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Virginia Tech
1.
Lin, Jiali.
Bayesian Multilevel-multiclass Graphical Model.
Degree: PhD, Statistics, 2019, Virginia Tech
URL: http://hdl.handle.net/10919/101092 
► Gaussian graphical model has been a popular tool to investigate conditional dependency between random variables by estimating sparse precision matrices. Two problems have been discussed.…
(more)
▼ Gaussian
graphical model has been a popular tool to investigate conditional dependency between random variables by estimating sparse precision matrices. Two problems have been discussed. One is to learn multiple Gaussian
graphical models at multilevel from unknown classes. Another one is to select Gaussian process in semiparametric multi-kernel machine regression.
The first problem is approached by Gaussian
graphical model. In this project, I consider learning multiple connected graphs among multilevel variables from unknown classes. I esti- mate the classes of the observations from the mixture distributions by evaluating the Bayes factor and learn the network structures by fitting a novel neighborhood selection algorithm. This approach is able to identify the class membership and to reveal network structures for multilevel variables simultaneously. Unlike most existing methods that solve this problem by frequentist approaches, I assess an alternative to a novel hierarchical Bayesian approach to incorporate prior knowledge.
The second problem focuses on the analysis of correlated high-dimensional data which has been useful in many applications. In this work, I consider a problem of detecting signals with a semiparametric regression
model which can study the effects of fixed covariates (e.g. clinical variables) and sets of elements (e.g. pathways of genes). I
model the unknown high-dimension functions of multi-sets via multi-Gaussian kernel machines to consider the possibility that elements within the same set interact with each other. Hence, my variable selection can be considered as Gaussian process selection. I develop my Gaussian process selection under the Bayesian variable selection framework.
Advisors/Committee Members: Kim, Inyoung (committeechair), Deng, Xinwei (committee member), Guo, Feng (committee member), Terrell, George R. (committee member).
Subjects/Keywords: Gaussian graphical model
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APA (6th Edition):
Lin, J. (2019). Bayesian Multilevel-multiclass Graphical Model. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/101092
Chicago Manual of Style (16th Edition):
Lin, Jiali. “Bayesian Multilevel-multiclass Graphical Model.” 2019. Doctoral Dissertation, Virginia Tech. Accessed January 16, 2021.
http://hdl.handle.net/10919/101092.
MLA Handbook (7th Edition):
Lin, Jiali. “Bayesian Multilevel-multiclass Graphical Model.” 2019. Web. 16 Jan 2021.
Vancouver:
Lin J. Bayesian Multilevel-multiclass Graphical Model. [Internet] [Doctoral dissertation]. Virginia Tech; 2019. [cited 2021 Jan 16].
Available from: http://hdl.handle.net/10919/101092.
Council of Science Editors:
Lin J. Bayesian Multilevel-multiclass Graphical Model. [Doctoral Dissertation]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/101092
University of Alberta
2.
Sen, Abhishek.
Hand Tracking by Fusion of Color and a Range Sensor.
Degree: MS, Department of Computing Science, 2012, University of Alberta
URL: https://era.library.ualberta.ca/files/pv63g128r
► In this work we have developed a decentralized algorithm for efficient localization and tracking of hands from a sequence of depth and colour images. We…
(more)
▼ In this work we have developed a decentralized
algorithm for efficient localization and tracking of hands from a
sequence of depth and colour images. We deduce the location of
key-points using a Bayesian framework. We use anthropomorphic
constraints for modelling the interaction between
body-parts.Furthermore, we incorporate an occlusion reasoning and
data association preservation procedure for dealing with
ambiguities. Our work is adaptive to illumination changes despite
utilizing the skin-color information for tracking. Experimental
results demonstrate that our system produces more accurate tracking
of the head and hands in video, compared to prior
research.
Subjects/Keywords: hand tracking; graphical model
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APA (6th Edition):
Sen, A. (2012). Hand Tracking by Fusion of Color and a Range Sensor. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/pv63g128r
Chicago Manual of Style (16th Edition):
Sen, Abhishek. “Hand Tracking by Fusion of Color and a Range Sensor.” 2012. Masters Thesis, University of Alberta. Accessed January 16, 2021.
https://era.library.ualberta.ca/files/pv63g128r.
MLA Handbook (7th Edition):
Sen, Abhishek. “Hand Tracking by Fusion of Color and a Range Sensor.” 2012. Web. 16 Jan 2021.
Vancouver:
Sen A. Hand Tracking by Fusion of Color and a Range Sensor. [Internet] [Masters thesis]. University of Alberta; 2012. [cited 2021 Jan 16].
Available from: https://era.library.ualberta.ca/files/pv63g128r.
Council of Science Editors:
Sen A. Hand Tracking by Fusion of Color and a Range Sensor. [Masters Thesis]. University of Alberta; 2012. Available from: https://era.library.ualberta.ca/files/pv63g128r
University of Rochester
3.
LaCombe, Jason R.
Non-Informative Priors for Structural Inference in
Bayesian Networks.
Degree: PhD, 2011, University of Rochester
URL: http://hdl.handle.net/1802/15981
► A fundamental problem in multivariate statistics is the determination of dependency relationships among random variables. Bayesian networks equate the dependency properties of the considered variables…
(more)
▼ A fundamental problem in multivariate statistics is
the determination of dependency relationships
among random
variables. Bayesian networks equate the dependency properties of
the
considered variables with edges in a graph, allowing us a
mathematically tractable framework
for inferring complex
relationships. Thus the original dependency problem reduces to one
of
model selection, with graphs as the models in consideration. A
reasonable solution is to choose
the graph that is most likely to
have generated the observed data. However as the number of
variables begins to exceed the sample size, the traditional
statistical method of Maximum Likelihood
estimation tends to
choose graphs that are overly complex{i.e. graphs with an
excessive
number of edges. We illustrate this over-tting problem
by approximating the expected number
of excess edges for a
sequence of graphs of increasing size.
From a Bayesian
perspective, one possible solution to the above problem of
over-tting is
to introduce a prior on the model-space to penalize
complexity. The application of a Bayesian
framework provides us
not only with a means of selecting a reasonable graph, but also of
expressing
our condence in our selection through the use of
posterior density sampling procedures. Our
emphasis is on
constructing a prior{the Isomorphism (Iso) prior{which we prove to
be unbiased
in estimating many important characteristics of a
model, such as the number of edges. Furthermore,
for a special
class of problems we are able to demonstrate several important
relationships
between the Iso and existing priors.
We perform
three simulation studies to exemplify the performance of the Iso
prior against
alternatives, with the results supporting our
previous conclusions. Finally we close with a
discussion{an
exposition on our current and future work{as well a presentation of
a general
theorem regarding the existence and construction of
non-informative priors for more general
model-selection
problems.
Subjects/Keywords: Bayesian-Networks; Model-Selection; Prior Graphical Model
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APA (6th Edition):
LaCombe, J. R. (2011). Non-Informative Priors for Structural Inference in
Bayesian Networks. (Doctoral Dissertation). University of Rochester. Retrieved from http://hdl.handle.net/1802/15981
Chicago Manual of Style (16th Edition):
LaCombe, Jason R. “Non-Informative Priors for Structural Inference in
Bayesian Networks.” 2011. Doctoral Dissertation, University of Rochester. Accessed January 16, 2021.
http://hdl.handle.net/1802/15981.
MLA Handbook (7th Edition):
LaCombe, Jason R. “Non-Informative Priors for Structural Inference in
Bayesian Networks.” 2011. Web. 16 Jan 2021.
Vancouver:
LaCombe JR. Non-Informative Priors for Structural Inference in
Bayesian Networks. [Internet] [Doctoral dissertation]. University of Rochester; 2011. [cited 2021 Jan 16].
Available from: http://hdl.handle.net/1802/15981.
Council of Science Editors:
LaCombe JR. Non-Informative Priors for Structural Inference in
Bayesian Networks. [Doctoral Dissertation]. University of Rochester; 2011. Available from: http://hdl.handle.net/1802/15981
Boston University
4.
Kang, Xinyu.
Statistical methods for topology inference, denoising, and bootstrapping in networks.
Degree: PhD, Mathematics & Statistics, 2018, Boston University
URL: http://hdl.handle.net/2144/33117
► Quite often, the data we observe can be effectively represented using graphs. The underlying structure of the resulting graph, however, might contain noise and does…
(more)
▼ Quite often, the data we observe can be effectively represented using graphs. The underlying structure of the resulting graph, however, might contain noise and does not always hold constant across scales. With the right tools, we could possibly address these two problems. This thesis focuses on developing the right tools and provides insights in looking at them. Specifically, I study several problems that incorporate network data within the multi-scale framework, aiming at identifying common patterns and differences, of signals over networks across different scales. Additional topics in network denoising and network bootstrapping will also be discussed.
The first problem we consider is the connectivity changes in dynamic networks constructed from multiple time series data. Multivariate time series data is often non-stationary. Furthermore, it is not uncommon to expect changes in a system across multiple time scales. Motivated by these observations, we in-corporate the traditional Granger-causal type of modeling within the multi-scale framework and propose a new method to detect the connectivity changes and recover the dynamic network structure.
The second problem we consider is how to denoise and approximate signals over a network adjacency matrix. We propose an adaptive unbalanced Haar wavelet based transformation of the network data, and show that it is efficient in approximation and denoising of the graph signals over a network adjacency matrix. We focus on the exact decompositions of the network, the corresponding approximation theory, and denoising signals over graphs, particularly from the perspective of compression of the networks. We also provide a real data application on denoising EEG signals over a DTI network.
The third problem we consider is in network denoising and network inference. Network representation is popular in characterizing complex systems. However, errors observed in the original measurements will propagate to network statistics and hence induce uncertainties to the summaries of the networks. We propose a spectral-denoising based resampling method to produce confidence intervals that propagate the inferential errors for a number of Lipschitz continuous net- work statistics. We illustrate the effectiveness of the method through a series of simulation studies.
Advisors/Committee Members: Kolaczyk, Eric D. (advisor).
Subjects/Keywords: Statistics; Network; Graphical model; Multiscale modeling
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APA ·
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Manager
APA (6th Edition):
Kang, X. (2018). Statistical methods for topology inference, denoising, and bootstrapping in networks. (Doctoral Dissertation). Boston University. Retrieved from http://hdl.handle.net/2144/33117
Chicago Manual of Style (16th Edition):
Kang, Xinyu. “Statistical methods for topology inference, denoising, and bootstrapping in networks.” 2018. Doctoral Dissertation, Boston University. Accessed January 16, 2021.
http://hdl.handle.net/2144/33117.
MLA Handbook (7th Edition):
Kang, Xinyu. “Statistical methods for topology inference, denoising, and bootstrapping in networks.” 2018. Web. 16 Jan 2021.
Vancouver:
Kang X. Statistical methods for topology inference, denoising, and bootstrapping in networks. [Internet] [Doctoral dissertation]. Boston University; 2018. [cited 2021 Jan 16].
Available from: http://hdl.handle.net/2144/33117.
Council of Science Editors:
Kang X. Statistical methods for topology inference, denoising, and bootstrapping in networks. [Doctoral Dissertation]. Boston University; 2018. Available from: http://hdl.handle.net/2144/33117
University of Missouri – Columbia
5.
