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You searched for `subject:(probabilistic inference)`

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106 total matches.

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University of Melbourne

1.
Li, Yuan.
* Probabilistic* models for aggregating crowdsourced annotations.

Degree: 2019, University of Melbourne

URL: http://hdl.handle.net/11343/227106

► This thesis explores aggregation methods for crowdsourced annotations. Crowdsourcing is a popular means of creating training and evaluation datasets for machine learning, e.g. used for…
(more)

Subjects/Keywords: crowdsourcing; probabilistic models; Bayesian inference

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Li, Y. (2019). Probabilistic models for aggregating crowdsourced annotations. (Doctoral Dissertation). University of Melbourne. Retrieved from http://hdl.handle.net/11343/227106

Chicago Manual of Style (16^{th} Edition):

Li, Yuan. “Probabilistic models for aggregating crowdsourced annotations.” 2019. Doctoral Dissertation, University of Melbourne. Accessed May 11, 2021. http://hdl.handle.net/11343/227106.

MLA Handbook (7^{th} Edition):

Li, Yuan. “Probabilistic models for aggregating crowdsourced annotations.” 2019. Web. 11 May 2021.

Vancouver:

Li Y. Probabilistic models for aggregating crowdsourced annotations. [Internet] [Doctoral dissertation]. University of Melbourne; 2019. [cited 2021 May 11]. Available from: http://hdl.handle.net/11343/227106.

Council of Science Editors:

Li Y. Probabilistic models for aggregating crowdsourced annotations. [Doctoral Dissertation]. University of Melbourne; 2019. Available from: http://hdl.handle.net/11343/227106

Cornell University

2.
Radecki, Peter.
Applied *Probabilistic* *Inference*: Model Estimation For Hvac Predictive Controls And All-Weather Perception For Autonomous Vehicles.

Degree: PhD, Mechanical Engineering, 2016, Cornell University

URL: http://hdl.handle.net/1813/44364

► *Probabilistic* *inference* and reasoning is applied to two major application areas: HVAC controls in buildings and autonomous vehicle perception. Although the physical domains differ vastly,…
(more)

Subjects/Keywords: Kalman Filter; Probabilistic Inference; Model Predictive Control

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APA (6^{th} Edition):

Radecki, P. (2016). Applied Probabilistic Inference: Model Estimation For Hvac Predictive Controls And All-Weather Perception For Autonomous Vehicles. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/44364

Chicago Manual of Style (16^{th} Edition):

Radecki, Peter. “Applied Probabilistic Inference: Model Estimation For Hvac Predictive Controls And All-Weather Perception For Autonomous Vehicles.” 2016. Doctoral Dissertation, Cornell University. Accessed May 11, 2021. http://hdl.handle.net/1813/44364.

MLA Handbook (7^{th} Edition):

Radecki, Peter. “Applied Probabilistic Inference: Model Estimation For Hvac Predictive Controls And All-Weather Perception For Autonomous Vehicles.” 2016. Web. 11 May 2021.

Vancouver:

Radecki P. Applied Probabilistic Inference: Model Estimation For Hvac Predictive Controls And All-Weather Perception For Autonomous Vehicles. [Internet] [Doctoral dissertation]. Cornell University; 2016. [cited 2021 May 11]. Available from: http://hdl.handle.net/1813/44364.

Council of Science Editors:

Radecki P. Applied Probabilistic Inference: Model Estimation For Hvac Predictive Controls And All-Weather Perception For Autonomous Vehicles. [Doctoral Dissertation]. Cornell University; 2016. Available from: http://hdl.handle.net/1813/44364

Australian National University

3.
Mohasel Afshar, Hadi.
*Probabilistic**Inference* in Piecewise Graphical Models
.

Degree: 2016, Australian National University

URL: http://hdl.handle.net/1885/107386

► In many applications of *probabilistic* *inference* the models contain piecewise densities that are differentiable except at partition boundaries. For instance, (1) some models may intrinsically…
(more)

Subjects/Keywords: Piecewise; Graphical models; probabilistic inference; MCMC; sampling

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APA (6^{th} Edition):

Mohasel Afshar, H. (2016). Probabilistic Inference in Piecewise Graphical Models . (Thesis). Australian National University. Retrieved from http://hdl.handle.net/1885/107386

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Mohasel Afshar, Hadi. “Probabilistic Inference in Piecewise Graphical Models .” 2016. Thesis, Australian National University. Accessed May 11, 2021. http://hdl.handle.net/1885/107386.

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Mohasel Afshar, Hadi. “Probabilistic Inference in Piecewise Graphical Models .” 2016. Web. 11 May 2021.

Vancouver:

Mohasel Afshar H. Probabilistic Inference in Piecewise Graphical Models . [Internet] [Thesis]. Australian National University; 2016. [cited 2021 May 11]. Available from: http://hdl.handle.net/1885/107386.

