Doctoral Dissertation]. Rice University; 2019. Available from: http://hdl.handle.net/1911/106148

Rice University
3.
Ackermann, Etienne Rudolph.
Latent variable models for hippocampal sequence analysis.
Degree: PhD, Neuroengineering, 2019, Rice University
URL: http://hdl.handle.net/1911/106149
► Place cell activity of hippocampal pyramidal cells has been described as the cognitive substrate of spatial memory. Indeed, the activity of ensembles of neurons within…
(more)
▼ Place cell activity of hippocampal pyramidal cells has been described as the cognitive substrate of spatial memory. Indeed, the activity of ensembles of neurons within the hippocampus is thought to enable memory formation, storage, recall, and even decision making. Replay is observed during hippocampal sharp-wave-ripple-associated population burst events (PBEs) and is critical for consolidation and recall-guided behaviors. Notably, these PBEs occur during times of inactivity, so that their representations cannot easily be matched with observable animal behavior.
In my thesis, I present an approach to uncover temporal structure within hippocampal output patterns during PBEs. More specifically, I use
hidden Markov models (HMMs) to study PBEs observed in rats during exploration of both linear tracks and open fields, and I demonstrate that estimated models are consistent with a spatial map of the environment. Moreover, I demonstrate how the
model can be used to identify hippocampal replay without recourse to the place code. These results suggest that downstream regions may rely on PBEs to provide a substrate for memory. Moreover, by forming models independent of animal behavior, I lay the groundwork for studies of non-spatial memory.
Next, I present a new
model, the "clusterless" switching Poisson
hidden Markov model, which extends my work on HMMs of PBEs to the case where we only have multiunit (unsorted) spikes. Indeed, spike sorting is challenging, time-consuming, often subjective (not reproducible), and throws away potentially valuable information from unsorted spikes, as well as our certainty about the cluster assignments. It has previously been shown that we can often do just as well, or in some cases even better, if we forego the spike sorting process altogether, and work directly with the unsorted data. Consequently, my clusterless HMM will enable us to combine the benefits of unsupervised learning for internally generated neural activity, with the benefits of clusterless approaches (more data leading to higher fidelity, especially at fine temporal scales, and additional probabilistic / soft information to exploit). I demonstrate the
model's ability to recover
model parameters for simulated data, and show that it is able to learn a spatially-consistent representation of the environment from real experimental data.
Advisors/Committee Members: Kemere, Caleb (advisor).
Subjects/Keywords: hippocampal replay; hidden Markov model
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Ackermann, E. R. (2019). Latent variable models for hippocampal sequence analysis. (Doctoral Dissertation). Rice University. Retrieved from http://hdl.handle.net/1911/106149
Chicago Manual of Style (16th Edition):
Ackermann, Etienne Rudolph. “Latent variable models for hippocampal sequence analysis.” 2019. Doctoral Dissertation, Rice University. Accessed March 01, 2021.
http://hdl.handle.net/1911/106149.
MLA Handbook (7th Edition):
Ackermann, Etienne Rudolph. “Latent variable models for hippocampal sequence analysis.” 2019. Web. 01 Mar 2021.
Vancouver:
Ackermann ER. Latent variable models for hippocampal sequence analysis. [Internet] [Doctoral dissertation]. Rice University; 2019. [cited 2021 Mar 01].
Available from: http://hdl.handle.net/1911/106149.
Council of Science Editors:
Ackermann ER. Latent variable models for hippocampal sequence analysis. [Doctoral Dissertation]. Rice University; 2019. Available from: http://hdl.handle.net/1911/106149

NSYSU
4.
Huang, Yu-Zhi.
Detecting Attack Sequence in Cloud Based on Hidden Markov Model.
Degree: Master, Computer Science and Engineering, 2012, NSYSU
URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0726112-150041
► Cloud computing provides business new working paradigm with the benefit of cost reduce and resource sharing. Tasks from different users may be performed on the…
(more)
▼ Cloud computing provides business new working paradigm with the benefit of cost reduce and resource sharing. Tasks from different users may be performed on the same machine. Therefore, one primary security concern is whether user data is secure in cloud. On the other hand, hacker may facilitate cloud computing to launch larger range of attack, such as a request of port scan in cloud with virtual machines executing such malicious action. In addition, hacker may perform a sequence of attacks in order to compromise his target system in cloud, for example, evading an easy-to-exploit machine in a cloud and then using the previous compromised to attack the target. Such attack plan may be stealthy or inside the computing environment, so intrusion detection system or firewall has difficulty to identify it.
The proposed detection system analyzes logs from cloud to extract the intensions of the actions recorded in logs. Stealthy reconnaissance actions are often neglected by administrator for the insignificant number of violations.
Hidden Markov model is adopted to
model the sequence of attack performed by hacker and such stealthy events in a long time frame will become significant in the state-aware
model. The preliminary results show that the proposed system can identify such attack plans in the real network.
Advisors/Committee Members: Chia-Mei Chen (chair), D. J. Guan (committee member), Chun-I Fan (chair).
Subjects/Keywords: Cloud Computing; Hidden Markov Model; Attack Plan
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Huang, Y. (2012). Detecting Attack Sequence in Cloud Based on Hidden Markov Model. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0726112-150041
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Huang, Yu-Zhi. “Detecting Attack Sequence in Cloud Based on Hidden Markov Model.” 2012. Thesis, NSYSU. Accessed March 01, 2021.
http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0726112-150041.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Huang, Yu-Zhi. “Detecting Attack Sequence in Cloud Based on Hidden Markov Model.” 2012. Web. 01 Mar 2021.
Vancouver:
Huang Y. Detecting Attack Sequence in Cloud Based on Hidden Markov Model. [Internet] [Thesis]. NSYSU; 2012. [cited 2021 Mar 01].
Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0726112-150041.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Huang Y. Detecting Attack Sequence in Cloud Based on Hidden Markov Model. [Thesis]. NSYSU; 2012. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0726112-150041
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
5.
Nut Sornchumni.
Research on Parameter Estimation of Hidden Markov Model with Differential Privacy : 隠れマルコフモデルに対するパラメータ推定アルゴリズムの差分プライバシー化に関する研究; カクレ マルコフ モデル ニ タイスル パラメータ スイテイ アルゴリズム ノ サブン プライバシーカ ニ カンスル ケンキュウ.
Degree: Nara Institute of Science and Technology / 奈良先端科学技術大学院大学
URL: http://hdl.handle.net/10061/9846
Subjects/Keywords: Hidden Markov model
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Sornchumni, N. (n.d.). Research on Parameter Estimation of Hidden Markov Model with Differential Privacy : 隠れマルコフモデルに対するパラメータ推定アルゴリズムの差分プライバシー化に関する研究; カクレ マルコフ モデル ニ タイスル パラメータ スイテイ アルゴリズム ノ サブン プライバシーカ ニ カンスル ケンキュウ. (Thesis). Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Retrieved from http://hdl.handle.net/10061/9846
Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Sornchumni, Nut. “Research on Parameter Estimation of Hidden Markov Model with Differential Privacy : 隠れマルコフモデルに対するパラメータ推定アルゴリズムの差分プライバシー化に関する研究; カクレ マルコフ モデル ニ タイスル パラメータ スイテイ アルゴリズム ノ サブン プライバシーカ ニ カンスル ケンキュウ.” Thesis, Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Accessed March 01, 2021.
http://hdl.handle.net/10061/9846.
Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Sornchumni, Nut. “Research on Parameter Estimation of Hidden Markov Model with Differential Privacy : 隠れマルコフモデルに対するパラメータ推定アルゴリズムの差分プライバシー化に関する研究; カクレ マルコフ モデル ニ タイスル パラメータ スイテイ アルゴリズム ノ サブン プライバシーカ ニ カンスル ケンキュウ.” Web. 01 Mar 2021.
Note: this citation may be lacking information needed for this citation format:
No year of publication.
Vancouver:
Sornchumni N. Research on Parameter Estimation of Hidden Markov Model with Differential Privacy : 隠れマルコフモデルに対するパラメータ推定アルゴリズムの差分プライバシー化に関する研究; カクレ マルコフ モデル ニ タイスル パラメータ スイテイ アルゴリズム ノ サブン プライバシーカ ニ カンスル ケンキュウ. [Internet] [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; [cited 2021 Mar 01].
Available from: http://hdl.handle.net/10061/9846.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.
Council of Science Editors:
Sornchumni N. Research on Parameter Estimation of Hidden Markov Model with Differential Privacy : 隠れマルコフモデルに対するパラメータ推定アルゴリズムの差分プライバシー化に関する研究; カクレ マルコフ モデル ニ タイスル パラメータ スイテイ アルゴリズム ノ サブン プライバシーカ ニ カンスル ケンキュウ. [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; Available from: http://hdl.handle.net/10061/9846
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

Penn State University
6.
Osotsi, Ame J.
Event Detection in Twitter Data: A Hidden Markov Model-Based Change Point Algorithm.
Degree: 2016, Penn State University
URL: https://submit-etda.libraries.psu.edu/catalog/vm40xr56k
► Twitter is a popular microblogging platform that displays real-time status updates from over 140 million users a day. The users post about anything, from daily…
(more)
▼ Twitter is a popular microblogging platform that displays real-time status updates from over 140 million users a day. The users post about anything, from daily life events to important global events. We attempt to analyze this rich source of user-generated data using
hidden Markov models, which have been very successful in describing time series observations. The aim of this project is to quantify how conversation in Twitter evolve in response to two major events: an unexpected school shooting, and the Super Bowl. We use a
hidden Markov model-based change point algorithm. This thesis first introduces the data and the
hidden Markov models underlying the change point algorithm. We then describe the change point algorithm and related problems such as finding confidence intervals,
model selection, and computing summaries. Finally, we show results on the two datasets and propose future avenues of research.
Advisors/Committee Members: Dr Qunhua Li, Thesis Advisor/Co-Advisor, Dr Dave Hunter, Committee Member, Dr John Fricks, Committee Member.
Subjects/Keywords: Change Point; Hidden Markov Model; Twitter
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Osotsi, A. J. (2016). Event Detection in Twitter Data: A Hidden Markov Model-Based Change Point Algorithm. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/vm40xr56k
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Osotsi, Ame J. “Event Detection in Twitter Data: A Hidden Markov Model-Based Change Point Algorithm.” 2016. Thesis, Penn State University. Accessed March 01, 2021.
https://submit-etda.libraries.psu.edu/catalog/vm40xr56k.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Osotsi, Ame J. “Event Detection in Twitter Data: A Hidden Markov Model-Based Change Point Algorithm.” 2016. Web. 01 Mar 2021.
Vancouver:
Osotsi AJ. Event Detection in Twitter Data: A Hidden Markov Model-Based Change Point Algorithm. [Internet] [Thesis]. Penn State University; 2016. [cited 2021 Mar 01].
Available from: https://submit-etda.libraries.psu.edu/catalog/vm40xr56k.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Osotsi AJ. Event Detection in Twitter Data: A Hidden Markov Model-Based Change Point Algorithm. [Thesis]. Penn State University; 2016. Available from: https://submit-etda.libraries.psu.edu/catalog/vm40xr56k
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