Liang, Ye.
Bayesian methods on selected topics.
Degree: 2012, University of Missouri – Columbia
URL: http://hdl.handle.net/10355/15884
► Bayesian methods are widely adopted nowadays in statistical analysis. It is especially useful for the statistical inference of complex models or hierarchical models, for which…
(more)
▼ Bayesian methods are widely adopted nowadays in statistical analysis. It is especially useful for the statistical inference of complex models or hierarchical models, for which the frequentist methods are usually difficult to be applied. Though as a decision-making theory, often there are debates on the prior choices, the Bayesian methods benefits from its computational feasibility, with a variety of Markov chain Monte Carlo algorithms available. Three topics are studied using Bayesian methods. First, the competing risks
model for masked failure data is investigated, which suffers from an identification problem. The identification problem and possible solutions are discussed and a Bayesian framework is used for the complex
model. The other two topics are relevant, focusing on the lattice system and areal data. For a specific lattice system called generative star-shape
model, objective priors are developed in order to achieve better estimations. The last topic is modeling areal data from a special project. A hierarchical
model is developed for modeling the bounded outcomes with spatial variation and a Bayesian analysis is performed.
Advisors/Committee Members: Sun, Dongchu (advisor).
Subjects/Keywords: Bayesian statistics; spatial statistics; epidemiology; graphical model
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APA ·
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Manager
APA (6th Edition):
Liang, Y. (2012). Bayesian methods on selected topics. (Thesis). University of Missouri – Columbia. Retrieved from http://hdl.handle.net/10355/15884
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):
Liang, Ye. “Bayesian methods on selected topics.” 2012. Thesis, University of Missouri – Columbia. Accessed January 16, 2021.
http://hdl.handle.net/10355/15884.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Liang, Ye. “Bayesian methods on selected topics.” 2012. Web. 16 Jan 2021.
Vancouver:
Liang Y. Bayesian methods on selected topics. [Internet] [Thesis]. University of Missouri – Columbia; 2012. [cited 2021 Jan 16].
Available from: http://hdl.handle.net/10355/15884.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Liang Y. Bayesian methods on selected topics. [Thesis]. University of Missouri – Columbia; 2012. Available from: http://hdl.handle.net/10355/15884
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of New South Wales
6.
Liu, Xianghang.
New Algorithms for Graphical Models and Their Applications in Learning.
Degree: Computer Science & Engineering, 2015, University of New South Wales
URL: http://handle.unsw.edu.au/1959.4/55080
;
https://unsworks.unsw.edu.au/fapi/datastream/unsworks:36494/SOURCE02?view=true
► Probabilistic graphical models bring together graph theory and probability theory in a powerful formalism for multivariate statistical modelling. Since many machine learning problems involve the…
(more)
▼ Probabilistic
graphical models bring together graph theory and probability theory in a powerful formalism for multivariate statistical modelling. Since many machine learning problems involve the modelling of multivariate probability distributions,
graphical mod- els can be a good fit to these problems. In this thesis, we show that applying
graphical models in machine learning problems can have several advantages: First, it can better capture the nature of the problem. Second, it gives us great flexibility in modelling. Finally, it provides us with e
Advisors/Committee Members: Caetano, Tiberio, Computer Science & Engineering, Faculty of Engineering, UNSW, Blair, Alan, Computer Science & Engineering, Faculty of Engineering, UNSW.
Subjects/Keywords: statistical inference; machine learning; graphical model
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APA ·
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Manager
APA (6th Edition):
Liu, X. (2015). New Algorithms for Graphical Models and Their Applications in Learning. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/55080 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:36494/SOURCE02?view=true
Chicago Manual of Style (16th Edition):
Liu, Xianghang. “New Algorithms for Graphical Models and Their Applications in Learning.” 2015. Doctoral Dissertation, University of New South Wales. Accessed January 16, 2021.
http://handle.unsw.edu.au/1959.4/55080 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:36494/SOURCE02?view=true.
MLA Handbook (7th Edition):
Liu, Xianghang. “New Algorithms for Graphical Models and Their Applications in Learning.” 2015. Web. 16 Jan 2021.
Vancouver:
Liu X. New Algorithms for Graphical Models and Their Applications in Learning. [Internet] [Doctoral dissertation]. University of New South Wales; 2015. [cited 2021 Jan 16].
Available from: http://handle.unsw.edu.au/1959.4/55080 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:36494/SOURCE02?view=true.
Council of Science Editors:
Liu X. New Algorithms for Graphical Models and Their Applications in Learning. [Doctoral Dissertation]. University of New South Wales; 2015. Available from: http://handle.unsw.edu.au/1959.4/55080 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:36494/SOURCE02?view=true
Virginia Tech
7.
Zhang, Yafei.
Variable screening and graphical modeling for ultra-high dimensional longitudinal data.
Degree: PhD, Statistics, 2019, Virginia Tech
URL: http://hdl.handle.net/10919/101662
► Ultrahigh-dimensional variable selection is of great importance in the statistical research. And independence screening is a powerful tool to select important variable when there are…
(more)
▼ Ultrahigh-dimensional variable selection is of great importance in the statistical research. And independence screening is a powerful tool to select important variable when there are massive variables. Some commonly used independence screening procedures are based on single replicate data and are not applicable to longitudinal data. This motivates us to propose a new Sure Independence Screening (SIS) procedure to bring the dimension from ultra-high down to a relatively large scale which is similar to or smaller than the sample size. In chapter 2, we provide two types of SIS, and their iterative extensions (iterative SIS) to enhance the finite sample performance. An upper bound on the number of variables to be included is derived and assumptions are given under which sure screening is applicable. The proposed procedures are assessed by simulations and an application of them to a study on systemic lupus erythematosus illustrates the practical use of these procedures. After the variables screening process, we then explore the relationship among the variables.
Graphical models are commonly used to explore the association network for a set of variables, which could be genes or other objects under study. However,
graphical modes currently used are only designed for single replicate data, rather than longitudinal data. In chapter 3, we propose a penalized likelihood approach to identify the edges in a conditional independence graph for longitudinal data. We used pairwise coordinate descent combined with second order cone programming to optimize the penalized likelihood and estimate the parameters. Furthermore, we extended the nodewise regression method the for longitudinal data case. Simulation and real data analysis exhibit the competitive performance of the penalized likelihood method.
Advisors/Committee Members: Du, Pang (committeechair), Wu, Xiaowei (committee member), Kim, Inyoung (committee member), Hong, Yili (committee member).
Subjects/Keywords: graphical model; variable screening; longitudinal data analysis
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APA ·
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APA (6th Edition):
Zhang, Y. (2019). Variable screening and graphical modeling for ultra-high dimensional longitudinal data. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/101662
Chicago Manual of Style (16th Edition):
Zhang, Yafei. “Variable screening and graphical modeling for ultra-high dimensional longitudinal data.” 2019. Doctoral Dissertation, Virginia Tech. Accessed January 16, 2021.
http://hdl.handle.net/10919/101662.
MLA Handbook (7th Edition):
Zhang, Yafei. “Variable screening and graphical modeling for ultra-high dimensional longitudinal data.” 2019. Web. 16 Jan 2021.
Vancouver:
Zhang Y. Variable screening and graphical modeling for ultra-high dimensional longitudinal data. [Internet] [Doctoral dissertation]. Virginia Tech; 2019. [cited 2021 Jan 16].
Available from: http://hdl.handle.net/10919/101662.
Council of Science Editors:
Zhang Y. Variable screening and graphical modeling for ultra-high dimensional longitudinal data. [Doctoral Dissertation]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/101662
University of California – Irvine
8.
Ping, Wei.
Learning and Inference in Latent Variable Graphical Models.
Degree: Computer Science, 2016, University of California – Irvine
URL: http://www.escholarship.org/uc/item/7q90z4b5
► Probabilistic graphical models such as Markov random fields provide a powerful framework and tools for machine learning, especially for structured output learning. Latent variables naturally…
(more)
▼ Probabilistic graphical models such as Markov random fields provide a powerful framework and tools for machine learning, especially for structured output learning. Latent variables naturally exist in many applications of these models; they may arise from partially labeled data, or be introduced to enrich model flexibility. However, the presence of latent variables presents challenges for learning and inference.For example, the standard approach of using maximum a posteriori (MAP) prediction is complicated by the fact that, in latent variable models (LVMs), we typically want to first marginalize out the latent variables, leading to an inference task called marginal MAP. Unfortunately, marginal MAP prediction can be NP-hard even on relatively simple models such as trees, and few methods have been developed in the literature. This thesis presents a class of variational bounds for marginal MAP that generalizes the popular dual-decomposition method for MAP inference, and enables an efficient block coordinate descent algorithm to solve the corresponding optimization. Similarly, when learning LVMs for structured prediction, it is critically important to maintain the effect of uncertainty over latent variables by marginalization. We propose the marginal structured SVM, which uses marginal MAP inference to properly handle that uncertainty inside the framework of max-margin learning.We then turn our attention to an important subclass of latent variable models, restricted Boltzmann machines (RBMs). RBMs are two-layer latent variable models that are widely used to capture complex distributions of observed data, including as building block for deep probabilistic models. One practical problem in RBMs is model selection: we need to determine the hidden (latent) layer size before performing learning. We propose an infinite RBM model and apply the Frank-Wolfe algorithm to solve the resulting learning problem. The resulting algorithm can be interpreted as inserting a hidden variable into a RBM model at each iteration, to easily and efficiently perform model selection during learning. We also study the role of approximate inference in RBMs and conditional RBMs. In particular, there is a common assumption that belief propagation methods do not work well on RBM-based models, especially for learning. In contrast, we demonstrate that for conditional models and structured prediction, learning RBM-based models with belief propagation and its variants can provide much better results than the state-of-the-art contrastive divergence methods.
Subjects/Keywords: Computer science; Dual-decomposition; Graphical Model; Latent Variable Model; Structured SVM
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APA ·
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Manager
APA (6th Edition):
Ping, W. (2016). Learning and Inference in Latent Variable Graphical Models. (Thesis). University of California – Irvine. Retrieved from http://www.escholarship.org/uc/item/7q90z4b5
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):
Ping, Wei. “Learning and Inference in Latent Variable Graphical Models.” 2016. Thesis, University of California – Irvine. Accessed January 16, 2021.
http://www.escholarship.org/uc/item/7q90z4b5.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Ping, Wei. “Learning and Inference in Latent Variable Graphical Models.” 2016. Web. 16 Jan 2021.
Vancouver:
Ping W. Learning and Inference in Latent Variable Graphical Models. [Internet] [Thesis]. University of California – Irvine; 2016. [cited 2021 Jan 16].
Available from: http://www.escholarship.org/uc/item/7q90z4b5.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Ping W. Learning and Inference in Latent Variable Graphical Models. [Thesis]. University of California – Irvine; 2016. Available from: http://www.escholarship.org/uc/item/7q90z4b5
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Penn State University
9.
Lee, Sanghack.
Causal Discovery from Relational Data: Theory and Practice.