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Mohasel Afshar H. Probabilistic Inference in Piecewise Graphical Models . [Thesis]. Australian National University; 2016. Available from: http://hdl.handle.net/1885/107386

Not specified: Masters Thesis or Doctoral Dissertation

Rice University

4.
Vasudeva Raju, Rajkumar.
Inferring Implicit * Inference*.

Degree: PhD, Engineering, 2019, Rice University

URL: http://hdl.handle.net/1911/107811

► One of the biggest challenges in theoretical neuroscience is to understand how the collective activity of neuronal populations generate behaviorally relevant computations. Repeating patterns of…
(more)

Subjects/Keywords: neural message passing; probabilistic inference; probabilistic population codes

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APA (6^{th} Edition):

Vasudeva Raju, R. (2019). Inferring Implicit Inference. (Doctoral Dissertation). Rice University. Retrieved from http://hdl.handle.net/1911/107811

Chicago Manual of Style (16^{th} Edition):

Vasudeva Raju, Rajkumar. “Inferring Implicit Inference.” 2019. Doctoral Dissertation, Rice University. Accessed May 11, 2021. http://hdl.handle.net/1911/107811.

MLA Handbook (7^{th} Edition):

Vasudeva Raju, Rajkumar. “Inferring Implicit Inference.” 2019. Web. 11 May 2021.

Vancouver:

Vasudeva Raju R. Inferring Implicit Inference. [Internet] [Doctoral dissertation]. Rice University; 2019. [cited 2021 May 11]. Available from: http://hdl.handle.net/1911/107811.

Council of Science Editors:

Vasudeva Raju R. Inferring Implicit Inference. [Doctoral Dissertation]. Rice University; 2019. Available from: http://hdl.handle.net/1911/107811

Rice University

5.
Vasudeva Raju, Rajkumar.
* Inference* by Reparameterization using Neural Population Codes.

Degree: MS, Engineering, 2015, Rice University

URL: http://hdl.handle.net/1911/88182

► Behavioral experiments on humans and animals suggest that the brain performs *probabilistic* *inference* to interpret its environment. Here we present a general-purpose, biologically plausible implementation…
(more)

Subjects/Keywords: Probabilistic Inference; Probabilistic Population Codes; Tree-based Re-parameterization; neural network

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Vasudeva Raju, R. (2015). Inference by Reparameterization using Neural Population Codes. (Masters Thesis). Rice University. Retrieved from http://hdl.handle.net/1911/88182

Chicago Manual of Style (16^{th} Edition):

Vasudeva Raju, Rajkumar. “Inference by Reparameterization using Neural Population Codes.” 2015. Masters Thesis, Rice University. Accessed May 11, 2021. http://hdl.handle.net/1911/88182.

MLA Handbook (7^{th} Edition):

Vasudeva Raju, Rajkumar. “Inference by Reparameterization using Neural Population Codes.” 2015. Web. 11 May 2021.

Vancouver:

Vasudeva Raju R. Inference by Reparameterization using Neural Population Codes. [Internet] [Masters thesis]. Rice University; 2015. [cited 2021 May 11]. Available from: http://hdl.handle.net/1911/88182.

Council of Science Editors:

Vasudeva Raju R. Inference by Reparameterization using Neural Population Codes. [Masters Thesis]. Rice University; 2015. Available from: http://hdl.handle.net/1911/88182

Universiteit Utrecht

6.
Burgwal, M.D. van de.
Treecost-based Preprocessing for *Probabilistic* Networks.

Degree: 2015, Universiteit Utrecht

URL: http://dspace.library.uu.nl:8080/handle/1874/311172

► *Probabilistic* *inference* is an important problem in probability theory and concerns the process of computing the probability distribution of variables, given the evidence of other…
(more)

Subjects/Keywords: Treecost; treewidth; preprocessing; tree decomposition; probabilistic inference; probabilistic networks; graph theory; network theory

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Burgwal, M. D. v. d. (2015). Treecost-based Preprocessing for Probabilistic Networks. (Masters Thesis). Universiteit Utrecht. Retrieved from http://dspace.library.uu.nl:8080/handle/1874/311172

Chicago Manual of Style (16^{th} Edition):

Burgwal, M D van de. “Treecost-based Preprocessing for Probabilistic Networks.” 2015. Masters Thesis, Universiteit Utrecht. Accessed May 11, 2021. http://dspace.library.uu.nl:8080/handle/1874/311172.

MLA Handbook (7^{th} Edition):

Burgwal, M D van de. “Treecost-based Preprocessing for Probabilistic Networks.” 2015. Web. 11 May 2021.

Vancouver:

Burgwal MDvd. Treecost-based Preprocessing for Probabilistic Networks. [Internet] [Masters thesis]. Universiteit Utrecht; 2015. [cited 2021 May 11]. Available from: http://dspace.library.uu.nl:8080/handle/1874/311172.