NSYSU
7.
Liu, Lun-Kang.
The Analysis and Forecasting of the Bitcoin Return.
Degree: Master, Institute Of Applied Mathematics, 2018, NSYSU
URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0603118-164022
► The feasibility of cryptocurrency as an investment tool has been a topic of considerable concern in recent years. Bitcoin is a cryptocurrency that is universally…
(more)
▼ The feasibility of cryptocurrency as an investment tool has been a topic of considerable concern in recent years. Bitcoin is a cryptocurrency that is universally used regardless of regional restrictions. When Bitcoin is considered as an investment tool, it operates in the same way as investing in legal currencies, such as: US Dollars, Euros, and British Pounds. However, due to the fluctuations of Bitcoin return are larger than the legal currencies, Bitcoin return forecasting is a big challenge compared to those for the legal currencies. In order to predict the return of Bitcoin, this thesis tries to build up a prediction
model based on the historical data of the Bitcoin and other top five cryptocurrencies such as the Litecoin, Ethereum, Ripple, and Dash which have a longer development time. Moreover, we include the information obtained from text mining analyzing the Bitcoin online forum articles to find the keyword sentiment scores as explanatory variables into the prediction
model as well. The
hidden Markov model (HMM) has been used to predict the trend of the Bitcoin daily rate of return. The prediction of the ups and downs trend of Bitcoin on the next day with acceptable accuracy should be helpful for making decisions on the investment strategy of Bitcoin.
Advisors/Committee Members: Mei-Hui Guo (chair), Huang, Shih-Feng (chair), Mong-Na Lo Huang (committee member), Fu-Chuen Chang (chair).
Subjects/Keywords: Text mining; Hidden Markov model; Cryptocurrency
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Liu, L. (2018). The Analysis and Forecasting of the Bitcoin Return. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0603118-164022
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Liu, Lun-Kang. “The Analysis and Forecasting of the Bitcoin Return.” 2018. Thesis, NSYSU. Accessed March 01, 2021.
http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0603118-164022.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Liu, Lun-Kang. “The Analysis and Forecasting of the Bitcoin Return.” 2018. Web. 01 Mar 2021.
Vancouver:
Liu L. The Analysis and Forecasting of the Bitcoin Return. [Internet] [Thesis]. NSYSU; 2018. [cited 2021 Mar 01].
Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0603118-164022.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Liu L. The Analysis and Forecasting of the Bitcoin Return. [Thesis]. NSYSU; 2018. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0603118-164022
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of North Texas
8.
Saudagar, Abdullah Naseer Ahmed.
Automatic Extraction of Highlights from a Baseball Video Using HMM and MPEG-7 Descriptors.
Degree: 2011, University of North Texas
URL: https://digital.library.unt.edu/ark:/67531/metadc103388/
► In today’s fast paced world, as the number of stations of television programming offered is increasing rapidly, time accessible to watch them remains same or…
(more)
▼ In today’s fast paced world, as the number of stations of television programming offered is increasing rapidly, time accessible to watch them remains same or decreasing. Sports videos are typically lengthy and they appeal to a massive crowd. Though sports video is lengthy, most of the viewer’s desire to watch specific segments of the video which are fascinating, like a home-run in a baseball or goal in soccer i.e., users prefer to watch highlights to save time. When associated to the entire span of the video, these segments form only a minor share. Hence these videos need to be summarized for effective presentation and data management. This thesis explores the ability to extract highlights automatically using MPEG-7 features and
hidden Markov model (HMM), so that viewing time can be reduced. Video is first segmented into scene shots, in which the detection of the shot is the fundamental task. After the video is segmented into shots, extraction of key frames allows a suitable representation of the whole shot. Feature extraction is crucial processing step in the classification, video indexing and retrieval system. Frame features such as color, motion, texture, edges are extracted from the key frames. A baseball highlight contains certain types of scene shots and these shots follow a particular transition pattern. The shots are classified as close-up, out-field, base and audience. I first try to identify the type of the shot using low level features extracted from the key frames of each shot. For the identification of the highlight I use the
hidden Markov model using the transition pattern of the shots in time domain. Experimental results suggest that with reasonable accuracy highlights can be extracted from the video.
Advisors/Committee Members: Namuduri, Kamesh, Guturu, Parthasarathy, Varanasi, Murali R..
Subjects/Keywords: highlights; descriptors; MPEG-7; hidden Markov model
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University of Texas – Austin
9.
Li, Na.
Hidden Markov model and financial application.
Degree: MSin Statistics, Statistics, 2016, University of Texas – Austin
URL: http://hdl.handle.net/2152/47043
► A Hidden Markov model (HMM) is a statistical model in which the system being modeled is assumed to be a Markov process with numerous unobserved…
(more)
▼ A
Hidden Markov model (HMM) is a statistical
model in which the system being modeled is assumed to be a
Markov process with numerous unobserved (
hidden) states. This report applies HMM to financial time series data to explore the underlying regimes that can be predicted by the
model. These underlying regimes can be used as an important signal of market environments and used as guidance by investors to adjust their portfolio to maximize the performance. This report is composed of three chapters. The 1st chapter will introduce the difficulties in predicting financial time series, the limitations with traditional time series models, justification for choosing HMM and previous studies. The 2nd chapter will go through a detailed overview of HMM
model, including the basic math frame works, and fundamental questions and algorithm to be addressed by the
model. In the 3rd chapter, the trend analysis of the stock market is found using
Hidden Markov Model. For a given observation sequence, the
hidden sequence of states and their corresponding probability values are found. This analysis builds a platform for investors to decision makers to make decisions on the basis of probability and pattern of transition of each
hidden state which cannot be observed from market data.
Advisors/Committee Members: Lin, Lizhen (advisor), Margaret, Myers (committee member).
Subjects/Keywords: Hidden Markov model; Financial time series
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APA (6th Edition):
Li, N. (2016). Hidden Markov model and financial application. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/47043
Chicago Manual of Style (16th Edition):
Li, Na. “Hidden Markov model and financial application.” 2016. Masters Thesis, University of Texas – Austin. Accessed March 01, 2021.
http://hdl.handle.net/2152/47043.
MLA Handbook (7th Edition):
Li, Na. “Hidden Markov model and financial application.” 2016. Web. 01 Mar 2021.
Vancouver:
Li N. Hidden Markov model and financial application. [Internet] [Masters thesis]. University of Texas – Austin; 2016. [cited 2021 Mar 01].
Available from: http://hdl.handle.net/2152/47043.
Council of Science Editors:
Li N. Hidden Markov model and financial application. [Masters Thesis]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/47043

University of Texas – Austin
10.
-6024-5216.
Uncertainty quantification and its properties for hidden Markov models with application to condition based maintenance.
Degree: PhD, Operations Research and Industrial Engineering, 2018, University of Texas – Austin
URL: http://hdl.handle.net/2152/63715
► Condition-based maintenance (CBM) can be viewed as a transformation of data gathered from a piece of equipment into information about its condition, and further into…
(more)
▼ Condition-based maintenance (CBM) can be viewed as a transformation of data gathered from a piece of equipment into information about its condition, and further into decisions on what to do with the equipment.
Hidden Markov model (HMM) is a useful framework to probabilistically
model the condition of complex engineering systems with partial observability of the underlying states. Condition monitoring and prediction of such type of system requires accurate knowledge of HMM that describes the degradation of such a system with data collected from the sensors mounted on it, as well as understanding of the uncertainty of the HMMs identified from the available data. To that end, this thesis proposes a novel HMM estimation scheme based on the principles of Bayes theorem. The newly proposed Bayesian estimation approach for estimating HMM parameters naturally yields information about
model parametric uncertainties via posterior distributions of HMM parameters emanating from the estimation process. In addition, a novel condition monitoring scheme based on uncertain
HMMs of the degradation process is proposed and demonstrated on a large dataset obtained from a semiconductor manufacturing facility. Portion of the data was used to build operating mode specific HMMs of machine degradation via the newly proposed Bayesian estimation process, while the remainder of the data was used for monitoring of machine condition using the uncertain degradation HMMs yielded by Bayesian estimation. Comparison with a traditional signature-based statistical monitoring method showed that the newly proposed approach effectively utilizes the fact that its parameters are uncertain themselves, leading to orders of magnitude fewer false alarms. This methodology is further extended to address the practical issue that maintenance interventions are usually imperfect. We propose both a novel non-ergodic and non-homogeneous HMM that assumes imperfect maintenances and a novel process monitoring method capable of monitoring the
hidden states considering
model uncertainty. Significant improvement in both the log-likelihood of estimated HMM parameters and monitoring performance were observed, compared to those obtained using degradation HMMs that always assumed perfect maintenance.
Finally, behavior of the posterior distribution of parameters of unidirectional non- ergodic HMMs modeling in this thesis for degradation was theoretically analyzed in terms of their evolution as more data become available in the estimation process. The convergence problem is formulated as a Bernstein-von Mises theorem (BvMT), and under certain regularity conditions, the sequence of posterior distributions is proven to converge to a Gaussian distribution with variance matrix being the inverse of the Fisher information matrix. An example of a unidirectional HMM is presented for which the regularity conditions are verified, and illustrations of expected theoretical results are given using simulation. The understanding of such convergence of posterior distributions
enables one to…
Advisors/Committee Members: Djurdjanovic, Dragan (advisor), Hasenbein, John (committee member), Bickel, James Eric (committee member), Walker, Stephen G (committee member), Hanasusanto, Grani (committee member).
Subjects/Keywords: Hidden Markov model; Condition-based maintenance
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
-6024-5216. (2018). Uncertainty quantification and its properties for hidden Markov models with application to condition based maintenance. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/63715
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Chicago Manual of Style (16th Edition):
-6024-5216. “Uncertainty quantification and its properties for hidden Markov models with application to condition based maintenance.” 2018. Doctoral Dissertation, University of Texas – Austin. Accessed March 01, 2021.
http://hdl.handle.net/2152/63715.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
MLA Handbook (7th Edition):
-6024-5216. “Uncertainty quantification and its properties for hidden Markov models with application to condition based maintenance.” 2018. Web. 01 Mar 2021.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Vancouver:
-6024-5216. Uncertainty quantification and its properties for hidden Markov models with application to condition based maintenance. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2018. [cited 2021 Mar 01].
Available from: http://hdl.handle.net/2152/63715.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Council of Science Editors:
-6024-5216. Uncertainty quantification and its properties for hidden Markov models with application to condition based maintenance. [Doctoral Dissertation]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/63715
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Virginia Tech
11.
Liu, Mingming.
Predicting the Functional Effects of Human Short Variations Using Hidden Markov Models.
Degree: PhD, Computer Science and Applications, 2015, Virginia Tech
URL: http://hdl.handle.net/10919/73703
► With the development of sequencing technologies, more and more sequence variants are available for investigation. Different types of variants in the human genome have been…
(more)
▼ With the development of sequencing technologies, more and more sequence variants are available for investigation. Different types of variants in the human genome have been identified, including single nucleotide polymorphisms (SNPs), short insertions and deletions (indels), and large structural variations such as large duplications and deletions. Of great research interest is the functional effects of these variants. Although many programs have been developed to predict the effect of SNPs,
few can be used to predict the effect of indels or multiple variants, such as multiple SNPs,
multiple indels, or a combination of both. Moreover, fine grained prediction of the functional outcome
of variants is not available. To address these limitations, we developed a prediction framework, HMMvar, to predict the functional effects of coding variants (SNPs or indels), using profile
hidden Markov models (HMMs). Based on HMMvar, we proposed HMMvar-multi to explore the joint effects of multiple variants in the same gene. For fine grained functional outcome prediction, we developed HMMvar-func to computationally define and predict four types of functional outcome of a variant: gain, loss, switch, and conservation of function.
Advisors/Committee Members: Zhang, Liqing (committeechair), Heath, Lenwood S. (committee member), Wu, Xiaowei (committee member), Hu, Jianjun (committee member), Watson, Layne T. (committee member).
Subjects/Keywords: Genetic variation; Indel; SNP; Hidden Markov Model
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Liu, M. (2015). Predicting the Functional Effects of Human Short Variations Using Hidden Markov Models. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/73703
Chicago Manual of Style (16th Edition):
Liu, Mingming. “Predicting the Functional Effects of Human Short Variations Using Hidden Markov Models.” 2015. Doctoral Dissertation, Virginia Tech. Accessed March 01, 2021.
http://hdl.handle.net/10919/73703.
MLA Handbook (7th Edition):
Liu, Mingming. “Predicting the Functional Effects of Human Short Variations Using Hidden Markov Models.” 2015. Web. 01 Mar 2021.
Vancouver:
Liu M. Predicting the Functional Effects of Human Short Variations Using Hidden Markov Models. [Internet] [Doctoral dissertation]. Virginia Tech; 2015. [cited 2021 Mar 01].
Available from: http://hdl.handle.net/10919/73703.
Council of Science Editors:
Liu M. Predicting the Functional Effects of Human Short Variations Using Hidden Markov Models. [Doctoral Dissertation]. Virginia Tech; 2015. Available from: http://hdl.handle.net/10919/73703