Degree: 2018, Penn State University
URL: https://submit-etda.libraries.psu.edu/catalog/14976sxl439
► Discovery of causal relationships from observational and experimental data is a central problem with applications across multiple areas of scientific endeavor. There has been considerable…
(more)
▼ Discovery of causal relationships from observational and experimental data is a central problem with applications across multiple areas of scientific endeavor. There has been considerable progress over the past decades on algorithms for eliciting causal relationships through a set of conditional independence queries from data. Much of this work assumes that the data instances are independent and identically distributed (iid). However, in many real-world applications, because the underlying data exhibits a relational structure of the sort that is modeled in practice by an entity-relationship
model, the iid assumption is violated. Motivated by the limitations of traditional approaches to learning causal relationships from relational data, a relational causal
model is recently introduced. The key idea behind the relational causal
model is that a cause and its effects are in a direct or indirect relationship that is reflected in the relational data. Traditional approaches for reasoning with and learning causal models from iid data cannot be trivially applied in the relational setting. Against this background, this dissertation investigates a set of closely related research problems having to do with causal inference with relational data: (i) characterizing the conditional independence relations that hold in a given relational causal
model, (ii) sound and complete learning of the structure of a relational causal
model using an independence oracle, (iii) measuring the strength of conditional dependence and testing conditional independence among relational variables from relational data, and (iv) robustly learning the structure of a relational causal
model from relational data.
Advisors/Committee Members: Vasant Gajanan Honavar, Dissertation Advisor/Co-Advisor, Vasant Gajanan Honavar, Committee Chair/Co-Chair, Clyde Lee Giles, Committee Member, John Yen, Committee Member, Bharath Kumar Sriperumbudur, Outside Member.
Subjects/Keywords: Causality; Causal Model; Graphical Models; Relational Data; Relational Model
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APA ·
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Manager
APA (6th Edition):
Lee, S. (2018). Causal Discovery from Relational Data: Theory and Practice. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/14976sxl439
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):
Lee, Sanghack. “Causal Discovery from Relational Data: Theory and Practice.” 2018. Thesis, Penn State University. Accessed January 16, 2021.
https://submit-etda.libraries.psu.edu/catalog/14976sxl439.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Lee, Sanghack. “Causal Discovery from Relational Data: Theory and Practice.” 2018. Web. 16 Jan 2021.
Vancouver:
Lee S. Causal Discovery from Relational Data: Theory and Practice. [Internet] [Thesis]. Penn State University; 2018. [cited 2021 Jan 16].
Available from: https://submit-etda.libraries.psu.edu/catalog/14976sxl439.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Lee S. Causal Discovery from Relational Data: Theory and Practice. [Thesis]. Penn State University; 2018. Available from: https://submit-etda.libraries.psu.edu/catalog/14976sxl439
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Waterloo
10.
Princz, Daniel.
The CRANE Framework for Simulation Model Workflows.
Degree: 2016, University of Waterloo
URL: http://hdl.handle.net/10012/10230
► CRANE is presented as a flexible framework for linking simulation models and model support tools to form integrated modelling systems for engineering and scientific applications,…
(more)
▼ CRANE is presented as a flexible framework for linking simulation models and model support tools to form integrated modelling systems for engineering and scientific applications, evaluated using the scientific workflow approach. CRANE was written using an object-oriented programming language; the separation of its core processing component from its user interface; support for plugins that can be updated and enhanced independent of the framework; and with intuitive user-friendly features and human-readable configuration files. Its strength is its ability to connect to legacy simulation models, whose code cannot be modified, through structured and/or free-format text files. The framework contains an engine that interprets the requirements of simulation models and modelling support tools, and facilitates the flow of data between these components in a simulation workflow. In addition, a user interface provides a familiar graphic interface through which the engine can be configured and monitored during the evaluation of the simulation workflow.
A case study was undertaken to demonstrate the ability of CRANE to wrap around, configure, and evaluate two versions of a hydrologic simulation model. Using the default parameter configuration, both versions of the model failed to capture the hydrologic regime of the basin; the modified version of the model only marginally improved the results by redistributing excess meltwater in a presumably more physically based way. The modified version of the model allowed excess meltwater to contribute to ponded storage and infiltrate into soil. By contrast, the original version of the model increased the evaporation rate to account for the excess meltwater. Given the poor overall performance of the model in this particular modelling scenario, the contribution of the modification could not be definitively commented upon. It was concluded that further assessment would be improved by better parameterization of the model. CRANE was used to configure the input files for the model, as well as to execute a simple simulation workflow. Unfortunately, the relative simplicity of the case study did not highlight the more advanced features of the framework. As this is a preliminary introduction of the framework, additional and different types of case studies are recommended, the results from which would identify areas where the framework can continue to be developed and enhanced.
Subjects/Keywords: model calibration; parameter estimation; graphical user interface; simulation model workflow; model-independent software
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APA (6th Edition):
Princz, D. (2016). The CRANE Framework for Simulation Model Workflows. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/10230
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):
Princz, Daniel. “The CRANE Framework for Simulation Model Workflows.” 2016. Thesis, University of Waterloo. Accessed January 16, 2021.
http://hdl.handle.net/10012/10230.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Princz, Daniel. “The CRANE Framework for Simulation Model Workflows.” 2016. Web. 16 Jan 2021.
Vancouver:
Princz D. The CRANE Framework for Simulation Model Workflows. [Internet] [Thesis]. University of Waterloo; 2016. [cited 2021 Jan 16].
Available from: http://hdl.handle.net/10012/10230.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Princz D. The CRANE Framework for Simulation Model Workflows. [Thesis]. University of Waterloo; 2016. Available from: http://hdl.handle.net/10012/10230
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Delft University of Technology
11.
Cueto Fernandez, Judith (author).
Joint angle coupling of a musculoskeletal model and a graphical model of the hand for enhanced display in medical education.
Degree: 2020, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:02cc8ccb-33ae-4b07-a402-ac5baf3ec365
► Advanced anatomical knowledge and understanding of the muscles involved in various movements are crucial for medical practitioners to reach the correct diagnostic and successfully predict…
(more)
▼ Advanced anatomical knowledge and understanding of the muscles involved in various movements are crucial for medical practitioners to reach the correct diagnostic and successfully predict surgery outcomes. To acquire this knowledge, 3D graphical anatomical models which are displayed stereoscopically can effectively supplement cadaveric dissections. Nevertheless, the movements implemented in the available graphical models do not accurately reproduce the intricate dynamics of the human body, which is especially relevant in the case of the hand. Biomechanical models, on the other hand, provide accurate movement simulations from experimental data, while lacking a detailed graphical representation. Thus, the current paper focuses on the incorporation of the biomechanical model of the hand developed by Mirakhorlo et al. (2018) into a comprehensive graphical anatomical model (Zygote Media Group Inc), to be used for educational purposes. Motion capture data of a pinch task was acquired to validate the combinational approach, and an inverse kinematics simulation was performed in OpenSim using the musculoskeletal model. A reference value based on the fingertip distance difference at the pinch pose was calculated from the experimental data and the simulated motion by the musculoskeletal model. This value was used for validation of the musculoskeletal model reproducibility by the graphical model. Comparison shows that the graphical model reproduced the simulated motion with satisfactory visual effects and within an acceptable range from the reference metric. The presented approach is considered a successful first step towards a biomechanically and anatomically accurate graphical model of the human hand. This lays the foundation for further work on minimising the effect of the anatomical differences between the two models in order to achieve a better match.
Biomechanical Engineering
Advisors/Committee Members: Mugge, W. (mentor), Geelen, J.E. (mentor), van der Helm, F.C.T. (graduation committee), Bogomolova, K. (mentor), Hierck, B.P. (mentor), Hovius, S. (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: Hand Model; Musculoskeletal Model; Graphical Model; Inverse Kinematics; Motion Capture; Joints; Finger
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APA (6th Edition):
Cueto Fernandez, J. (. (2020). Joint angle coupling of a musculoskeletal model and a graphical model of the hand for enhanced display in medical education. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:02cc8ccb-33ae-4b07-a402-ac5baf3ec365
Chicago Manual of Style (16th Edition):
Cueto Fernandez, Judith (author). “Joint angle coupling of a musculoskeletal model and a graphical model of the hand for enhanced display in medical education.” 2020. Masters Thesis, Delft University of Technology. Accessed January 16, 2021.
http://resolver.tudelft.nl/uuid:02cc8ccb-33ae-4b07-a402-ac5baf3ec365.
MLA Handbook (7th Edition):
Cueto Fernandez, Judith (author). “Joint angle coupling of a musculoskeletal model and a graphical model of the hand for enhanced display in medical education.” 2020. Web. 16 Jan 2021.
Vancouver:
Cueto Fernandez J(. Joint angle coupling of a musculoskeletal model and a graphical model of the hand for enhanced display in medical education. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Jan 16].
Available from: http://resolver.tudelft.nl/uuid:02cc8ccb-33ae-4b07-a402-ac5baf3ec365.
Council of Science Editors:
Cueto Fernandez J(. Joint angle coupling of a musculoskeletal model and a graphical model of the hand for enhanced display in medical education. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:02cc8ccb-33ae-4b07-a402-ac5baf3ec365
Carnegie Mellon University
12.
Wytock, Matt.
Optimizing Optimization: Scalable Convex Programming with Proximal Operators.
Degree: 2016, Carnegie Mellon University
URL: http://repository.cmu.edu/dissertations/785
► Convex optimization has developed a wide variety of useful tools critical to many applications in machine learning. However, unlike linear and quadratic programming, general convex…
(more)
▼ Convex optimization has developed a wide variety of useful tools critical to many applications in machine learning. However, unlike linear and quadratic programming, general convex solvers have not yet reached sufficient maturity to fully decouple the convex programming model from the numerical algorithms required for implementation. Especially as datasets grow in size, there is a significant gap in speed and scalability between general solvers and specialized algorithms. This thesis addresses this gap with a new model for convex programming based on an intermediate representation of convex problems as a sum of functions with efficient proximal operators. This representation serves two purposes: 1) many problems can be expressed in terms of functions with simple proximal operators, and 2) the proximal operator form serves as a general interface to any specialized algorithm that can incorporate additional `2-regularization. On a single CPU core, numerical results demonstrate that the prox-affine form results in significantly faster algorithms than existing general solvers based on conic forms. In addition, splitting problems into separable sums is attractive from the perspective of distributing solver work amongst multiple cores and machines. We apply large-scale convex programming to several problems arising from building the next-generation, information-enabled electrical grid. In these problems (as is common in many domains) large, high-dimensional datasets present opportunities for novel data-driven solutions. We present approaches based on convex models for several problems: probabilistic forecasting of electricity generation and demand, preventing failures in microgrids and source separation for whole-home energy disaggregation.
Subjects/Keywords: convex optimization; proximal operator; operator splitting; Newton method; sparsity; graphical model
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APA (6th Edition):
Wytock, M. (2016). Optimizing Optimization: Scalable Convex Programming with Proximal Operators. (Thesis). Carnegie Mellon University. Retrieved from http://repository.cmu.edu/dissertations/785
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):
Wytock, Matt. “Optimizing Optimization: Scalable Convex Programming with Proximal Operators.” 2016. Thesis, Carnegie Mellon University. Accessed January 16, 2021.
http://repository.cmu.edu/dissertations/785.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Wytock, Matt. “Optimizing Optimization: Scalable Convex Programming with Proximal Operators.” 2016. Web. 16 Jan 2021.