Council of Science Editors:

Burgwal MDvd. Treecost-based Preprocessing for Probabilistic Networks. [Masters Thesis]. Universiteit Utrecht; 2015. Available from: http://dspace.library.uu.nl:8080/handle/1874/311172

University of Illinois – Urbana-Champaign

7.
Ko, Glenn Gihyun.
Sampling architectures for *probabilistic* * inference*.

Degree: PhD, Electrical & Computer Engr, 2017, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/99482

► In recent years, machine learning (ML) algorithms for applications such as computer vision, machine listening, topic modeling (i.e., extraction) from large text data sets, etc.,…
(more)

Subjects/Keywords: Machine learning; Probabilistic graphical model; Probabilistic inference; Markov chain Monte Carlo; Gibbs sampling

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Ko, G. G. (2017). Sampling architectures for probabilistic inference. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/99482

Chicago Manual of Style (16^{th} Edition):

Ko, Glenn Gihyun. “Sampling architectures for probabilistic inference.” 2017. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed May 11, 2021. http://hdl.handle.net/2142/99482.

MLA Handbook (7^{th} Edition):

Ko, Glenn Gihyun. “Sampling architectures for probabilistic inference.” 2017. Web. 11 May 2021.

Vancouver:

Ko GG. Sampling architectures for probabilistic inference. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2017. [cited 2021 May 11]. Available from: http://hdl.handle.net/2142/99482.

Council of Science Editors:

Ko GG. Sampling architectures for probabilistic inference. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2017. Available from: http://hdl.handle.net/2142/99482

University of Cambridge

8.
Hennig, Philipp.
Approximate *inference* in graphical models.

Degree: PhD, 2011, University of Cambridge

URL: https://doi.org/10.17863/CAM.16567 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.541854

► Probability theory provides a mathematically rigorous yet conceptually flexible calculus of uncertainty, allowing the construction of complex hierarchical models for real-world *inference* tasks. Unfortunately, exact…
(more)

Subjects/Keywords: 519.2; Applied mathematics; Computer science; Probability theory; Probabilistic inference; Graphical models; Approximate inference

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Hennig, P. (2011). Approximate inference in graphical models. (Doctoral Dissertation). University of Cambridge. Retrieved from https://doi.org/10.17863/CAM.16567 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.541854

Chicago Manual of Style (16^{th} Edition):

Hennig, Philipp. “Approximate inference in graphical models.” 2011. Doctoral Dissertation, University of Cambridge. Accessed May 11, 2021. https://doi.org/10.17863/CAM.16567 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.541854.

MLA Handbook (7^{th} Edition):

Hennig, Philipp. “Approximate inference in graphical models.” 2011. Web. 11 May 2021.

Vancouver:

Hennig P. Approximate inference in graphical models. [Internet] [Doctoral dissertation]. University of Cambridge; 2011. [cited 2021 May 11]. Available from: https://doi.org/10.17863/CAM.16567 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.541854.

Council of Science Editors:

Hennig P. Approximate inference in graphical models. [Doctoral Dissertation]. University of Cambridge; 2011. Available from: https://doi.org/10.17863/CAM.16567 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.541854

9.
Hegnar, Eva.
* Probabilistic* deep learning with variational

Degree: Chalmers tekniska högskola / Institutionen för matematiska vetenskaper, 2020, Chalmers University of Technology

URL: http://hdl.handle.net/20.500.12380/301602

► Deep neural networks are used in the petroleum industry to model gas and oil rate. To optimise the production, the uncertainty of the network predictions…
(more)

Subjects/Keywords: deep neural network; Bayesian inference; variational inference; black box variational inference; reparameterisation trick; probabilistic modelling; production optimisation; flow rate estimation

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APA (6^{th} Edition):

Hegnar, E. (2020). Probabilistic deep learning with variational inference . (Thesis). Chalmers University of Technology. Retrieved from http://hdl.handle.net/20.500.12380/301602

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Hegnar, Eva. “Probabilistic deep learning with variational inference .” 2020. Thesis, Chalmers University of Technology. Accessed May 11, 2021. http://hdl.handle.net/20.500.12380/301602.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Hegnar, Eva. “Probabilistic deep learning with variational inference .” 2020. Web. 11 May 2021.