Anna University
12.
Jagadeesh Kannan R.
Handwritten character recognition using hybrid
techniques.
Degree: Information and Communication, 2013, Anna University
URL: http://shodhganga.inflibnet.ac.in/handle/10603/9849
► The primary objective of this research work is to develop a character recognition system, which can further be developed to recognize all printed and handwritten…
(more)
▼ The primary objective of this research work is to
develop a character recognition system, which can further be
developed to recognize all printed and handwritten Tamil
characters. Such a system will be extremely useful to digitize
innumerable ancient documents available both on paper and palm
leaves and are dated few hundred years back. The problem considered
is to develop a recognition system for Printed and Handwritten
Tamil and Numeral Characters. An overview of the research work is
presented below. The first component of this research work focuses
on technique to recognize handwritten Tamil characters using a
Hidden Markov Model (HMM) approach, for a subset of the Tamil
alphabet. The second component of this research work also focuses
on technique to recognize handwritten Tamil characters using an
Octal Graph approach. The third component introduces a methodology
to recognize handwritten characters using Structural Hidden Markov
Models (SHMM). The proposed approach is motivated by the need to
model complex structures which are encountered in many areas such
as speech/handwriting recognition, content-based information
retrieval etc. The observations considered are strings that produce
the structures. These observations are related in the sense they
all contribute to produce a particular structure. The recognition
efficiency of the system is 96.5%. The results reported in this
component shows that the Structural Hidden Markov Model (SHMM)
produces better recognition than the Hidden Markov Model. A
character recognition system was successfully developed for subset
of Tamil characters and numerals and found to perform reasonably
well with sufficient accuracy. With this preliminary study of
character recognition systems it is planned to continue the work in
future. A more complete OCR will evolve in public domain with this
work as the starting point, so that the primary source of
motivation will benefit from the efforts.
None
Advisors/Committee Members: Suresh R M.
Subjects/Keywords: Structural Hidden Markov Model; Hidden Markov Model; Character recognition system; Tamil character
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
R, J. K. (2013). Handwritten character recognition using hybrid
techniques. (Thesis). Anna University. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/9849
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
R, Jagadeesh Kannan. “Handwritten character recognition using hybrid
techniques.” 2013. Thesis, Anna University. Accessed March 01, 2021.
http://shodhganga.inflibnet.ac.in/handle/10603/9849.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
R, Jagadeesh Kannan. “Handwritten character recognition using hybrid
techniques.” 2013. Web. 01 Mar 2021.
Vancouver:
R JK. Handwritten character recognition using hybrid
techniques. [Internet] [Thesis]. Anna University; 2013. [cited 2021 Mar 01].
Available from: http://shodhganga.inflibnet.ac.in/handle/10603/9849.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
R JK. Handwritten character recognition using hybrid
techniques. [Thesis]. Anna University; 2013. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/9849
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Virginia Tech
13.
Ali Akbar Soltan, Reza.
Enhancements in Markovian Dynamics.
Degree: PhD, Mechanical Engineering, 2012, Virginia Tech
URL: http://hdl.handle.net/10919/77345
► Many common statistical techniques for modeling multidimensional dynamic data sets can be seen as variants of one (or multiple) underlying linear/nonlinear model(s). These statistical techniques…
(more)
▼ Many common statistical techniques for modeling multidimensional dynamic data sets can be seen as variants of one (or multiple) underlying linear/nonlinear
model(s). These statistical techniques fall into two broad categories of supervised and unsupervised learning. The emphasis of this dissertation is on unsupervised learning under multiple generative models. For linear models, this has been achieved by collective observations and derivations made by previous authors during the last few decades. Factor analysis, polynomial chaos expansion, principal component analysis, gaussian mixture clustering, vector quantization, and Kalman filter models can all be unified as some variations of unsupervised learning under a single basic linear generative
model.
Hidden Markov modeling (HMM), however, is categorized as an unsupervised learning under multiple linear/nonlinear generative models. This dissertation is primarily focused on
hidden Markov models (HMMs).
On the first half of this dissertation we study enhancements on the theory of
hidden Markov modeling. These include three branches: 1) a robust as well as a closed-form parameter estimation solution to the expectation maximization (EM) process of HMMs for the case of elliptically symmetrical densities; 2) a two-step HMM, with a combined state sequence via an extended Viterbi algorithm for smoother state estimation; and 3) a duration-dependent HMM, for estimating the expected residency frequency on each state. Then, the second half of the dissertation studies three novel applications of these methods: 1) the applications of
Markov switching models on the Bifurcation Theory in nonlinear dynamics; 2) a Game Theory application of HMM, based on fundamental theory of card counting and an example on the game of Baccarat; and 3) Trust modeling and the estimation of trustworthiness metrics in cyber security systems via
Markov switching models.
As a result of the duration dependent HMM, we achieved a better estimation for the expected duration of stay on each regime. Then by robust and closed form solution to the EM algorithm we achieved robustness against outliers in the training data set as well as higher computational efficiency in the maximization step of the EM algorithm. By means of the two-step HMM we achieved smoother probability estimation with higher likelihood than the standard HMM.
Advisors/Committee Members: Ahmadian, Mehdi (committeechair), Hall, T. Simin (committee member), Southward, Steve C. (committee member), Asl, Farshid M. (committee member), Ball, Joseph A. (committee member), Taheri, Saied (committee member).
Subjects/Keywords: Duration Dependent Hidden Markov; Expectation Maximization; Nonlinear Stochastic Model; Hidden Markov Model; Maximum Likelihood Estimation
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Ali Akbar Soltan, R. (2012). Enhancements in Markovian Dynamics. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/77345
Chicago Manual of Style (16th Edition):
Ali Akbar Soltan, Reza. “Enhancements in Markovian Dynamics.” 2012. Doctoral Dissertation, Virginia Tech. Accessed March 01, 2021.
http://hdl.handle.net/10919/77345.
MLA Handbook (7th Edition):
Ali Akbar Soltan, Reza. “Enhancements in Markovian Dynamics.” 2012. Web. 01 Mar 2021.
Vancouver:
Ali Akbar Soltan R. Enhancements in Markovian Dynamics. [Internet] [Doctoral dissertation]. Virginia Tech; 2012. [cited 2021 Mar 01].
Available from: http://hdl.handle.net/10919/77345.
Council of Science Editors:
Ali Akbar Soltan R. Enhancements in Markovian Dynamics. [Doctoral Dissertation]. Virginia Tech; 2012. Available from: http://hdl.handle.net/10919/77345