Vancouver:
Wytock M. Optimizing Optimization: Scalable Convex Programming with Proximal Operators. [Internet] [Thesis]. Carnegie Mellon University; 2016. [cited 2021 Jan 16].
Available from: http://repository.cmu.edu/dissertations/785.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Wytock M. Optimizing Optimization: Scalable Convex Programming with Proximal Operators. [Thesis]. Carnegie Mellon University; 2016. Available from: http://repository.cmu.edu/dissertations/785
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of California – Berkeley
13.
Kao, Wei-Chun.
Algorithms for Next-Generation High-Throughput Sequencing Technologies.
Degree: Electrical Engineering & Computer Sciences, 2011, University of California – Berkeley
URL: http://www.escholarship.org/uc/item/86b9c87d
► Recent advances of DNA sequencing technologies are allowingresearchers to generateimmense amounts of data in a fast and cost effective fashion, enablinggenome-wide association study and populationgenetic…
(more)
▼ Recent advances of DNA sequencing technologies are allowingresearchers to generateimmense amounts of data in a fast and cost effective fashion, enablinggenome-wide association study and populationgenetic research which could not be done a decade ago.There are quite numerous computational challenges arising from thisadvancement, however. Examples include efficient algorithms for processing rawdata generated by sequencing instruments, algorithms for detecting andcorrecting sequencing errors, algorithms for detecting genomevariations from sequence data, just to name a few. Because differentsequencing technologies can have drastically differentcharacteristics, these algorithms often need to be adapted in orderto produce most accurate results.In this thesis, I will address a few of the aforementioned problems. First, Iwill describe two model-based basecalling algorithms for the Illuminasequencing platforms: BayesCall and naiveBayesCall. The novelty of BayesCall algorithmis that it is fully unsupervised, requiring notraining data with known labels, and therefore it is applicable to data without a reference sequence.It also dramatically improves sequencing accuracies.Built upon BayesCall algorithm, naiveBayesCall dramatically improves computationalefficiency by approximating the original model without sacrificingaccuracy. We will also show that improved basecall can have positiveeffects on the downstream sequence analysis, such as the detection ofsingle nucleotide polymorphism and the assembly of novel genomes.In the third chapter, an algorithm, called ECHO, for correcting short-readsequencing errors will be described. The correction algorithm efficiently computesall overlaps between sequencing reads and corrects errorsby using statistical models. Since it does not rely on referencegenomes, ECHO can also be applied to de novo sequencing.Most other error correction algorithms require users to specifykey parameters, but the optimal values for these parameters are unknown tousers and can be hard to specify. Without key parameters beingoptimized, the effectiveness of error correction algorithm couldsometimes be dramatically reduced.Based on statistical models, ECHO optimizes these parametersaccordingly. We will show that ECHO can significantly reducesequence error rates and also facilitate downstream sequenceanalysis. It is also demonstrated that ECHO can be extended to detectheterozygousity from sequencing data.These algorithms are developed in hopes tomake downstream analysis of sequence data easier and ultimatelyfacilitate genome researches.
Subjects/Keywords: Computer science; Bioinformatics; Graphical Model; Illumina; Sequencing; Signal processing
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APA (6th Edition):
Kao, W. (2011). Algorithms for Next-Generation High-Throughput Sequencing Technologies. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/86b9c87d
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):
Kao, Wei-Chun. “Algorithms for Next-Generation High-Throughput Sequencing Technologies.” 2011. Thesis, University of California – Berkeley. Accessed January 16, 2021.
http://www.escholarship.org/uc/item/86b9c87d.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Kao, Wei-Chun. “Algorithms for Next-Generation High-Throughput Sequencing Technologies.” 2011. Web. 16 Jan 2021.
Vancouver:
Kao W. Algorithms for Next-Generation High-Throughput Sequencing Technologies. [Internet] [Thesis]. University of California – Berkeley; 2011. [cited 2021 Jan 16].
Available from: http://www.escholarship.org/uc/item/86b9c87d.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Kao W. Algorithms for Next-Generation High-Throughput Sequencing Technologies. [Thesis]. University of California – Berkeley; 2011. Available from: http://www.escholarship.org/uc/item/86b9c87d
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Mississippi State University
14.
Shi, Jinchuan.
A framework for integrating influence diagrams and POMDPs.
Degree: PhD, Computer Science and Engineering, 2018, Mississippi State University
URL: http://sun.library.msstate.edu/ETD-db/theses/available/etd-03022018-153923/
;
► An influence diagram is a widely-used graphical model for representing and solving problems of sequential decision making under imperfect information. A closely-related model for the…
(more)
▼ An influence diagram is a widely-used
graphical model for representing and solving problems of sequential decision making under imperfect information. A closely-related
model for the same class of problems is a partially observable Markov decision process (POMDP). This dissertation leverages the relationship between these two models to develop improved algorithms for solving influence diagrams.
The primary contribution is to generalize two classic dynamic programming algorithms for solving influence diagrams, Arc Reversal and Variable Elimination, by integrating them with a dynamic programming technique originally developed for solving POMDPs. This generalization relaxes constraints on the ordering of the steps of these algorithms in a way that dramatically improves scalability, especially in solving complex, multi-stage decision problems.
A secondary contribution is the adoption of a more compact and intuitive representation of the solution of an influence diagram, called a strategy. Instead of representing a
strategy as a table or as a tree, a strategy is represented as an acyclic graph, which can be exponentially more compact, making the strategy easier to interpret and understand.
Advisors/Committee Members: Ioana Banicescu (committee member), J. Edward Swan II (committee member), Maxwell Young (committee member), Eric Hansen (chair).
Subjects/Keywords: POMDP; Graphical Model; Probabilistic Inference; Theoretical Decision Planning; Influence Diagram
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APA ·
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APA (6th Edition):
Shi, J. (2018). A framework for integrating influence diagrams and POMDPs. (Doctoral Dissertation). Mississippi State University. Retrieved from http://sun.library.msstate.edu/ETD-db/theses/available/etd-03022018-153923/ ;
Chicago Manual of Style (16th Edition):
Shi, Jinchuan. “A framework for integrating influence diagrams and POMDPs.” 2018. Doctoral Dissertation, Mississippi State University. Accessed January 16, 2021.
http://sun.library.msstate.edu/ETD-db/theses/available/etd-03022018-153923/ ;.
MLA Handbook (7th Edition):
Shi, Jinchuan. “A framework for integrating influence diagrams and POMDPs.” 2018. Web. 16 Jan 2021.
Vancouver:
Shi J. A framework for integrating influence diagrams and POMDPs. [Internet] [Doctoral dissertation]. Mississippi State University; 2018. [cited 2021 Jan 16].
Available from: http://sun.library.msstate.edu/ETD-db/theses/available/etd-03022018-153923/ ;.
Council of Science Editors:
Shi J. A framework for integrating influence diagrams and POMDPs. [Doctoral Dissertation]. Mississippi State University; 2018. Available from: http://sun.library.msstate.edu/ETD-db/theses/available/etd-03022018-153923/ ;
University of Alberta
15.
Eastman, Thomas.
A disease classifier for metabolic profiles based on
metabolic pathway knowledge.
Degree: MS, Department of Computing Science, 2010, University of Alberta
URL: https://era.library.ualberta.ca/files/fx719n29m
► This thesis presents Pathway Informed Analysis (PIA), a classification method for predicting disease states (diagnosis) from metabolic profile measurements that incorporates biological knowledge in the…
(more)
▼ This thesis presents Pathway Informed Analysis (PIA),
a classification method for predicting disease states (diagnosis)
from metabolic profile measurements that incorporates biological
knowledge in the form of metabolic pathways. A metabolic pathway
describes a set of chemical reactions that perform a specific
biological function. A significant amount of biological knowledge
produced by efforts to identify and understand these pathways is
formalized in readily accessible databases such as the Kyoto
Encyclopedia of Genes and Genomes. PIA uses metabolic pathways to
identify relationships among the metabolite concentrations that are
measured by a metabolic profile. Specifically, PIA assumes that the
class-conditional metabolite concentrations (diseased vs. healthy,
respectively) follow multivariate normal distributions. It further
assumes that conditional independence statements about these
distributions derived from the pathways relate the concentrations
of the metabolites to each other. The two assumptions allow for a
natural representation of the class-conditional distributions using
a type of probabilistic graphical model called a Gaussian Markov
Random Field. PIA efficiently estimates the parameters defining
these distributions from example patients to produce a classifier.
It classifies an undiagnosed patient by evaluating both models to
determine the most probable class given their metabolic profile. We
apply PIA to a data set of cancer patients to diagnose those with a
muscle wasting disease called cachexia. Standard machine learning
algorithms such as Naive Bayes, Tree-augmented Naive Bayes, Support
Vector Machines and C4.5 are used to evaluate the performance of
PIA. The overall classification accuracy of PIA is better than
these algorithms on this data set but the difference is not
statistically significant. We also apply PIA to several other
classification tasks. Some involve predicting various manipulations
of the metabolic processes performed in experiments with worms.
Other tasks are to classify pigs according to properties of their
dietary intake. The accuracy of PIA at these tasks is not
significantly better than the standard algorithms.
Subjects/Keywords: metabolic profile; cachexia; graphical model; machine learning; metabolic pathway
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Manager
APA (6th Edition):
Eastman, T. (2010). A disease classifier for metabolic profiles based on
metabolic pathway knowledge. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/fx719n29m
Chicago Manual of Style (16th Edition):
Eastman, Thomas. “A disease classifier for metabolic profiles based on
metabolic pathway knowledge.” 2010. Masters Thesis, University of Alberta. Accessed January 16, 2021.
https://era.library.ualberta.ca/files/fx719n29m.
MLA Handbook (7th Edition):
Eastman, Thomas. “A disease classifier for metabolic profiles based on
metabolic pathway knowledge.” 2010. Web. 16 Jan 2021.
Vancouver:
Eastman T. A disease classifier for metabolic profiles based on
metabolic pathway knowledge. [Internet] [Masters thesis]. University of Alberta; 2010. [cited 2021 Jan 16].
Available from: https://era.library.ualberta.ca/files/fx719n29m.
Council of Science Editors:
Eastman T. A disease classifier for metabolic profiles based on
metabolic pathway knowledge. [Masters Thesis]. University of Alberta; 2010. Available from: https://era.library.ualberta.ca/files/fx719n29m
University of Alberta
16.
Zhu, Yunan.
Estimating Sparse Graphical Models: Insights Through
Simulation.
Degree: MS, Department of Mathematical and Statistical
Sciences, 2015, University of Alberta
URL: https://era.library.ualberta.ca/files/4j03d241c
► Graphical models are frequently used to explore networks among a set of variables. Several methods for estimating sparse graphs have been proposed and their theoretical…
(more)
▼ Graphical models are frequently used to explore
networks among a set of variables. Several methods for estimating
sparse graphs have been proposed and their theoretical properties
have been explored. There are also several selection criteria to
select the optimal estimated models. However, their practical
performance has not been studied in detail. In this work, several
estimation procedures (glasso, bootstrap glasso, adptive lasso,
SCAD, DP-glasso and Huge) and several selection criteria (AIC, BIC,
CV, ebic, ric and stars) are compared under various simulation
settings, such as different dimensions or sample sizes, different
types of data, and different sparsity levels of the true model
structures. Then we use several evaluation criteria to compare the
optimal estimated models and discuss in detail the superiority and
deficiency of each combination of estimating methods and selection
criteria.
Subjects/Keywords: bootstrap; penalized log-likelihood; graphical model; glasso; estimate evaluation
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APA ·
Chicago ·
MLA ·
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Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Zhu, Y. (2015). Estimating Sparse Graphical Models: Insights Through
Simulation. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/4j03d241c
Chicago Manual of Style (16th Edition):
Zhu, Yunan. “Estimating Sparse Graphical Models: Insights Through
Simulation.” 2015. Masters Thesis, University of Alberta. Accessed January 16, 2021.
https://era.library.ualberta.ca/files/4j03d241c.
MLA Handbook (7th Edition):
Zhu, Yunan. “Estimating Sparse Graphical Models: Insights Through
Simulation.” 2015. Web. 16 Jan 2021.
Vancouver:
Zhu Y. Estimating Sparse Graphical Models: Insights Through
Simulation. [Internet] [Masters thesis]. University of Alberta; 2015. [cited 2021 Jan 16].
Available from: https://era.library.ualberta.ca/files/4j03d241c.
Council of Science Editors:
Zhu Y. Estimating Sparse Graphical Models: Insights Through
Simulation. [Masters Thesis]. University of Alberta; 2015. Available from: https://era.library.ualberta.ca/files/4j03d241c
Penn State University
17.
Agarwal, Amal.
Model-Based Clustering of Nonparametric Weighted Networks.
Degree: 2019, Penn State University
URL: https://submit-etda.libraries.psu.edu/catalog/17363aua257
► Water pollution is a major global environmental problem, and it poses a great environmental risk to public health and biological diversity. This work is motivated…
(more)
▼ Water pollution is a major global environmental problem, and it poses a great environmental
risk to public health and biological diversity. This work is motivated by assessing the
potential environmental threat of coal mining through increased sulfate concentrations in
river networks, which do not belong to any simple parametric distribution. However, existing
network models mainly focus on binary or discrete networks and weighted networks
with known parametric weight distributions. We propose a principled nonparametric
weighted network
model based on exponential-family random graph models and local
likelihood estimation and study its
model-based clustering with application to largescale
water pollution network analysis. We do not require any parametric distribution
assumption on network weights. The proposed method greatly extends the methodology
and applicability of statistical network models. Furthermore, it is scalable to large and
complex networks in large-scale environmental studies and geoscientific research. The
power of our proposed methods is demonstrated in extensive simulation studies.
Advisors/Committee Members: Lingzhou Xue, Thesis Advisor/Co-Advisor, Zhibiao Zhao, Committee Member, Ephraim Mont Hanks, Program Head/Chair.
Subjects/Keywords: Exponential-family random graphical model; Local likelihood; Variational inference
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APA (6th Edition):
Agarwal, A. (2019). Model-Based Clustering of Nonparametric Weighted Networks. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/17363aua257
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):
Agarwal, Amal. “Model-Based Clustering of Nonparametric Weighted Networks.” 2019. Thesis, Penn State University. Accessed January 16, 2021.
https://submit-etda.libraries.psu.edu/catalog/17363aua257.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Agarwal, Amal. “Model-Based Clustering of Nonparametric Weighted Networks.” 2019. Web. 16 Jan 2021.
Vancouver:
Agarwal A. Model-Based Clustering of Nonparametric Weighted Networks. [Internet] [Thesis]. Penn State University; 2019. [cited 2021 Jan 16].
Available from: https://submit-etda.libraries.psu.edu/catalog/17363aua257.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Agarwal A. Model-Based Clustering of Nonparametric Weighted Networks. [Thesis]. Penn State University; 2019. Available from: https://submit-etda.libraries.psu.edu/catalog/17363aua257
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Toronto
18.
Yeo, Alexia.
COMBINATORIALLY CONSTRAINED PORTFOLIO OPTIMIZATION USING MESSAGE PASSING ALGORITHMS.
Degree: 2018, University of Toronto
URL: http://hdl.handle.net/1807/89580
► Portfolio optimization aims to find the optimal investment strategy for a series of assets that results in a minimization of the portfolio's variance. Real life…
(more)
▼ Portfolio optimization aims to find the optimal investment strategy for a series of assets that results in a minimization of the portfolio's variance. Real life portfolio considerations like limiting the number of assets or limiting assets to be traded in lots add combinatorial constraints to the original problem that are computationally expensive for commercial solvers. The aim of this thesis is to explore the use of message passing approaches to solve the portfolio problem with these hard constraints. Message passing represents a class of algorithms used to solve inference problems in graphical models. They are known to recover good sub-optimal solutions and we test this for the posed problem by comparing with branch-and-bound exact methods. Computational results for the portfolio problem with varying cardinality, target return and portfolio size confirm that message passing is a viable method for finding sub-optimal solutions and can return tight bounds around the exact solution.
M.A.S.
Advisors/Committee Members: Kwon, Roy H, Mechanical and Industrial Engineering.
Subjects/Keywords: Mixed Integer Programming; Portfolio Optimization; Probabilistic Graphical Model; 0796
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APA (6th Edition):
Yeo, A. (2018). COMBINATORIALLY CONSTRAINED PORTFOLIO OPTIMIZATION USING MESSAGE PASSING ALGORITHMS. (Masters Thesis). University of Toronto. Retrieved from http://hdl.handle.net/1807/89580
Chicago Manual of Style (16th Edition):
Yeo, Alexia. “COMBINATORIALLY CONSTRAINED PORTFOLIO OPTIMIZATION USING MESSAGE PASSING ALGORITHMS.” 2018. Masters Thesis, University of Toronto. Accessed January 16, 2021.
http://hdl.handle.net/1807/89580.
MLA Handbook (7th Edition):
Yeo, Alexia. “COMBINATORIALLY CONSTRAINED PORTFOLIO OPTIMIZATION USING MESSAGE PASSING ALGORITHMS.” 2018. Web. 16 Jan 2021.
Vancouver:
Yeo A. COMBINATORIALLY CONSTRAINED PORTFOLIO OPTIMIZATION USING MESSAGE PASSING ALGORITHMS. [Internet] [Masters thesis]. University of Toronto; 2018. [cited 2021 Jan 16].
Available from: http://hdl.handle.net/1807/89580.
Council of Science Editors:
Yeo A. COMBINATORIALLY CONSTRAINED PORTFOLIO OPTIMIZATION USING MESSAGE PASSING ALGORITHMS. [Masters Thesis]. University of Toronto; 2018. Available from: http://hdl.handle.net/1807/89580
University of Adelaide
19.
Wang, Zhenhua.
Markov random fields with unknown heterogeneous graphs.
Degree: 2014, University of Adelaide
URL: http://hdl.handle.net/2440/98246
► Markov Random Fields have been widely used in computer vision problems, for example image denoising, segmentation and human action recognition. The structure of the graph…
(more)
▼ Markov Random Fields have been widely used in computer vision problems, for example image denoising, segmentation and human action recognition. The structure of the graph can be determined using human heuristics or domain knowledge, or can be learned from data when assuming graphs are homogeneous in topology. However, there are many applications for heterogeneous graphs. This research concentrates on estimating heterogeneous graphs and labels simultaneously from the observation. The joint estimation of graphs and labels is formulated into maximising a joint likelihood inference. Unfortunately, these inference problems are generally NP-complete, and we thus develop novel algorithms for effectively and efficiently finding approximate solutions. We also demonstrate how to learn Markov random field parameters from data with our inference techniques. Our contributions are as follows. First, we show that estimating graphs and performing maximum a posteriori inference can be achieved simultaneously by solving a bilinear programming problem, for which we provide a branch and bound solver. Second, we relax the inference problem into a tighter non-convex quadratic programming problem, and propose a convex-concave procedure algorithm to solve the non-convex quadratic programming. Third, we derive the partial-dual of a mixed integer programming relaxation of the inference problem, which admits a scalable message passing-style algorithm. Lastly, we show how to learn the parameters of Markov random fields with the structured max-margin training and the proposed inference algorithms. We evaluate the proposed algorithms on both synthetic data and real applications including human action/activity recognition and semantic image segmentation. Our inference algorithms usually outperform the state-of-the-art. Within our proposed algorithms, the quadratic programming algorithm often performs better than the bilinear programming and the message passing algorithms, while the message passing algorithm is the most efficient.
Advisors/Committee Members: van den Hengel, Anton John (advisor), Shi, Qinfeng (advisor), Dick, Anthony Robert (advisor), School of Computer Science (school).
Subjects/Keywords: structured learning; Probablistic Graphical Model; Markov Random Field; unknown graph
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APA ·
Chicago ·
MLA ·
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APA (6th Edition):
Wang, Z. (2014). Markov random fields with unknown heterogeneous graphs. (Thesis). University of Adelaide. Retrieved from http://hdl.handle.net/2440/98246
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):
Wang, Zhenhua. “Markov random fields with unknown heterogeneous graphs.” 2014. Thesis, University of Adelaide. Accessed January 16, 2021.
http://hdl.handle.net/2440/98246.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Wang, Zhenhua. “Markov random fields with unknown heterogeneous graphs.” 2014. Web. 16 Jan 2021.
Vancouver:
Wang Z. Markov random fields with unknown heterogeneous graphs. [Internet] [Thesis]. University of Adelaide; 2014. [cited 2021 Jan 16].
Available from: http://hdl.handle.net/2440/98246.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Wang Z. Markov random fields with unknown heterogeneous graphs. [Thesis]. University of Adelaide; 2014. Available from: http://hdl.handle.net/2440/98246
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
20.
Pacini, Clare.
Inferring condition specific regulatory networks with small sample sizes: a case study in bacillus subtilis and infection of mus musculus by the parasite Toxoplasma gondii.
Degree: PhD, 2017, University of Cambridge
URL: https://www.repository.cam.ac.uk/handle/1810/269711
► Modelling interactions between genes and their regulators is fundamental to understanding how, for example a disease progresses, or the impact of inserting a synthetic circuit…
(more)
▼ Modelling interactions between genes and their regulators is fundamental to understanding how, for example a disease progresses, or the impact of inserting a synthetic circuit into a cell. We use an existing method to infer regulatory networks under multiple conditions: the Joint Graphical Lasso (JGL), a shrinkage based Gaussian graphical model. We apply this method to two data sets: one, a publicly available set of microarray experiments perturbing the gram-positive bacteria Bacillus subtilis under multiple experimental conditions; the second, a set of RNA-seq samples of Mouse (Mus musculus) embryonic fibroblasts (MEFs) infected with different strains of the parasite Toxoplasma gondii. In both cases we infer a subset of the regulatory networks using relatively small sample sizes.
For the Bacillus subtilis analysis we focused on the use of these regulatory networks in synthetic biology and found examples of transcriptional units active only under a subset of conditions, this information can be useful when designing circuits to have condition dependent behaviour. We developed methods for large network decomposition that made use of the condition information and showed a greater specificity of identifying single transcriptional units from the larger network using our method. Through annotating these results with known information we were able to identify novel connections and found supporting evidence for a selection of these from publicly available experimental results.