University of Waterloo
14.
Shi, Yidan.
Statistical Methods for Event History Data under Response Dependent Sampling and Incomplete Observation.
Degree: 2020, University of Waterloo
URL: http://hdl.handle.net/10012/16062
► This thesis discusses statistical problems in event history data analysis including survival analysis and multistate models. Research questions in this thesis are motivated by the…
(more)
▼ This thesis discusses statistical problems in event history data analysis including survival analysis and multistate models. Research questions in this thesis are motivated by the Nun Study, which contains longevity data and longitudinal follow-up of cognition functions in 678 religious sisters. Our research interests lie in modeling the survival pattern and the disease process for dementia. These data are subject to a process-dependent sampling scheme, and the homogeneous Markov assumption is violated when using a multistate model to fit the panel data for cognition. In this thesis, we formulated three statistical questions according to the aforementioned issues and propose approaches to deal with these problems.
Survival analysis is often subject to left-truncation when the data are collected within certain study windows. Naive methods ignoring the sampling conditions yield invalid estimates. Much work has been done to deal with the bias caused by left-truncation. However, discussion on the loss-in-efficiency is limited. In Chapter 2, we proposed a method in which auxiliary information is borrowed to improve the efficiency in estimation. The auxiliary information includes summary-level statistics from a previous study on the same cohort and census data for a comparable population. The likelihood and score functions are developed. A Monte Carlo approximation is proposed to deal with the difficulty in obtaining tractable forms of the score and information functions. The method is illustrated by both simulation and real data application to the Nun Study.
Continuous-time Markov models are widely used for analyzing longitudinal data on the disease progression over time due to the great convenience for computing the probability transition matrices and the likelihood functions. However, in practice, the Markov assumption does not always hold. Most of the existing methods relax the Markov assumption while losing the advantage of that assumption in the calculation of transition probabilities. In Chapter 3, we consider the case where the violation of the Markov property is due to multiple underlying types of disease. We propose a mixture hidden Markov model where the underlying process is characterized by a mixture of multiple time-homogeneous Markov chains, one for each disease type, while the observation process contains states corresponding to the common symptomatic stages of these diseases. The method can be applied to modeling the disease process of Alzheimer's disease and other types of dementia. In the Nun Study, autopsies were conducted on some of the deceased participants so that one can know whether these individuals have Alzheimer's pathology in their brains. Our method can incorporate these partially observed pathology data as disease type indicators to improve the efficiency in estimation. The predictions for the overall prevalence and type-specific prevalence for dementia are calculated based on the proposed method. The performance of the proposed methods is also evaluated via simulation studies.
…
Subjects/Keywords: survival analysis; multistate model; left truncation; hidden Markov model; auxiliary information
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Shi, Y. (2020). Statistical Methods for Event History Data under Response Dependent Sampling and Incomplete Observation. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/16062
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Shi, Yidan. “Statistical Methods for Event History Data under Response Dependent Sampling and Incomplete Observation.” 2020. Thesis, University of Waterloo. Accessed March 01, 2021.
http://hdl.handle.net/10012/16062.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Shi, Yidan. “Statistical Methods for Event History Data under Response Dependent Sampling and Incomplete Observation.” 2020. Web. 01 Mar 2021.
Vancouver:
Shi Y. Statistical Methods for Event History Data under Response Dependent Sampling and Incomplete Observation. [Internet] [Thesis]. University of Waterloo; 2020. [cited 2021 Mar 01].
Available from: http://hdl.handle.net/10012/16062.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Shi Y. Statistical Methods for Event History Data under Response Dependent Sampling and Incomplete Observation. [Thesis]. University of Waterloo; 2020. Available from: http://hdl.handle.net/10012/16062
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of St. Andrews
15.
Worthington, Hannah.
The statistical development of integrated multi-state stopover models
.
Degree: 2016, University of St. Andrews
URL: http://hdl.handle.net/10023/18206
► This thesis focusses on the analysis of ecological capture-recapture data and the estimation of population parameters of interest. Many of the common models applied to…
(more)
▼ This thesis focusses on the analysis of ecological capture-recapture data and the
estimation of population parameters of interest. Many of the common models applied
to such data, for example the Cormack-Jolly-Seber
model, condition on the first capture of an individual or on the number of individuals encountered. A consequence
of this conditioning is that it is not possible to estimate the total abundance
directly. Stopover models remove the conditioning on first capture and instead
explicitly
model the arrival of individuals into the population. This permits the
estimation of abundance through the likelihood along with other parameters such
as capture and retention probabilities.
We develop an integrated stopover
model capable of analysing multiple years of
data within a single likelihood and allowing parameters to be shared across years.
We consider special cases of this
model, writing the likelihood using sufficient statistics
as well as utilising the
hidden Markov model framework to allow for efficient
evaluation of the likelihood. We further extend this
model to an integrated multistate-stopover
model which incorporates any available discrete state information.
The new stopover models are applied to real ecological data sets. A cohort-dependent
single-year stopover
model is applied to data on grey seals, Halichoerus
grypus, where the cohorts are determined by birth year. The integrated stopover
model and integrated multi-state stopover
model are used to analyse a data set on
great crested newts, Triturus cristatus. A subset of this data is used to explore closed population models that permit capture probabilities to depend on discrete
state information. The final section of this thesis considers a capture-recapture-recovery
data set relating to Soay sheep, a breed of domestic sheep Ovis aries.
These data contain individual time-varying continuous covariates and raise the issue
of dealing with missing data.
Advisors/Committee Members: King, Ruth (advisor).
Subjects/Keywords: Integrated stopover model;
Capture-recapture;
Hidden Markov model;
Multi-state
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Worthington, H. (2016). The statistical development of integrated multi-state stopover models
. (Thesis). University of St. Andrews. Retrieved from http://hdl.handle.net/10023/18206
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Worthington, Hannah. “The statistical development of integrated multi-state stopover models
.” 2016. Thesis, University of St. Andrews. Accessed March 01, 2021.
http://hdl.handle.net/10023/18206.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Worthington, Hannah. “The statistical development of integrated multi-state stopover models
.” 2016. Web. 01 Mar 2021.
Vancouver:
Worthington H. The statistical development of integrated multi-state stopover models
. [Internet] [Thesis]. University of St. Andrews; 2016. [cited 2021 Mar 01].
Available from: http://hdl.handle.net/10023/18206.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Worthington H. The statistical development of integrated multi-state stopover models
. [Thesis]. University of St. Andrews; 2016. Available from: http://hdl.handle.net/10023/18206
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Melbourne
16.
Li, Yan.
A probabilistic approach for Wi-Fi based indoor localization.
Degree: 2019, University of Melbourne
URL: http://hdl.handle.net/11343/234278
► The Global Navigation Satellite System (GNSS) has been widely used to provide location information in outdoor environments, but it fails to provide reliable positioning indoors.…
(more)
▼ The Global Navigation Satellite System (GNSS) has been widely used to provide location information in outdoor environments, but it fails to provide reliable positioning indoors. WiFi based localization systems have attracted considerable attention because of the extensive deployment of Wireless Local Area Network (WLAN) infrastructure and the ubiquity of WiFi enabled mobile devices, offering a potentially low-cost way to track a mobile user in an indoor environment.
The mainstream WiFi fingerprint based systems deployed in a practical large-scale wireless environment still encounter critical challenges in terms of intensive survey cost and large variations in a dynamic environment. Crowdsourcing, by its nature, uses heterogeneous devices in the process of surveying the site. While this reduces the time needed during the surveying phase, account needs to be taken of the variation in sensor performance. This variation results in a diversity in received signal strength (RSS) values and varying sensitivities to different access points (APs).
In a complex and noisy indoor environment, for example a university building, a large number of APs can be sensed during both the survey and positioning phase, leading to a high-dimensional classification problem. In addition, because of multipath (fading channel) variation, signals from APs may not be sensed in every scan, thus resulting in a missing data problem. This PhD dissertation aims to mitigate these challenges and develop a practical room-level localization system at low deployment cost in a public wireless environment focusing on system architecture and methods.
First, room-level localization is defined in terms of cell-based localization. By segmenting the floor plan into cells, training data collection is carried out by fusing RSS measurements taken within each cell by all contributed devices. A multivariate linear regression model is applied to calibrate the RSS measurements collected from different devices involved in the crowdsourced training phase. The conventional method of dealing with missing data is to set a low RSS value which will distort the RSS distribution and cause biased estimation. The Expectation-Maximization (EM) imputation method is used instead to estimate missing RSS values in the incomplete RSS measurements. Different features of the RSS spatial correlation for both fixed single location and across-cell measurements are studied. It is demonstrated that the RSS independence assumption is not valid in this context.
We follow by using a high-dimensional probabilistic fingerprint for each cell, based on a multivariate Gaussian mixture model (MVGMM) to take account of spatial correlation of the signal strengths from multiple APs. The benefits of using information provided by invisible APs in differentiating between cells has been investigated, by incorporating a geometric distribution to provide a probability of existence of an AP that has not been seen in training.
Finally, we design two frameworks based on hidden Markov model (HMM) and route…
Subjects/Keywords: WiFi localization; Hidden Markov Model; Probabilistic fingerprinting; Gaussian mixture model
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Li, Y. (2019). A probabilistic approach for Wi-Fi based indoor localization. (Doctoral Dissertation). University of Melbourne. Retrieved from http://hdl.handle.net/11343/234278
Chicago Manual of Style (16th Edition):
Li, Yan. “A probabilistic approach for Wi-Fi based indoor localization.” 2019. Doctoral Dissertation, University of Melbourne. Accessed March 01, 2021.
http://hdl.handle.net/11343/234278.
MLA Handbook (7th Edition):
Li, Yan. “A probabilistic approach for Wi-Fi based indoor localization.” 2019. Web. 01 Mar 2021.
Vancouver:
Li Y. A probabilistic approach for Wi-Fi based indoor localization. [Internet] [Doctoral dissertation]. University of Melbourne; 2019. [cited 2021 Mar 01].
Available from: http://hdl.handle.net/11343/234278.
Council of Science Editors:
Li Y. A probabilistic approach for Wi-Fi based indoor localization. [Doctoral Dissertation]. University of Melbourne; 2019. Available from: http://hdl.handle.net/11343/234278