Biological data collection is typically expensive and due to the relatively small sample sizes of our MEF data set we developed a novel empirical Bayes method for reducing the false discovery rate when estimating block diagonal covariance matrices. Using these methods we were able to infer regulatory networks for the host infected with either the ME49 or RH strain of the parasite. This enabled the identification of known and novel regulatory mechanisms. The Toxoplasma gondii parasite has shown to subvert host function using similar mechanisms as cancers and through our analysis we were able to identify genes, networks and ontologies associated with cancer, including connections that have not previously been associated with T. gondii infection.
Finally a Shiny application was developed as an online resource giving access to the Bacillus subtilis inferred networks with interactive methods for exploring the networks including expansion of sub networks and large network decomposition.
Subjects/Keywords: Gaussian graphical model; Regulatory network; Small sample sizes
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APA ·
Chicago ·
MLA ·
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CSE |
Export
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APA (6th Edition):
Pacini, C. (2017). Inferring condition specific regulatory networks with small sample sizes: a case study in bacillus subtilis and infection of mus musculus by the parasite Toxoplasma gondii. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/269711
Chicago Manual of Style (16th Edition):
Pacini, Clare. “Inferring condition specific regulatory networks with small sample sizes: a case study in bacillus subtilis and infection of mus musculus by the parasite Toxoplasma gondii.” 2017. Doctoral Dissertation, University of Cambridge. Accessed January 16, 2021.
https://www.repository.cam.ac.uk/handle/1810/269711.
MLA Handbook (7th Edition):
Pacini, Clare. “Inferring condition specific regulatory networks with small sample sizes: a case study in bacillus subtilis and infection of mus musculus by the parasite Toxoplasma gondii.” 2017. Web. 16 Jan 2021.
Vancouver:
Pacini C. Inferring condition specific regulatory networks with small sample sizes: a case study in bacillus subtilis and infection of mus musculus by the parasite Toxoplasma gondii. [Internet] [Doctoral dissertation]. University of Cambridge; 2017. [cited 2021 Jan 16].
Available from: https://www.repository.cam.ac.uk/handle/1810/269711.
Council of Science Editors:
Pacini C. Inferring condition specific regulatory networks with small sample sizes: a case study in bacillus subtilis and infection of mus musculus by the parasite Toxoplasma gondii. [Doctoral Dissertation]. University of Cambridge; 2017. Available from: https://www.repository.cam.ac.uk/handle/1810/269711
University of Washington
21.
Li, Zehang.
Bayesian Methods for Graphical Models with Limited Data.
Degree: PhD, 2018, University of Washington
URL: http://hdl.handle.net/1773/43158
► Scientific studies in many fields involve understanding and characterizing dependence relationships among large numbers of variables. This can be challenging in settings where data is…
(more)
▼ Scientific studies in many fields involve understanding and characterizing dependence relationships among large numbers of variables. This can be challenging in settings where data is limited and noisy. Take survey data as an example, understanding the associations between questions may help researchers better explain themes amongst related questions and impute missing values. Yet, such data typically contains a combination of binary, continuous, and categorical variables, high proportions of missing values, and complex data structures. In this dissertation, we develop flexible models and algorithms to estimate Gaussian and latent Gaussian
graphical models from noisy data. First, we develop a latent Gaussian
graphical model for mixed data that takes advantage of informative prior beliefs on the marginal distribution of variables. Next, we propose several shrinkage priors for precision matrices and develop estimation procedures for fast posterior explorations of a single and multiple
graphical models. This work is motivated by modeling survey-based cause of death instruments, known as verbal autopsies (VAs). Our methods provide new perspectives in improving
model performance while recovering useful dependencies in the VA data.
Advisors/Committee Members: McCormick, Tyler H. (advisor).
Subjects/Keywords: Bayesian methods; Graphical model; Spike-and-slab; Verbal Autopsy; Statistics; Statistics
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APA ·
Chicago ·
MLA ·
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CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Li, Z. (2018). Bayesian Methods for Graphical Models with Limited Data. (Doctoral Dissertation). University of Washington. Retrieved from http://hdl.handle.net/1773/43158
Chicago Manual of Style (16th Edition):
Li, Zehang. “Bayesian Methods for Graphical Models with Limited Data.” 2018. Doctoral Dissertation, University of Washington. Accessed January 16, 2021.
http://hdl.handle.net/1773/43158.
MLA Handbook (7th Edition):
Li, Zehang. “Bayesian Methods for Graphical Models with Limited Data.” 2018. Web. 16 Jan 2021.
Vancouver:
Li Z. Bayesian Methods for Graphical Models with Limited Data. [Internet] [Doctoral dissertation]. University of Washington; 2018. [cited 2021 Jan 16].
Available from: http://hdl.handle.net/1773/43158.
Council of Science Editors:
Li Z. Bayesian Methods for Graphical Models with Limited Data. [Doctoral Dissertation]. University of Washington; 2018. Available from: http://hdl.handle.net/1773/43158
Michigan Technological University
22.
Shi, Lufeng.
USING PROBABILISTIC GRAPHICAL MODELS TO DRAW INFERENCES IN SENSOR NETWORKS WITH TRACKING APPLICATIONS.
Degree: PhD, Department of Electrical and Computer Engineering, 2014, Michigan Technological University
URL: https://digitalcommons.mtu.edu/etds/752
► Sensor networks have been an active research area in the past decade due to the variety of their applications. Many research studies have been…
(more)
▼ Sensor networks have been an active research area in the past decade due to the variety of their applications. Many research studies have been conducted to solve the problems underlying the middleware services of sensor networks, such as self-deployment, self-localization, and synchronization. With the provided middleware services, sensor networks have grown into a mature technology to be used as a detection and surveillance paradigm for many real-world applications.
The individual sensors are small in size. Thus, they can be deployed in areas with limited space to make unobstructed measurements in locations where the traditional centralized systems would have trouble to reach. However, there are a few physical limitations to sensor networks, which can prevent sensors from performing at their maximum potential. Individual sensors have limited power supply, the wireless band can get very cluttered when multiple sensors try to transmit at the same time. Furthermore, the individual sensors have limited communication range, so the network may not have a 1-hop communication topology and routing can be a problem in many cases.
Carefully designed algorithms can alleviate the physical limitations of sensor networks, and allow them to be utilized to their full potential.
Graphical models are an intuitive choice for designing sensor network algorithms. This thesis focuses on a classic application in sensor networks, detecting and tracking of targets. It develops feasible inference techniques for sensor networks using statistical
graphical model inference, binary sensor detection, events isolation and dynamic clustering. The main strategy is to use only binary data for rough global inferences, and then dynamically form small scale clusters around the target for detailed computations. This framework is then extended to network topology manipulation, so that the framework developed can be applied to tracking in different network topology settings.
Finally the system was tested in both simulation and real-world environments. The simulations were performed on various network topologies, from regularly distributed networks to randomly distributed networks. The results show that the algorithm performs well in randomly distributed networks, and hence requires minimum deployment effort. The experiments were carried out in both corridor and open space settings. A in-home falling detection system was simulated with real-world settings, it was setup with 30 bumblebee radars and 30 ultrasonic sensors driven by TI EZ430-RF2500 boards scanning a typical 800 sqft apartment. Bumblebee radars are calibrated to detect the falling of human body, and the two-tier tracking algorithm is used on the ultrasonic sensors to track the location of the elderly people.
Advisors/Committee Members: Jindong Tan.
Subjects/Keywords: Graphical Model; Statistical Inference; Target Tracking; Wireless Sensor Network; Computer Engineering
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Shi, L. (2014). USING PROBABILISTIC GRAPHICAL MODELS TO DRAW INFERENCES IN SENSOR NETWORKS WITH TRACKING APPLICATIONS. (Doctoral Dissertation). Michigan Technological University. Retrieved from https://digitalcommons.mtu.edu/etds/752
Chicago Manual of Style (16th Edition):
Shi, Lufeng. “USING PROBABILISTIC GRAPHICAL MODELS TO DRAW INFERENCES IN SENSOR NETWORKS WITH TRACKING APPLICATIONS.” 2014. Doctoral Dissertation, Michigan Technological University. Accessed January 16, 2021.
https://digitalcommons.mtu.edu/etds/752.
MLA Handbook (7th Edition):
Shi, Lufeng. “USING PROBABILISTIC GRAPHICAL MODELS TO DRAW INFERENCES IN SENSOR NETWORKS WITH TRACKING APPLICATIONS.” 2014. Web. 16 Jan 2021.
Vancouver:
Shi L. USING PROBABILISTIC GRAPHICAL MODELS TO DRAW INFERENCES IN SENSOR NETWORKS WITH TRACKING APPLICATIONS. [Internet] [Doctoral dissertation]. Michigan Technological University; 2014. [cited 2021 Jan 16].
Available from: https://digitalcommons.mtu.edu/etds/752.
Council of Science Editors:
Shi L. USING PROBABILISTIC GRAPHICAL MODELS TO DRAW INFERENCES IN SENSOR NETWORKS WITH TRACKING APPLICATIONS. [Doctoral Dissertation]. Michigan Technological University; 2014. Available from: https://digitalcommons.mtu.edu/etds/752
University of Minnesota
23.
Fu, Qiang.
Efficient inference algorithms for some probabilistic graphical models.
Degree: PhD, Computer science, 2014, University of Minnesota
URL: http://hdl.handle.net/11299/162960
► The probabilistic graphical model framework provides an essential tool to reason coherently from limited and noisy observations. The framework has been used in an enormous…
(more)
▼ The probabilistic graphical model framework provides an essential tool to reason coherently from limited and noisy observations. The framework has been used in an enormous range of application domains, which include: natural language processing, computer vision, bioinformatic, robot navigation and many more. We propose several inference algorithms for some probabilistic graphical models. For Bayesian network graphical models, we focus on the problem of overlapping clustering, where a data point is allowed to belong to multiple clusters. We present an overlapping clustering algo- rithm based on multiplicative mixture models. We analyze a general setting where each component of the multiplicative mixture is from an exponential family, and present an efficient alternating maximization algorithm to learn the model and infer overlap- ping clusters. We also propose a Bayesian Overlapping Subspace Clustering (BOSC) model which is a hierarchical generative model for matrices with potentially overlapping uniform sub-block structures. The BOSC model can also handle matrices with missing entries. We propose an EM-style algorithm based on approximate inference using Gibbs sampling and parameter estimation using coordinate descent for the BOSC model. We propose an EM-style algorithm based on approximate inference using Gibbs sampling and parameter estimation using coordinate descent for the BOSC model. We also consider Markov random field graphical models and address the problem of maximum a posteriori (MAP) inference. We first show that the drought detection problem from the climate science domain can be formulated as a MAP inference problem and propose an automatic drought detection problem. We then present a parallel MAP inference algorithm called Bethe-ADMM based on two ideas: tree-decomposition of the graph and the alternating direction method of multipliers (ADMM). However, unlike the standard ADMM, we use an inexact ADMM augmented with a Bethe-divergence based proximal function, which makes each subproblem in ADMM easy to solve in parallel using the sum-product algorithm. We rigorously prove global convergence of Bethe-ADMM. The proposed algorithm is extensively evaluated on both synthetic and real datasets to illustrate its effectiveness. Further, the parallel Bethe-ADMM is shown to scale almost linearly with increasing number of cores.
Subjects/Keywords: Bayesian network; Graphical model; MAP inference; Markov random field; Overlapping; Clustering
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Fu, Q. (2014). Efficient inference algorithms for some probabilistic graphical models. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/162960
Chicago Manual of Style (16th Edition):
Fu, Qiang. “Efficient inference algorithms for some probabilistic graphical models.” 2014. Doctoral Dissertation, University of Minnesota. Accessed January 16, 2021.
http://hdl.handle.net/11299/162960.
MLA Handbook (7th Edition):
Fu, Qiang. “Efficient inference algorithms for some probabilistic graphical models.” 2014. Web. 16 Jan 2021.
Vancouver:
Fu Q. Efficient inference algorithms for some probabilistic graphical models. [Internet] [Doctoral dissertation]. University of Minnesota; 2014. [cited 2021 Jan 16].
Available from: http://hdl.handle.net/11299/162960.
Council of Science Editors:
Fu Q. Efficient inference algorithms for some probabilistic graphical models. [Doctoral Dissertation]. University of Minnesota; 2014. Available from: http://hdl.handle.net/11299/162960
University of Georgia
24.
Sulek, Thaddeus Robert.
An application of graphical models to fMRI data using the lasso penalty.
Degree: 2018, University of Georgia
URL: http://hdl.handle.net/10724/37031
► In this thesis, we study the graphical lasso method and apply it to functional magnetic resonance imaging (fMRI) data. The graphical lasso method enables one…
(more)
▼ In this thesis, we study the graphical lasso method and apply it to functional magnetic resonance imaging (fMRI) data. The graphical lasso method enables one to construct undirected sparse graphs between variables of interest. The fMRI data
concerns subjects’ brain activities while they engage in saccadic eye movement tasks. The datasets are collected before and after they practice certain tasks. Using the graphical lasso procedure, we create undirected graphs that display the connections
between the different regions of interest (ROI) in the brain. By controlling the regularization parameter in this lasso procedure, we identify which ROIs are more strongly connected than the others. We compare these undirected graphs before and after the
practice and also across different practice groups.
Subjects/Keywords: Functional Magnetic Resonance Imaging Data; Graphical Model; Lasso; Regions of Interest
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Sulek, T. R. (2018). An application of graphical models to fMRI data using the lasso penalty. (Thesis). University of Georgia. Retrieved from http://hdl.handle.net/10724/37031
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):
Sulek, Thaddeus Robert. “An application of graphical models to fMRI data using the lasso penalty.” 2018. Thesis, University of Georgia. Accessed January 16, 2021.
http://hdl.handle.net/10724/37031.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Sulek, Thaddeus Robert. “An application of graphical models to fMRI data using the lasso penalty.” 2018. Web. 16 Jan 2021.
Vancouver:
Sulek TR. An application of graphical models to fMRI data using the lasso penalty. [Internet] [Thesis]. University of Georgia; 2018. [cited 2021 Jan 16].
Available from: http://hdl.handle.net/10724/37031.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Sulek TR. An application of graphical models to fMRI data using the lasso penalty. [Thesis]. University of Georgia; 2018. Available from: http://hdl.handle.net/10724/37031
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Georgia
25.
Sulek, Thaddeus Robert.
An application of graphical models to fMRI data using the lasso penalty.
Degree: 2018, University of Georgia
URL: http://hdl.handle.net/10724/37188
► In this thesis, we study the graphical lasso method and apply it to functional magnetic resonance imaging (fMRI) data. The graphical lasso method enables one…
(more)
▼ In this thesis, we study the graphical lasso method and apply it to functional magnetic resonance imaging (fMRI) data. The graphical lasso method enables one to construct undirected sparse graphs between variables of interest. The fMRI data
concerns subjects’ brain activities while they engage in saccadic eye movement tasks. The datasets are collected before and after they practice certain tasks. Using the graphical lasso procedure, we create undirected graphs that display the connections
between the different regions of interest (ROI) in the brain. By controlling the regularization parameter in this lasso procedure, we identify which ROIs are more strongly connected than the others. We compare these undirected graphs before and after the
practice and also across different practice groups.
Subjects/Keywords: Functional Magnetic Resonance Imaging Data; Graphical Model; Lasso; Regions of Interest
Record Details
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Share »
Record Details
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Cite
« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Sulek, T. R. (2018). An application of graphical models to fMRI data using the lasso penalty. (Thesis). University of Georgia. Retrieved from http://hdl.handle.net/10724/37188
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):
Sulek, Thaddeus Robert. “An application of graphical models to fMRI data using the lasso penalty.” 2018. Thesis, University of Georgia. Accessed January 16, 2021.
http://hdl.handle.net/10724/37188.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Sulek, Thaddeus Robert. “An application of graphical models to fMRI data using the lasso penalty.” 2018. Web. 16 Jan 2021.
Vancouver:
Sulek TR. An application of graphical models to fMRI data using the lasso penalty. [Internet] [Thesis]. University of Georgia; 2018. [cited 2021 Jan 16].
Available from: http://hdl.handle.net/10724/37188.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Sulek TR. An application of graphical models to fMRI data using the lasso penalty. [Thesis]. University of Georgia; 2018. Available from: http://hdl.handle.net/10724/37188
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Cambridge
26.
Pacini, Clare.
Inferring condition specific regulatory networks with small sample sizes : a case study in Bacillus subtilis and infection of Mus musculus by the parasite Toxoplasma gondii.
Degree: PhD, 2017, University of Cambridge
URL: https://doi.org/10.17863/CAM.16660
;
https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744325
► Modelling interactions between genes and their regulators is fundamental to understanding how, for example a disease progresses, or the impact of inserting a synthetic circuit…
(more)
▼ Modelling interactions between genes and their regulators is fundamental to understanding how, for example a disease progresses, or the impact of inserting a synthetic circuit into a cell. We use an existing method to infer regulatory networks under multiple conditions: the Joint Graphical Lasso (JGL), a shrinkage based Gaussian graphical model. We apply this method to two data sets: one, a publicly available set of microarray experiments perturbing the gram-positive bacteria Bacillus subtilis under multiple experimental conditions; the second, a set of RNA-seq samples of Mouse (Mus musculus) embryonic fibroblasts (MEFs) infected with different strains of the parasite Toxoplasma gondii. In both cases we infer a subset of the regulatory networks using relatively small sample sizes. For the Bacillus subtilis analysis we focused on the use of these regulatory networks in synthetic biology and found examples of transcriptional units active only under a subset of conditions, this information can be useful when designing circuits to have condition dependent behaviour. We developed methods for large network decomposition that made use of the condition information and showed a greater specificity of identifying single transcriptional units from the larger network using our method. Through annotating these results with known information we were able to identify novel connections and found supporting evidence for a selection of these from publicly available experimental results. Biological data collection is typically expensive and due to the relatively small sample sizes of our MEF data set we developed a novel empirical Bayes method for reducing the false discovery rate when estimating block diagonal covariance matrices. Using these methods we were able to infer regulatory networks for the host infected with either the ME49 or RH strain of the parasite. This enabled the identification of known and novel regulatory mechanisms. The Toxoplasma gondii parasite has shown to subvert host function using similar mechanisms as cancers and through our analysis we were able to identify genes, networks and ontologies associated with cancer, including connections that have not previously been associated with T. gondii infection. Finally a Shiny application was developed as an online resource giving access to the Bacillus subtilis inferred networks with interactive methods for exploring the networks including expansion of sub networks and large network decomposition.
Subjects/Keywords: 572.8; Gaussian graphical model; Regulatory network; Small sample sizes
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Record Details
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« Share
❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Pacini, C. (2017). Inferring condition specific regulatory networks with small sample sizes : a case study in Bacillus subtilis and infection of Mus musculus by the parasite Toxoplasma gondii. (Doctoral Dissertation). University of Cambridge. Retrieved from https://doi.org/10.17863/CAM.16660 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744325
Chicago Manual of Style (16th Edition):
Pacini, Clare. “Inferring condition specific regulatory networks with small sample sizes : a case study in Bacillus subtilis and infection of Mus musculus by the parasite Toxoplasma gondii.” 2017. Doctoral Dissertation, University of Cambridge. Accessed January 16, 2021.
https://doi.org/10.17863/CAM.16660 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744325.
MLA Handbook (7th Edition):
Pacini, Clare. “Inferring condition specific regulatory networks with small sample sizes : a case study in Bacillus subtilis and infection of Mus musculus by the parasite Toxoplasma gondii.” 2017. Web. 16 Jan 2021.
Vancouver:
Pacini C. Inferring condition specific regulatory networks with small sample sizes : a case study in Bacillus subtilis and infection of Mus musculus by the parasite Toxoplasma gondii. [Internet] [Doctoral dissertation]. University of Cambridge; 2017. [cited 2021 Jan 16].
Available from: https://doi.org/10.17863/CAM.16660 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744325.
Council of Science Editors:
Pacini C. Inferring condition specific regulatory networks with small sample sizes : a case study in Bacillus subtilis and infection of Mus musculus by the parasite Toxoplasma gondii. [Doctoral Dissertation]. University of Cambridge; 2017. Available from: https://doi.org/10.17863/CAM.16660 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744325
Virginia Tech
27.
Wang, Yizhi.
Automated Analysis of Astrocyte Activities from Large-scale Time-lapse Microscopic Imaging Data.
Degree: PhD, Electrical Engineering, 2019, Virginia Tech
URL: http://hdl.handle.net/10919/95988
► Astrocyte is an important type of glial cell in the brain. Unlike neurons, astrocyte cannot be electrically excited. However, the concentrations of many different molecules…
(more)
▼ Astrocyte is an important type of glial cell in the brain. Unlike neurons, astrocyte cannot be electrically excited. However, the concentrations of many different molecules inside and near astrocytes change over space and time and show complex patterns. Recording, analyzing, and deciphering these activity patterns enables the understanding of various roles astrocyte may play in the nervous system. Many of these important roles, such as sensory-motor integration and brain state modulation, were traditionally considered the territory of neurons, but recently found to be related to astrocytes. These activities can be monitored in the intracellular and extracellular spaces in either brain slices and living animals, thanks to the advancement of microscopes and genetically encoded fluorescent sensors. However, sophisticated analytical tools lag far behind the impressive capability of generating the data. The major reason is that existing tools are all based on the region-of-interest-based (ROI) approach. This approach assumes the field of view can be segmented to many regions, and all pixels in the region should be active together. In neuronal activity analysis, all pixels in an ROI (region of interest) correspond to a neuron and are assumed to share a common activity pattern (curve). This is not true for astrocyte activity data because astrocyte activities are spatially unfixed, size-varying, and propagative. In this dissertation, we developed a framework called AQuA to detect the activities directly. We designed an accurate and flexible detection pipeline that works with different types of astrocyte activity data sets. We designed a machine learning
model to characterize the signal propagation for the pipeline. We also implemented a compressive and user-friendly software package. The advantage of AQuA is confirmed in both simulation studies and three different types of real data sets.