University of Georgia
17.
Wu, Yong.
Modeling and searching for ncRNA secondary structure.
Degree: 2014, University of Georgia
URL: http://hdl.handle.net/10724/25717
► The discovery of functional non-coding RNAs (ncRNAs) has led to an increasing interest in efficient algorithms related to ncRNA secondary structure prediction and search for…
(more)
▼ The discovery of functional non-coding RNAs (ncRNAs) has led to an increasing interest in efficient algorithms related to ncRNA secondary structure prediction and search for new ncRNA in genomes. The hidden Markov model and covariance model
have been introduced to perform such tasks, but their limitations of modeling and computational complexity have compromised their practical application. Therefore, a tree-decomposition-based graph approach has been proposed to efficiently conduct the
structure-sequence alignment, which underlies our computational tool, RNATOPS. As an essential part, the modeling and searching for accurate component candidates in a structure become one of major issues in the search process. In this thesis, a
simplified model and many heuristic techniques have been proposed and exploited to address the issue. Comparisons between RNATOPS and Infernal have been conducted on several types of ncRNAs, which show the better performance of RNATOPS.
Subjects/Keywords: ncRNA; sencodary structure; hidden Markov model; covariance model
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APA ·
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MLA ·
Vancouver ·
CSE |
Export
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APA (6th Edition):
Wu, Y. (2014). Modeling and searching for ncRNA secondary structure. (Thesis). University of Georgia. Retrieved from http://hdl.handle.net/10724/25717
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Wu, Yong. “Modeling and searching for ncRNA secondary structure.” 2014. Thesis, University of Georgia. Accessed March 01, 2021.
http://hdl.handle.net/10724/25717.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Wu, Yong. “Modeling and searching for ncRNA secondary structure.” 2014. Web. 01 Mar 2021.
Vancouver:
Wu Y. Modeling and searching for ncRNA secondary structure. [Internet] [Thesis]. University of Georgia; 2014. [cited 2021 Mar 01].
Available from: http://hdl.handle.net/10724/25717.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Wu Y. Modeling and searching for ncRNA secondary structure. [Thesis]. University of Georgia; 2014. Available from: http://hdl.handle.net/10724/25717
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Illinois – Chicago
18.
Huang, Ziqian.
Segmentation of Financial and Marketing Data: Mixture Logit model and Hidden Markov Model.
Degree: 2012, University of Illinois – Chicago
URL: http://hdl.handle.net/10027/9611
► Segmentation refers to the assignment of each consumer to a set of similar consumers. The formation of the sets and the assignments are done simultaneously…
(more)
▼ Segmentation refers to the assignment of each consumer to a set of similar consumers. The formation of the sets and the assignments are done simultaneously in an algorithm. We focus on utilizing the Mixture Logit
Model (MLM) and the
Hidden Markov Model (HMM) to segment financial and marketing data. Both the MLM and the HMM originate from the Finite Mixture
Model (FMM). The MLM is also called the Mixture Logistic Regression (MLR). Traditionally, cluster analysis, including the K means algorithm and hierarchical methods, have been used as segmentation methods. Cluster analysis is unsupervised learning, in that there is no target variable. In the marketing and finance areas, segmentation results are often considered as one of the important inputs for modeling a target variable, because of the existence of different underlying market segments, both in theory and in reality. Our proposed segmentation methods begin with modeling a target variable, instead of unsupervised learning, and then the existence of segments is evaluated through certain
model selection criteria. The characteristics of each segment are shown from their parameter estimates, and further the segments can be profiled by other variables which are not used in the
model. This research makes a contribution by illustrating how to segment financial and marketing data objectively and systematically, with regard to incorporating the segmentation into the supervised modeling. We apply the MLM on one marketing solicitation responder dataset, the HMM on the S&P 500 monthly return data, and on one charity donation dataset. All of the results demonstrate that the MLM and the HMM perform better than the benchmark models or methods.
Advisors/Committee Members: Sclove, Stanley L. (advisor), Malter, Alan J. (committee member), Wang, Fangfang (committee member), Karabatsos, George (committee member), Stokes, Houston H. (committee member).
Subjects/Keywords: Segmentation; Finite Mixture Model; Mixture Logit Model; Hidden Markov Model; Quantitative Marketing Model
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Huang, Z. (2012). Segmentation of Financial and Marketing Data: Mixture Logit model and Hidden Markov Model. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/9611
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Huang, Ziqian. “Segmentation of Financial and Marketing Data: Mixture Logit model and Hidden Markov Model.” 2012. Thesis, University of Illinois – Chicago. Accessed March 01, 2021.
http://hdl.handle.net/10027/9611.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Huang, Ziqian. “Segmentation of Financial and Marketing Data: Mixture Logit model and Hidden Markov Model.” 2012. Web. 01 Mar 2021.
Vancouver:
Huang Z. Segmentation of Financial and Marketing Data: Mixture Logit model and Hidden Markov Model. [Internet] [Thesis]. University of Illinois – Chicago; 2012. [cited 2021 Mar 01].
Available from: http://hdl.handle.net/10027/9611.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Huang Z. Segmentation of Financial and Marketing Data: Mixture Logit model and Hidden Markov Model. [Thesis]. University of Illinois – Chicago; 2012. Available from: http://hdl.handle.net/10027/9611
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Illinois – Urbana-Champaign
19.
Khosrowpour, Ardalan.
Vision-based workface assessment using depth images for activity analysis of interior construction operations.
Degree: MS, 0106, 2014, University of Illinois – Urbana-Champaign
URL: http://hdl.handle.net/2142/46892
► Workface assessment –the process of determining the overall activity rates of onsite construction workers throughout a day– typically involves manual visual observations which are time-consuming…
(more)
▼ Workface assessment –the process of determining the overall activity rates of onsite construction workers throughout a day– typically involves manual visual observations which are time-consuming and labor-intensive. To minimize subjectivity and the time required for conducting detailed assessments, and allowing managers to spend their time on the more important task of assessing and implementing improvements, we propose a new inexpensive vision-based method using RGB-D sensors that is applicable to interior construction operations. This is particularly a challenging task as construction activities have a large range of intra-class variability including varying sequences of body posture and time-spent on each individual activity. On the other hand, the state-of-the-art skeleton extraction algorithms from RGB-D sequences are not robust enough especially when workers interact with tools or self-occlude the camera’s field-of-view. Existing vision-based methods are also rather limited as they can primarily classify “atomic” activities from RGB-D sequences involving one worker conducting a single activity.
To address these limitations, our proposed original method involves three main components: 1) an algorithm for detecting, tracking, and extracting body skeleton features from depth images; 2) A discriminative bag-of-poses activity classifier trained using multiple Support Vector Machines for classifying single visual activities from a given body skeleton sequence; and 3) a
Hidden Markov model with a Kernel Density Estimation function to represent emission probabilities in form of a statistical distribution of single activity classifiers. For training and testing purposes, we also introduce a new dataset of eleven RGB-D sequences for interior drywall construction operations involving three actual construction workers conducting eight different activities in various interior locations. Our experimental results with an average accuracy of 76% on the testing dataset show the promise of vision-based methods using RGB-D sequences for facilitating the activity analysis workface assessment.
Advisors/Committee Members: Golparvar-Fard, Mani (advisor).
Subjects/Keywords: Activity Analysis; Workface Assessment; RGB-D (RedGreenBlue-Depth) cameras; Hidden Markov ModelActivity analysis; Hidden Markov Model
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Khosrowpour, A. (2014). Vision-based workface assessment using depth images for activity analysis of interior construction operations. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/46892
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Khosrowpour, Ardalan. “Vision-based workface assessment using depth images for activity analysis of interior construction operations.” 2014. Thesis, University of Illinois – Urbana-Champaign. Accessed March 01, 2021.
http://hdl.handle.net/2142/46892.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Khosrowpour, Ardalan. “Vision-based workface assessment using depth images for activity analysis of interior construction operations.” 2014. Web. 01 Mar 2021.
Vancouver:
Khosrowpour A. Vision-based workface assessment using depth images for activity analysis of interior construction operations. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2014. [cited 2021 Mar 01].
Available from: http://hdl.handle.net/2142/46892.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Khosrowpour A. Vision-based workface assessment using depth images for activity analysis of interior construction operations. [Thesis]. University of Illinois – Urbana-Champaign; 2014. Available from: http://hdl.handle.net/2142/46892
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

George Mason University
20.
Nguyen, Thao Tran Nhu.
Hidden Markov model based spectrum sensing for cognitive radio
.
Degree: 2013, George Mason University
URL: http://hdl.handle.net/1920/8288
► Cognitive radio is an emerging technology for sensing and opportunistic spectrum access in wireless communication networks. It allows a secondary user to detect under-utilized spectrum…
(more)
▼ Cognitive radio is an emerging technology for sensing and opportunistic spectrum access in wireless communication networks. It allows a secondary user to detect under-utilized spectrum of a primary user and to dynamically access the spectrum without causing harmful interference to the primary user. A number of spectrum sensing techniques has been proposed in the literature to identify the state of the primary user in the temporal domain. However, most of these techniques make instantaneous decisions based on current measurement received at the cognitive radio, and they do not consider the transmission pattern of the primary user which can be acquired from past measurements. Thus, sensing performance can be improved by incorporating measurement history into the sensing decision. Moreover, using all available data may enable prediction of the primary user activity, which will allow a cognitive radio to better plan for its spectrum usage.
Advisors/Committee Members: Mark, Brian L (advisor), Ephraim, Yariv (advisor).
Subjects/Keywords: Engineering;
Baum algorithm;
Bivariate Markov chain;
Cognitive radio;
Hidden Markov model;
Spectrum Sensing
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Nguyen, T. T. N. (2013). Hidden Markov model based spectrum sensing for cognitive radio
. (Thesis). George Mason University. Retrieved from http://hdl.handle.net/1920/8288
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Nguyen, Thao Tran Nhu. “Hidden Markov model based spectrum sensing for cognitive radio
.” 2013. Thesis, George Mason University. Accessed March 01, 2021.
http://hdl.handle.net/1920/8288.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Nguyen, Thao Tran Nhu. “Hidden Markov model based spectrum sensing for cognitive radio
.” 2013. Web. 01 Mar 2021.
Vancouver:
Nguyen TTN. Hidden Markov model based spectrum sensing for cognitive radio
. [Internet] [Thesis]. George Mason University; 2013. [cited 2021 Mar 01].
Available from: http://hdl.handle.net/1920/8288.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Nguyen TTN. Hidden Markov model based spectrum sensing for cognitive radio
. [Thesis]. George Mason University; 2013. Available from: http://hdl.handle.net/1920/8288
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
21.
Le Corff, Sylvain.
Estimations pour les modèles de Markov cachés et approximations particulaires : Application à la cartographie et à la localisation simultanées. : Inference in hidden Markov models and particle approximations - application to the simultaneous localization and mapping problem.
Degree: Docteur es, Signal et Images, 2012, Paris, ENST
URL: http://www.theses.fr/2012ENST0052
► Dans cette thèse, nous nous intéressons à l'estimation de paramètres dans les chaînes de Markov cachées. Nous considérons tout d'abord le problème de l'estimation en…
(more)
▼ Dans cette thèse, nous nous intéressons à l'estimation de paramètres dans les chaînes de Markov cachées. Nous considérons tout d'abord le problème de l'estimation en ligne (sans sauvegarde des observations) au sens du maximum de vraisemblance. Nous proposons une nouvelle méthode basée sur l'algorithme Expectation Maximization appelée Block Online Expectation Maximization (BOEM). Cet algorithme est défini pour des chaînes de Markov cachées à espace d'état et espace d'observations généraux. Dans le cas d'espaces d'états généraux, l'algorithme BOEM requiert l'introduction de méthodes de Monte Carlo séquentielles pour approcher des espérances sous des lois de lissage. La convergence de l'algorithme nécessite alors un contrôle de la norme Lp de l'erreur d'approximation Monte Carlo explicite en le nombre d'observations et de particules. Une seconde partie de cette thèse se consacre à l'obtention de tels contrôles pour plusieurs méthodes de Monte Carlo séquentielles. Nous étudions enfin des applications de l'algorithme BOEM à des problèmes de cartographie et de localisation simultanées. La dernière partie de cette thèse est relative à l'estimation non paramétrique dans les chaînes de Markov cachées. Le problème considéré est abordé dans un cadre précis. Nous supposons que (Xk) est une marche aléatoire dont la loi des incréments est connue à un facteur d'échelle a près. Nous supposons que, pour tout k, Yk est une observation de f(Xk) dans un bruit additif gaussien, où f est une fonction que nous cherchons à estimer. Nous établissons l'identifiabilité du modèle statistique et nous proposons une estimation de f et de a à partir de la vraisemblance par paires des observations.
This document is dedicated to inference problems in hidden Markov models. The first part is devoted to an online maximum likelihood estimation procedure which does not store the observations. We propose a new Expectation Maximization based method called the Block Online Expectation Maximization (BOEM) algorithm. This algorithm solves the online estimation problem for general hidden Markov models. In complex situations, it requires the introduction of Sequential Monte Carlo methods to approximate several expectations under the fixed interval smoothing distributions. The convergence of the algorithm is shown under the assumption that the Lp mean error due to the Monte Carlo approximation can be controlled explicitly in the number of observations and in the number of particles. Therefore, a second part of the document establishes such controls for several Sequential Monte Carlo algorithms. This BOEM algorithm is then used to solve the simultaneous localization and mapping problem in different frameworks. Finally, the last part of this thesis is dedicated to nonparametric estimation in hidden Markov models. It is assumed that the Markov chain (Xk) is a random walk lying in a compact set with increment distribution known up to a scaling factor a. At each time step k, Yk is a noisy observations of f(Xk) where f is an unknown function. We establish the…
Advisors/Committee Members: Moulines, Éric (thesis director), Fort, Gersende (thesis director).
Subjects/Keywords: Modèle de Markov caché; Estimation non-paramétrique; Hidden Markov model; Non-parametric estimation
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Le Corff, S. (2012). Estimations pour les modèles de Markov cachés et approximations particulaires : Application à la cartographie et à la localisation simultanées. : Inference in hidden Markov models and particle approximations - application to the simultaneous localization and mapping problem. (Doctoral Dissertation). Paris, ENST. Retrieved from http://www.theses.fr/2012ENST0052
Chicago Manual of Style (16th Edition):
Le Corff, Sylvain. “Estimations pour les modèles de Markov cachés et approximations particulaires : Application à la cartographie et à la localisation simultanées. : Inference in hidden Markov models and particle approximations - application to the simultaneous localization and mapping problem.” 2012. Doctoral Dissertation, Paris, ENST. Accessed March 01, 2021.
http://www.theses.fr/2012ENST0052.
MLA Handbook (7th Edition):
Le Corff, Sylvain. “Estimations pour les modèles de Markov cachés et approximations particulaires : Application à la cartographie et à la localisation simultanées. : Inference in hidden Markov models and particle approximations - application to the simultaneous localization and mapping problem.” 2012. Web. 01 Mar 2021.
Vancouver:
Le Corff S. Estimations pour les modèles de Markov cachés et approximations particulaires : Application à la cartographie et à la localisation simultanées. : Inference in hidden Markov models and particle approximations - application to the simultaneous localization and mapping problem. [Internet] [Doctoral dissertation]. Paris, ENST; 2012. [cited 2021 Mar 01].
Available from: http://www.theses.fr/2012ENST0052.
Council of Science Editors:
Le Corff S. Estimations pour les modèles de Markov cachés et approximations particulaires : Application à la cartographie et à la localisation simultanées. : Inference in hidden Markov models and particle approximations - application to the simultaneous localization and mapping problem. [Doctoral Dissertation]. Paris, ENST; 2012. Available from: http://www.theses.fr/2012ENST0052
22.
Khemiri, Houssemeddine.
Approche générique appliquée à l'indexation audio par modélisation non supervisée : Unified data-driven approach for audio indexing, retrieval and recognition.
Degree: Docteur es, Signal et images, 2013, Paris, ENST
URL: http://www.theses.fr/2013ENST0055
► La quantité de données audio disponibles, telles que les enregistrements radio, la musique, les podcasts et les publicités est en augmentation constance. Par contre, il…
(more)
▼ La quantité de données audio disponibles, telles que les enregistrements radio, la musique, les podcasts et les publicités est en augmentation constance. Par contre, il n'y a pas beaucoup d'outils de classification et d'indexation, qui permettent aux utilisateurs de naviguer et retrouver des documents audio. Dans ces systèmes, les données audio sont traitées différemment en fonction des applications. La diversité de ces techniques d'indexation rend inadéquat le traitement simultané de flux audio où différents types de contenu audio coexistent. Dans cette thèse, nous présentons nos travaux sur l'extension de l'approche ALISP, développé initialement pour la parole, comme une méthode générique pour l'indexation et l'identification audio. La particularité des outils ALISP est qu'aucune transcription textuelle ou annotation manuelle est nécessaire lors de l'étape d'apprentissage. Le principe de cet outil est de transformer les données audio en une séquence de symboles. Ces symboles peuvent être utilisés à des fins d'indexation. La principale contribution de cette thèse est l'exploitation de l'approche ALISP comme une méthode générique pour l'indexation audio. Ce système est composé de trois modules: acquisition et modélisation des unités ALISP d'une manière non supervisée, transcription ALISP des données audio et comparaison des symboles ALISP avec la technique BLAST et la distance de Levenshtein. Les évaluations du système proposé pour les différentes applications sont effectuées avec la base de données YACAST et avec d'autres corpus disponibles publiquement pour différentes tâche de l'indexation audio.
The amount of available audio data, such as broadcast news archives, radio recordings, music and songs collections, podcasts or various internet media is constantly increasing. Therefore many audio indexing techniques are proposed in order to help users to browse audio documents. Nevertheless, these methods are developed for a specific audio content which makes them unsuitable to simultaneously treat audio streams where different types of audio document coexist. In this thesis we report our recent efforts in extending the ALISP approach developed for speech as a generic method for audio indexing, retrieval and recognition. The particularity of ALISP tools is that no textual transcriptions are needed during the learning step. Any input speech data is transformed into a sequence of arbitrary symbols. These symbols can be used for indexing purposes. The main contribution of this thesis is the exploitation of the ALISP approach as a generic method for audio indexing. The proposed system consists of three steps; an unsupervised training to model and acquire the ALISP HMM models, ALISP segmentation of audio data using the ALISP HMM models and a comparison of ALISP symbols using the BLAST algorithm and Levenshtein distance. The evaluations of the proposed systems are done on the YACAST and other publicly available corpora for several tasks of audio indexing.
Advisors/Committee Members: Chollet, Gérard (thesis director), Petrovska-Delacrétaz, Dijana (thesis director).
Subjects/Keywords: Signal audio; Modèle de Markov caché; ALISP segmentation; Audio signal; Hidden Markov model; Segmentation ALISP
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Khemiri, H. (2013). Approche générique appliquée à l'indexation audio par modélisation non supervisée : Unified data-driven approach for audio indexing, retrieval and recognition. (Doctoral Dissertation). Paris, ENST. Retrieved from http://www.theses.fr/2013ENST0055
Chicago Manual of Style (16th Edition):
Khemiri, Houssemeddine. “Approche générique appliquée à l'indexation audio par modélisation non supervisée : Unified data-driven approach for audio indexing, retrieval and recognition.” 2013. Doctoral Dissertation, Paris, ENST. Accessed March 01, 2021.
http://www.theses.fr/2013ENST0055.
MLA Handbook (7th Edition):
Khemiri, Houssemeddine. “Approche générique appliquée à l'indexation audio par modélisation non supervisée : Unified data-driven approach for audio indexing, retrieval and recognition.” 2013. Web. 01 Mar 2021.
Vancouver:
Khemiri H. Approche générique appliquée à l'indexation audio par modélisation non supervisée : Unified data-driven approach for audio indexing, retrieval and recognition. [Internet] [Doctoral dissertation]. Paris, ENST; 2013. [cited 2021 Mar 01].
Available from: http://www.theses.fr/2013ENST0055.
Council of Science Editors:
Khemiri H. Approche générique appliquée à l'indexation audio par modélisation non supervisée : Unified data-driven approach for audio indexing, retrieval and recognition. [Doctoral Dissertation]. Paris, ENST; 2013. Available from: http://www.theses.fr/2013ENST0055

University of Illinois – Urbana-Champaign
23.
Baharian Khoshkhou, Golshid.
Stochastic sequential assignment problem.
Degree: PhD, 0127, 2014, University of Illinois – Urbana-Champaign
URL: http://hdl.handle.net/2142/50503
► The stochastic sequential assignment problem (SSAP) studies the allocation of available distinct workers with deterministic values to sequentially-arriving tasks with stochastic parameters so as to…
(more)
▼ The stochastic sequential assignment problem (SSAP) studies the allocation of available distinct workers with deterministic values to sequentially-arriving tasks with stochastic parameters so as to maximize the expected total reward obtained from the assignments. The difficulty and challenge in making the assignment decisions is that the assignments are performed in real-time; specifically, pairing a worker with a task is done without knowledge of future task values. This thesis focuses on studying practical variations and extensions of the SSAP, with the goal of eliminating restricting assumptions so that the problem setting converges to that of real-world problems.
The existing SSAP literature considers a risk-neutral objective function, seeking an assignment policy to maximize the expected total reward; however, a risk-neutral objective function is not always desirable for the decision-maker since the probability distribution function (pdf) of the total reward might carry a high probability of low values. To take this issue into account, the first part of this dissertation studies the SSAP under a risk-sensitive objective function. Specifically, the assignments are performed so as to minimize the threshold probability, which is the probability of the total reward failing to achieve a specified target (threshold). A target-dependent
Markov decision process (MDP) is solved, and sufficient conditions for the existence of a deterministic
Markov optimal policy are provided. An approximate algorithm is presented, and convergence of the approximate value function to the optimal value function is established under mild conditions.
The second part of this thesis analyzes the limiting behavior of the SSAP as the number of assignments approaches infinity. The optimal assignment policy for the basic SSAP has a threshold structure and involves computing a new set of breakpoints upon the arrival of each task, which is cumbersome for large-scale problems. To address this issue, the second part of this dissertation focuses on obtaining stationary (time-independent) optimal assignment policies that maximize the long-run expected reward per task and are much easier to perform in real-world problems. An exponential convergence rate is established for the convergence of the expected total reward per task to the optimal value as the number of tasks approaches infinity. The limiting behavior of the SSAP is studied in two different settings. The first setting assumes an independent and identically distributed (IID) sequence of arriving tasks with observable distribution functions, while the second problem considers the case where task distributions are unobservable and belong to a pool of feasible distributions.
The next part of this dissertation basically brings the first two parts together, studying the limiting behavior of the target-dependent SSAP, where the goal is finding an assignment policy that minimizes the probability of the long-run reward per task failing to achieve a given target value. It is proven that the…
Advisors/Committee Members: Jacobson, Sheldon H. (advisor), Chen, Xin (Committee Chair), Jacobson, Sheldon H. (committee member), Kiyavash, Negar (committee member), Shanbhag, Vinayak V. (committee member).
Subjects/Keywords: sequential assignment; Markov decision process; stationary policy; hidden Markov model; threshold criteria; risk measure
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Baharian Khoshkhou, G. (2014). Stochastic sequential assignment problem. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/50503
Chicago Manual of Style (16th Edition):
Baharian Khoshkhou, Golshid. “Stochastic sequential assignment problem.” 2014. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed March 01, 2021.
http://hdl.handle.net/2142/50503.
MLA Handbook (7th Edition):
Baharian Khoshkhou, Golshid. “Stochastic sequential assignment problem.” 2014. Web. 01 Mar 2021.
Vancouver:
Baharian Khoshkhou G. Stochastic sequential assignment problem. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2014. [cited 2021 Mar 01].
Available from: http://hdl.handle.net/2142/50503.
Council of Science Editors:
Baharian Khoshkhou G. Stochastic sequential assignment problem. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2014. Available from: http://hdl.handle.net/2142/50503