Advisors/Committee Members: Yu, Guoqiang (committeechair), Wang, Yue J. (committee member), Ressom, Habtom W. (committee member), Haghighat, Alireza (committee member), Chantem, Thidapat (committee member).
Subjects/Keywords: Astrocyte activity; Image analysis; Curve alignment; Graphical model
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APA (6th Edition):
Wang, Y. (2019). Automated Analysis of Astrocyte Activities from Large-scale Time-lapse Microscopic Imaging Data. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/95988
Chicago Manual of Style (16th Edition):
Wang, Yizhi. “Automated Analysis of Astrocyte Activities from Large-scale Time-lapse Microscopic Imaging Data.” 2019. Doctoral Dissertation, Virginia Tech. Accessed January 16, 2021.
http://hdl.handle.net/10919/95988.
MLA Handbook (7th Edition):
Wang, Yizhi. “Automated Analysis of Astrocyte Activities from Large-scale Time-lapse Microscopic Imaging Data.” 2019. Web. 16 Jan 2021.
Vancouver:
Wang Y. Automated Analysis of Astrocyte Activities from Large-scale Time-lapse Microscopic Imaging Data. [Internet] [Doctoral dissertation]. Virginia Tech; 2019. [cited 2021 Jan 16].
Available from: http://hdl.handle.net/10919/95988.
Council of Science Editors:
Wang Y. Automated Analysis of Astrocyte Activities from Large-scale Time-lapse Microscopic Imaging Data. [Doctoral Dissertation]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/95988
Virginia Tech
28.
Shan, Liang.
Joint Gaussian Graphical Model for multi-class and multi-level data.
Degree: PhD, Statistics, 2016, Virginia Tech
URL: http://hdl.handle.net/10919/81412
► Gaussian graphical model has been a popular tool to investigate conditional dependency between random variables by estimating sparse precision matrices. The estimated precision matrices could…
(more)
▼ Gaussian
graphical model has been a popular tool to investigate conditional dependency between random variables by estimating sparse precision matrices. The estimated precision matrices could be mapped into networks for visualization. For related but different classes, jointly estimating networks by taking advantage of common structure across classes can help us better estimate conditional dependencies among variables. Furthermore, there may exist multilevel structure among variables; some variables are considered as higher level variables and others are nested in these higher level variables, which are called lower level variables. In this dissertation, we made several contributions to the area of joint estimation of Gaussian
graphical models across heterogeneous classes: the first is to propose a joint estimation method for estimating Gaussian
graphical models across unbalanced multi-classes, whereas the second considers multilevel variable information during the joint estimation procedure and simultaneously estimates higher level network and lower level network.
For the first project, we consider the problem of jointly estimating Gaussian
graphical models across unbalanced multi-class. Most existing methods require equal or similar sample size among classes. However, many real applications do not have similar sample sizes. Hence, in this dissertation, we propose the joint adaptive
graphical lasso, a weighted L1 penalized approach, for unbalanced multi-class problems. Our joint adaptive
graphical lasso approach combines information across classes so that their common characteristics can be shared during the estimation process. We also introduce regularization into the adaptive term so that the unbalancedness of data is taken into account. Simulation studies show that our approach performs better than existing methods in terms of false positive rate, accuracy, Mathews correlation coefficient, and false discovery rate. We demonstrate the advantage of our approach using liver cancer data set.
For the second one, we propose a method to jointly estimate the multilevel Gaussian
graphical models across multiple classes. Currently, methods are still limited to investigate a single level conditional dependency structure when there exists the multilevel structure among variables. Due to the fact that higher level variables may work together to accomplish certain tasks, simultaneously exploring conditional dependency structures among higher level variables and among lower level variables are of our main interest. Given multilevel data from heterogeneous classes, our method assures that common structures in terms of the multilevel conditional dependency are shared during the estimation procedure, yet unique structures for each class are retained as well. Our proposed approach is achieved by first introducing a higher level variable factor within a class, and then common factors across classes. The performance of our approach is evaluated on several simulated networks. We also demonstrate the advantage of our approach using…
Advisors/Committee Members: Kim, Inyoung (committeechair), Terrell, George R. (committee member), Deng, Xinwei (committee member), Guo, Feng (committee member).
Subjects/Keywords: Bias Correction; Gaussian graphical model; Heterogeneous classes; Joint adaptive graphical lasso; Joint estimation; Multilevel network; Precision matrix; Unbalanced multi-class.
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APA ·
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MLA ·
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APA (6th Edition):
Shan, L. (2016). Joint Gaussian Graphical Model for multi-class and multi-level data. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/81412
Chicago Manual of Style (16th Edition):
Shan, Liang. “Joint Gaussian Graphical Model for multi-class and multi-level data.” 2016. Doctoral Dissertation, Virginia Tech. Accessed January 16, 2021.
http://hdl.handle.net/10919/81412.
MLA Handbook (7th Edition):
Shan, Liang. “Joint Gaussian Graphical Model for multi-class and multi-level data.” 2016. Web. 16 Jan 2021.
Vancouver:
Shan L. Joint Gaussian Graphical Model for multi-class and multi-level data. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2021 Jan 16].
Available from: http://hdl.handle.net/10919/81412.
Council of Science Editors:
Shan L. Joint Gaussian Graphical Model for multi-class and multi-level data. [Doctoral Dissertation]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/81412
Penn State University
29.
Lee, Kevin Haeseung.
Statistical Learning of Complex Large-Scale Dynamic Systems.
Degree: 2017, Penn State University
URL: https://submit-etda.libraries.psu.edu/catalog/14151khl119
► Due to advances in data collection technologies, large-scale network/graph analysis has been increasingly important in various research fields such as artificial intelligence, business, finance, genomics,…
(more)
▼ Due to advances in data collection technologies, large-scale network/graph analysis has been increasingly important in various research fields such as artificial intelligence, business, finance, genomics, physics, sociology and many others. Moreover, recent large-scale network and high-dimensional data show the following common properties which present new challenges for existing statistical methods: i) the data come from different resources and have heterogeneous relations or dependencies; ii) the hidden structures may change over time as relations and dependencies are rarely static; and iii) the data are often collected in large-scale dynamic fashion. Hence, this dissertation focuses on modeling and learning large-scale dynamic networks and exploring the heterogeneous dependencies of high-dimensional data.
Dynamic networks modeling provides an emerging statistical technique for various real-world applications. It is a fundamental research question to detect the community structure in dynamic networks. However, due to significant computational challenges and difficulties in modeling communities, there is little progress in the current literature to effectively find communities in dynamic networks. In this dissertation, we introduce a novel
model-based clustering framework for dynamic networks, which is based on (semiparametric) exponential-family random graph models and inherits the philosophy of finite mixture modeling. To determine an appropriate number of communities, a composite conditional likelihood Bayesian information criterion is proposed. Moreover, an efficient variational expectation-maximization algorithm is designed to solve approximate maximum likelihood estimates of network parameters and mixing proportions. By using variational methods and minorization-maximization techniques, our methods have appealing scalability for large-scale dynamic networks. Finally, the power of our method is demonstrated by simulation studies and real-world applications.
Graphical models have been widely used to investigate the complex dependence structure of high-dimensional data, and it is common to assume that observed data follow a homogeneous
graphical model. However, observations usually come from different resources and have heterogeneous hidden commonality in real-world applications. In this dissertation, we introduce a novel regularized estimation scheme for learning a nonparametric mixture of Gaussian
graphical models, which explores the heterogeneous dependencies of high-dimensional data. We propose a unified penalized likelihood approach to effectively estimate both nonparametric functional parameters and heterogeneous
graphical parameters. We also present a generalized effective EM algorithm to address both non-convex optimization in high dimensions and the label-switching issue. Moreover, we prove both the ascent property and the local convergence hold for our proposed algorithm with probability tending to 1 and also verify the asymptotic properties of the local solution for our
model under standard…
Advisors/Committee Members: Lingzhou Xue, Dissertation Advisor/Co-Advisor, Lingzhou Xue, Committee Chair/Co-Chair, David R. Hunter, Committee Member, Runze Li, Committee Member, Nanyin Zhang, Outside Member, Mark S. Handcock, Special Member.
Subjects/Keywords: Statistical learning; Model-based clustering; Dynamic networks; Graphical model; EM algorithm; Variational inference
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APA ·
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MLA ·
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CSE |
Export
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APA (6th Edition):
Lee, K. H. (2017). Statistical Learning of Complex Large-Scale Dynamic Systems. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/14151khl119
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):
Lee, Kevin Haeseung. “Statistical Learning of Complex Large-Scale Dynamic Systems.” 2017. Thesis, Penn State University. Accessed January 16, 2021.
https://submit-etda.libraries.psu.edu/catalog/14151khl119.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Lee, Kevin Haeseung. “Statistical Learning of Complex Large-Scale Dynamic Systems.” 2017. Web. 16 Jan 2021.
Vancouver:
Lee KH. Statistical Learning of Complex Large-Scale Dynamic Systems. [Internet] [Thesis]. Penn State University; 2017. [cited 2021 Jan 16].
Available from: https://submit-etda.libraries.psu.edu/catalog/14151khl119.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Lee KH. Statistical Learning of Complex Large-Scale Dynamic Systems. [Thesis]. Penn State University; 2017. Available from: https://submit-etda.libraries.psu.edu/catalog/14151khl119
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Brno University of Technology
30.
Hraboš, Martin.
Simulátor robotického pracoviště: Simulator of Robotic Arm Workcell.
Degree: 2019, Brno University of Technology
URL: http://hdl.handle.net/11012/54934
► This thesis describes design and implementation of an application for simulating robotic arm. The real model of this robot is located in the Faculty of…
(more)
▼ This thesis describes design and implementation of an application for simulating robotic arm. The real
model of this robot is located in the Faculty of Information Technology in Brno. It is possible to control the moves of the robotic arm and also switch the view at the scene from six cameras.
Advisors/Committee Members: Luža, Radim (advisor), Rozman, Jaroslav (referee).
Subjects/Keywords: simulace; robotika; model; grafické rozhraní; OpenRAVE; Qt; simulation; robotics; model; graphical interface; OpenRAVE; Qt
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APA ·
Chicago ·
MLA ·
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Export
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Manager
APA (6th Edition):
Hraboš, M. (2019). Simulátor robotického pracoviště: Simulator of Robotic Arm Workcell. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/54934
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):
Hraboš, Martin. “Simulátor robotického pracoviště: Simulator of Robotic Arm Workcell.” 2019. Thesis, Brno University of Technology. Accessed January 16, 2021.
http://hdl.handle.net/11012/54934.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Hraboš, Martin. “Simulátor robotického pracoviště: Simulator of Robotic Arm Workcell.” 2019. Web. 16 Jan 2021.
Vancouver:
Hraboš M. Simulátor robotického pracoviště: Simulator of Robotic Arm Workcell. [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2021 Jan 16].
Available from: http://hdl.handle.net/11012/54934.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Hraboš M. Simulátor robotického pracoviště: Simulator of Robotic Arm Workcell. [Thesis]. Brno University of Technology; 2019. Available from: http://hdl.handle.net/11012/54934
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
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Open Access Theses and Dissertations