University of Waterloo
24.
Raffa, Jesse Daniel.
Multivariate Longitudinal Data Analysis with Mixed Effects Hidden Markov Models.
Degree: 2013, University of Waterloo
URL: http://hdl.handle.net/10012/7255
► Longitudinal studies, where data on study subjects are collected over time, is increasingly involving multivariate longitudinal responses. Frequently, the heterogeneity observed in a multivariate longitudinal…
(more)
▼ Longitudinal studies, where data on study subjects are collected over time, is increasingly involving multivariate longitudinal responses. Frequently, the heterogeneity observed in a multivariate longitudinal response can be attributed to underlying unobserved disease states in addition to any between-subject differences. We propose modeling such disease states using a hidden Markov model (HMM) approach and expand upon previous work, which incorporated random effects into HMMs for the analysis of univariate longitudinal data, to the setting of a multivariate longitudinal response. Multivariate longitudinal data are modeled jointly using separate but correlated random effects between longitudinal responses of mixed data types in addition to a shared underlying hidden process. We use a computationally efficient Bayesian approach via Markov chain Monte Carlo (MCMC) to fit such models. We apply this methodology to bivariate longitudinal response data from a smoking cessation clinical trial. Under these models, we examine how to incorporate a treatment effect on the disease states, as well as develop methods to classify observations by disease state and to attempt to understand patient dropout. Simulation studies were performed to evaluate the properties of such models and their applications under a variety of realistic situations.
Subjects/Keywords: multivariate longitudinal data; hidden markov model; random effects; Markov chain monte carlo
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Raffa, J. D. (2013). Multivariate Longitudinal Data Analysis with Mixed Effects Hidden Markov Models. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/7255
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Raffa, Jesse Daniel. “Multivariate Longitudinal Data Analysis with Mixed Effects Hidden Markov Models.” 2013. Thesis, University of Waterloo. Accessed March 01, 2021.
http://hdl.handle.net/10012/7255.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Raffa, Jesse Daniel. “Multivariate Longitudinal Data Analysis with Mixed Effects Hidden Markov Models.” 2013. Web. 01 Mar 2021.
Vancouver:
Raffa JD. Multivariate Longitudinal Data Analysis with Mixed Effects Hidden Markov Models. [Internet] [Thesis]. University of Waterloo; 2013. [cited 2021 Mar 01].
Available from: http://hdl.handle.net/10012/7255.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Raffa JD. Multivariate Longitudinal Data Analysis with Mixed Effects Hidden Markov Models. [Thesis]. University of Waterloo; 2013. Available from: http://hdl.handle.net/10012/7255
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

San Jose State University
25.
Chou, Peter.
Dueling-HMM Analysis on Masquerade Detection.
Degree: MS, Computer Science, 2016, San Jose State University
URL: https://doi.org/10.31979/etd.497c-u3tp
;
https://scholarworks.sjsu.edu/etd_projects/464
► Masquerade detection is the ability to detect attackers known as masqueraders that intrude on another user’s system and pose as legitimate users. Once a…
(more)
▼ Masquerade detection is the ability to detect attackers known as masqueraders that intrude on another user’s system and pose as legitimate users. Once a masquerader obtains access to a user’s system, the masquerader has free reign over whatever data is on that system. In this research, we focus on masquerade detection and user classi cation using the following two di erent approaches: the heavy hitter approach and 2 di erent approaches based on
hidden Markov models (HMMs), the dueling-HMM and threshold-HMM strategies.
The heavy hitter approach computes the frequent elements seen in the training data sequence and test data sequence and computes the distance to see whether the test data sequence is masqueraded or not. The results show very misleading classi cations, suggesting that the approach is not viable for masquerade detection.
A
hidden Markov model is a tool for representing probability distributions over sequences of observations [9]. Previous research has shown that using a threshold-based
hidden Markov model (HMM) approach is successful in a variety of categories: malware detection, intrusion detection, pattern recognition, etc. We have veri ed that using a threshold-based HMM approach produces high accuracy with low amounts of a false positives. Using the dueling- HMM approach, which utilizes multiple training HMMs, we obtain an overall accuracy of 81.96%. With the introduction of the bias in the dueling-HMM approach, we produce similar results to the results obtained in the threshold-based HMM approach, where we see many non-masqueraded data detected, while many masqueraded data avoid detection, yet still result in an high overall accuracy.
Advisors/Committee Members: Tom Austin, Mark Stamp, Robert Chun.
Subjects/Keywords: masquarade intrusion detection Hidden Markov Model; Information Security
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Chou, P. (2016). Dueling-HMM Analysis on Masquerade Detection. (Masters Thesis). San Jose State University. Retrieved from https://doi.org/10.31979/etd.497c-u3tp ; https://scholarworks.sjsu.edu/etd_projects/464
Chicago Manual of Style (16th Edition):
Chou, Peter. “Dueling-HMM Analysis on Masquerade Detection.” 2016. Masters Thesis, San Jose State University. Accessed March 01, 2021.
https://doi.org/10.31979/etd.497c-u3tp ; https://scholarworks.sjsu.edu/etd_projects/464.
MLA Handbook (7th Edition):
Chou, Peter. “Dueling-HMM Analysis on Masquerade Detection.” 2016. Web. 01 Mar 2021.
Vancouver:
Chou P. Dueling-HMM Analysis on Masquerade Detection. [Internet] [Masters thesis]. San Jose State University; 2016. [cited 2021 Mar 01].
Available from: https://doi.org/10.31979/etd.497c-u3tp ; https://scholarworks.sjsu.edu/etd_projects/464.
Council of Science Editors:
Chou P. Dueling-HMM Analysis on Masquerade Detection. [Masters Thesis]. San Jose State University; 2016. Available from: https://doi.org/10.31979/etd.497c-u3tp ; https://scholarworks.sjsu.edu/etd_projects/464

San Jose State University
26.
Kicinski, Maciej.
AB INITIO PROTEIN STRUCTURE PREDICTION ALGORITHMS.
Degree: MS, Computer Science, 2011, San Jose State University
URL: https://doi.org/10.31979/etd.dycd-k9fd
;
https://scholarworks.sjsu.edu/etd_projects/165
► Genes that encode novel proteins are constantly being discovered and added to databases, but the speed with which their structures are being determined is…
(more)
▼ Genes that encode novel proteins are constantly being discovered and added to databases, but the speed with which their structures are being determined is not keeping up with this rate of discovery. Currently, homology and threading methods perform the best for protein structure prediction, but they are not appropriate to use for all proteins. Still, the best way to determine a protein's structure is through biological experimentation. This research looks into possible methods and relations that pertain to ab initio protein structure prediction. The study includes the use of positional and transitional probabilities of amino acids obtained from a non-redundant set of proteins created by Jpred for training computational methods. The methods this study focuses on are
Hidden Markov Models and incorporating neighboring amino acids in the primary structure of proteins with the above-mentioned probabilities. The methods are presented to predict the secondary structure of amino acids without relying on the existence of a homolog. The main goal of this research is to be able to obtain information from an amino acid sequence that could be used for all future predictions of protein structures. Further, analysis of the performance of the methods is presented for explanation of how they could be incorporated in current and future work.
Advisors/Committee Members: Sami Khuri, Mark Stamp, Sarah Green.
Subjects/Keywords: protein structure prediction hidden markov model; Bioinformatics; Other Computer Sciences
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Kicinski, M. (2011). AB INITIO PROTEIN STRUCTURE PREDICTION ALGORITHMS. (Masters Thesis). San Jose State University. Retrieved from https://doi.org/10.31979/etd.dycd-k9fd ; https://scholarworks.sjsu.edu/etd_projects/165
Chicago Manual of Style (16th Edition):
Kicinski, Maciej. “AB INITIO PROTEIN STRUCTURE PREDICTION ALGORITHMS.” 2011. Masters Thesis, San Jose State University. Accessed March 01, 2021.
https://doi.org/10.31979/etd.dycd-k9fd ; https://scholarworks.sjsu.edu/etd_projects/165.
MLA Handbook (7th Edition):
Kicinski, Maciej. “AB INITIO PROTEIN STRUCTURE PREDICTION ALGORITHMS.” 2011. Web. 01 Mar 2021.
Vancouver:
Kicinski M. AB INITIO PROTEIN STRUCTURE PREDICTION ALGORITHMS. [Internet] [Masters thesis]. San Jose State University; 2011. [cited 2021 Mar 01].
Available from: https://doi.org/10.31979/etd.dycd-k9fd ; https://scholarworks.sjsu.edu/etd_projects/165.
Council of Science Editors:
Kicinski M. AB INITIO PROTEIN STRUCTURE PREDICTION ALGORITHMS. [Masters Thesis]. San Jose State University; 2011. Available from: https://doi.org/10.31979/etd.dycd-k9fd ; https://scholarworks.sjsu.edu/etd_projects/165

University of Alberta
27.
Hladky, Stephen Michael.
Predicting opponent locations in first-person shooter video
games.
Degree: MS, Department of Computing Science, 2009, University of Alberta
URL: https://era.library.ualberta.ca/files/5x21tg037
► Commercial video game developers constantly strive to create intelligent humanoid characters that are controlled by computers. To ensure computer opponents are challenging to human players,…
(more)
▼ Commercial video game developers constantly strive to
create intelligent humanoid characters that are controlled by
computers. To ensure computer opponents are challenging to human
players, these characters are often allowed to cheat. Although they
appear skillful at playing video games, cheating characters may not
behave in a human-like manner and can contribute to a lack of
player enjoyment if caught. This work investigates the problem of
predicting opponent positions in the video game Counter-Strike:
Source without cheating. Prediction models are machine-learned from
records of past matches and are informed only by game information
available to a human player. Results show that the best models
estimate opponent positions with similar or better accuracy than
human experts. Moreover, the mistakes these models make are closer
to human predictions than actual opponent locations perturbed by a
corresponding amount of Gaussian noise.
Subjects/Keywords: opponent modelling; particle filter; hidden semi-Markov model; believability; video games
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Hladky, S. M. (2009). Predicting opponent locations in first-person shooter video
games. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/5x21tg037
Chicago Manual of Style (16th Edition):
Hladky, Stephen Michael. “Predicting opponent locations in first-person shooter video
games.” 2009. Masters Thesis, University of Alberta. Accessed March 01, 2021.
https://era.library.ualberta.ca/files/5x21tg037.
MLA Handbook (7th Edition):
Hladky, Stephen Michael. “Predicting opponent locations in first-person shooter video
games.” 2009. Web. 01 Mar 2021.
Vancouver:
Hladky SM. Predicting opponent locations in first-person shooter video
games. [Internet] [Masters thesis]. University of Alberta; 2009. [cited 2021 Mar 01].
Available from: https://era.library.ualberta.ca/files/5x21tg037.
Council of Science Editors:
Hladky SM. Predicting opponent locations in first-person shooter video
games. [Masters Thesis]. University of Alberta; 2009. Available from: https://era.library.ualberta.ca/files/5x21tg037

University of Illinois – Urbana-Champaign
28.
Zhang, Susu.
A multilevel logistic hidden Markov model for learning under cognitive diagnosis.
Degree: MS, Psychology, 2018, University of Illinois – Urbana-Champaign
URL: http://hdl.handle.net/2142/101112
► Students who wish to learn a specific skill have increasing access to a growing number of online courses and open-source educational repositories of instructional tools,…
(more)
▼ Students who wish to learn a specific skill have increasing access to a growing number of online courses and open-source educational repositories of instructional tools, including videos, slides, and exercises. Navigating these tools is time consuming and the search itself can hinder the learning of the skill. Educators are hence interested in aiding students by selecting the optimal content sequence for individual learners, specifically which skill one should learn next and which material one should use to study. Such adaptive selection would rely on preknowledge of how the learners’ and the instructional tools’ characteristics jointly affect the probability of acquiring a certain skill. Building upon previous research on Latent Transition Analysis and Learning Trajectories, we propose a multilevel logistic
hidden Markov model for learning based on cognitive diagnosis models, where the probability that a learner acquires the target skill depends not only on the general difficulty of the skill and the learner's mastery of other skills in the curriculum, but also on the effectiveness of the particular learning tool and the its interaction with mastery of other skills, captured by random slopes and intercepts for each learning tool. A Bayesian modeling framework and an MCMC algorithm for parameter estimation are proposed and evaluated using a simulation study.
Advisors/Committee Members: Chang, Hua-Hua (advisor), Anderson, Carolyn J (committee member).
Subjects/Keywords: Hidden Markov Model; Cognitive Diagnostic Modeling; Learning; Multilevel Modeling
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Zhang, S. (2018). A multilevel logistic hidden Markov model for learning under cognitive diagnosis. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/101112
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Zhang, Susu. “A multilevel logistic hidden Markov model for learning under cognitive diagnosis.” 2018. Thesis, University of Illinois – Urbana-Champaign. Accessed March 01, 2021.
http://hdl.handle.net/2142/101112.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Zhang, Susu. “A multilevel logistic hidden Markov model for learning under cognitive diagnosis.” 2018. Web. 01 Mar 2021.
Vancouver:
Zhang S. A multilevel logistic hidden Markov model for learning under cognitive diagnosis. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2018. [cited 2021 Mar 01].
Available from: http://hdl.handle.net/2142/101112.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Zhang S. A multilevel logistic hidden Markov model for learning under cognitive diagnosis. [Thesis]. University of Illinois – Urbana-Champaign; 2018. Available from: http://hdl.handle.net/2142/101112
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Penn State University
29.
Bardakci, Ibrahim Ekrem.
Approximate Realization of Hidden Markov Models.
Degree: 2014, Penn State University
URL: https://submit-etda.libraries.psu.edu/catalog/23697
► The realization problem for hidden Markov models (HMMs) has been studied over years. However, it does not always have solution and requires the exact string…
(more)
▼ The realization problem for
hidden Markov models (HMMs) has been studied over years. However, it does not always have solution and requires the exact string probabilities of all string of �finite length. In real world applications, it is not feasible
and only fi�nite number of approximate string probabilities of strings up to certain length are usually available. Since the string probabilities are approximate, the realization algorithms will produce an outcome of HMM that has high order. Accordingly, it may be more useful to make a good low order approximate realization of string probabilities rather than try to match them exactly.
In this thesis, we aim to give a heuristic as a solution to approximate realization problem for HMM by presenting a low-order approximation of the HMM given a sequence of observations. We propose an algorithm to fi�nd an HMM that approximately generates the given string probabilities which are computed from the observation sequence. The algorithm fi�rst minimizes the rank of Hankel matrix constructed from an HMM using nuclear norm generalization, subsequently given the rank information �finds an HMM using non-negative matrix factorization that
approximately realizes the given string probabilities.
Advisors/Committee Members: Constantino Manuel Lagoa, Thesis Advisor/Co-Advisor.
Subjects/Keywords: Hidden Markov Model; Approximate Realization; Hankel Matrix; Nuclear Norm
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Bardakci, I. E. (2014). Approximate Realization of Hidden Markov Models. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/23697
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Bardakci, Ibrahim Ekrem. “Approximate Realization of Hidden Markov Models.” 2014. Thesis, Penn State University. Accessed March 01, 2021.
https://submit-etda.libraries.psu.edu/catalog/23697.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Bardakci, Ibrahim Ekrem. “Approximate Realization of Hidden Markov Models.” 2014. Web. 01 Mar 2021.
Vancouver:
Bardakci IE. Approximate Realization of Hidden Markov Models. [Internet] [Thesis]. Penn State University; 2014. [cited 2021 Mar 01].
Available from: https://submit-etda.libraries.psu.edu/catalog/23697.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Bardakci IE. Approximate Realization of Hidden Markov Models. [Thesis]. Penn State University; 2014. Available from: https://submit-etda.libraries.psu.edu/catalog/23697
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Penn State University
30.
Bray, Brandon A.
Dyadic Interaction Patterns During Infancy and Early Childhood.
Degree: 2017, Penn State University
URL: https://submit-etda.libraries.psu.edu/catalog/14805bab456
► Dyadic interaction patterns, the dynamic social interplay between caregiver and infant characterized by each partner’s response to the behavior of the other, are considered one…
(more)
▼ Dyadic interaction patterns, the dynamic social interplay between caregiver and infant characterized by each partner’s response to the behavior of the other, are considered one of the foundational factors of infants’ emergent self-regulation (Beebe et al., 1992; Kopp, 1982; Schore, 1996). Theoretically, then, dyadic interaction patterns should change over time as infants develop regulatory autonomy and capabilities for mobility, social engagement, and independence (Kopp, 1982), and vary depending on caregivers’ interactive styles (Feldman, 2007). Although research has examined links between early dyadic interaction patterns, measured as dyadic synchrony between parents’ and infants’ behaviors, and later child outcomes, relatively little is known about the specific types of parents’ and infants’ behaviors that typically co-occur at different ages.
To address this gap, the current study provided detailed descriptive data about dyadic interaction patterns across infancy and early toddlerhood for mother-child dyads. Following advances in time-series data analytic methods for modeling dyadic data (e.g., Stifter & Rovine, 2015), the current study used
Hidden Markov Modeling (HMM; Visser & Speekenbrink, 2010) to identify patterns of moment-to-moment behaviors co-occurring between mothers and their children (latent dyadic states) and to compute probabilities of transitions among those states at three ages: 9-, 18-, and 27-months.
The current study used microcoded observations of adoptive mothers’ and infants’ behaviors collected as part of the Early Growth and Development Study (Leve et al., 2013) during an observational Teaching Task at child ages 9-, 18-, and 27-months (N = 551). The Teaching Task elicits maternal support for infant autonomy and exploration, therefore relevant behaviors that were coded included, for example, maternal scaffolding, praise, and social bids, and child attention to task, compliance, and toy exploration. Separately at each age, HMM was used to compute the probabilities of all possible latent dyadic states (i.e., all possible co-occurring mother-child behaviors) and to determine the number of dyadic states that resulted in best
model fit. In other words, HMM quantified specific patterns of dyadic interaction that were most likely to occur at 9-, 18-, and 27-months. Then, separately at each age, HMM was used to compute a set of transition probabilities (i.e., the probability of dyads’ moving from one latent state to any other latent state). Thus, HMM provided a rich description of the content of dyadic interactions (i.e., the most likely types of co-occurring behaviors) and the process of dyadic interactions (most likely patterns of movement among dyadic states) at each age. The dyadic interaction patterns at each age were discussed in terms of similarities and differences at the different ages. HMM can be used in future research to examine individual differences in dyadic interaction patterns, explore genetic and environmental contributions to the development of dyadic interaction patterns, and…
Advisors/Committee Members: Ginger A Moore, Thesis Advisor/Co-Advisor, Pamela Marie Cole, Committee Member, Jenae Marie Neiderhiser, Committee Member.
Subjects/Keywords: dyadic interaction; parenting; dyadic regulation; Hidden Markov Model
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Bray, B. A. (2017). Dyadic Interaction Patterns During Infancy and Early Childhood. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/14805bab456
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Bray, Brandon A. “Dyadic Interaction Patterns During Infancy and Early Childhood.” 2017. Thesis, Penn State University. Accessed March 01, 2021.
https://submit-etda.libraries.psu.edu/catalog/14805bab456.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Bray, Brandon A. “Dyadic Interaction Patterns During Infancy and Early Childhood.” 2017. Web. 01 Mar 2021.
Vancouver:
Bray BA. Dyadic Interaction Patterns During Infancy and Early Childhood. [Internet] [Thesis]. Penn State University; 2017. [cited 2021 Mar 01].
Available from: https://submit-etda.libraries.psu.edu/catalog/14805bab456.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Bray BA. Dyadic Interaction Patterns During Infancy and Early Childhood. [Thesis]. Penn State University; 2017. Available from: https://submit-etda.libraries.psu.edu/catalog/14805bab456
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
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