Vancouver:

Hegnar E. Probabilistic deep learning with variational inference . [Internet] [Thesis]. Chalmers University of Technology; 2020. [cited 2021 May 11]. Available from: http://hdl.handle.net/20.500.12380/301602.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Hegnar E. Probabilistic deep learning with variational inference . [Thesis]. Chalmers University of Technology; 2020. Available from: http://hdl.handle.net/20.500.12380/301602

Not specified: Masters Thesis or Doctoral Dissertation

Mississippi State University

10. 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)

Subjects/Keywords: POMDP; Graphical Model; Probabilistic Inference; Theoretical Decision Planning; Influence Diagram

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APA (6^{th} 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 (16^{th} Edition):

Shi, Jinchuan. “A framework for integrating influence diagrams and POMDPs.” 2018. Doctoral Dissertation, Mississippi State University. Accessed May 11, 2021. http://sun.library.msstate.edu/ETD-db/theses/available/etd-03022018-153923/ ;.

MLA Handbook (7^{th} Edition):

Shi, Jinchuan. “A framework for integrating influence diagrams and POMDPs.” 2018. Web. 11 May 2021.

Vancouver:

Shi J. A framework for integrating influence diagrams and POMDPs. [Internet] [Doctoral dissertation]. Mississippi State University; 2018. [cited 2021 May 11]. 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 Toronto

11.
Dhoot, Aditya.
Wind Farm Layout Optimization Using Approximate *Inference* in Graphical Models.

Degree: 2016, University of Toronto

URL: http://hdl.handle.net/1807/72692

►

Wind farm layout optimization (WFLO) determines the optimal location of wind turbines within a fixed geographical area to maximize the total power capacity of the… (more)

Subjects/Keywords: approximate inference; probabilistic graphical models; wind farm design; 0796

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Dhoot, A. (2016). Wind Farm Layout Optimization Using Approximate Inference in Graphical Models. (Masters Thesis). University of Toronto. Retrieved from http://hdl.handle.net/1807/72692

Chicago Manual of Style (16^{th} Edition):

Dhoot, Aditya. “Wind Farm Layout Optimization Using Approximate Inference in Graphical Models.” 2016. Masters Thesis, University of Toronto. Accessed May 11, 2021. http://hdl.handle.net/1807/72692.

MLA Handbook (7^{th} Edition):

Dhoot, Aditya. “Wind Farm Layout Optimization Using Approximate Inference in Graphical Models.” 2016. Web. 11 May 2021.

Vancouver:

Dhoot A. Wind Farm Layout Optimization Using Approximate Inference in Graphical Models. [Internet] [Masters thesis]. University of Toronto; 2016. [cited 2021 May 11]. Available from: http://hdl.handle.net/1807/72692.

Council of Science Editors:

Dhoot A. Wind Farm Layout Optimization Using Approximate Inference in Graphical Models. [Masters Thesis]. University of Toronto; 2016. Available from: http://hdl.handle.net/1807/72692

George Mason University

12.
Sun, Wei.
Efficient *Inference* For Hybrid Bayesian Networks
.

Degree: 2007, George Mason University

URL: http://hdl.handle.net/1920/2952

► Uncertainty is everywhere in real life so we have to use stochastic model for most real-world problems. In general, both the systems mechanism and the…
(more)

Subjects/Keywords: Bayesian networks; probabilistic inference; algorithm

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Sun, W. (2007). Efficient Inference For Hybrid Bayesian Networks . (Thesis). George Mason University. Retrieved from http://hdl.handle.net/1920/2952

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Sun, Wei. “Efficient Inference For Hybrid Bayesian Networks .” 2007. Thesis, George Mason University. Accessed May 11, 2021. http://hdl.handle.net/1920/2952.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Sun, Wei. “Efficient Inference For Hybrid Bayesian Networks .” 2007. Web. 11 May 2021.

Vancouver:

Sun W. Efficient Inference For Hybrid Bayesian Networks . [Internet] [Thesis]. George Mason University; 2007. [cited 2021 May 11]. Available from: http://hdl.handle.net/1920/2952.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Sun W. Efficient Inference For Hybrid Bayesian Networks . [Thesis]. George Mason University; 2007. Available from: http://hdl.handle.net/1920/2952

Not specified: Masters Thesis or Doctoral Dissertation

Indiana University

13. Narayanan, Praveen. Verifiable and reusable conditioning .

Degree: 2019, Indiana University

URL: http://hdl.handle.net/2022/24645

► Bayesian analysis exhibits two kinds of modularity. First, it is composed of conceptually separate steps: modeling and *inference*. Second, *inference* is itself composed of two…
(more)

Subjects/Keywords: Bayesian inference; disintegration; measure theory; metaprogramming; probabilistic programming

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APA (6^{th} Edition):

Narayanan, P. (2019). Verifiable and reusable conditioning . (Thesis). Indiana University. Retrieved from http://hdl.handle.net/2022/24645

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Narayanan, Praveen. “Verifiable and reusable conditioning .” 2019. Thesis, Indiana University. Accessed May 11, 2021. http://hdl.handle.net/2022/24645.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Narayanan, Praveen. “Verifiable and reusable conditioning .” 2019. Web. 11 May 2021.

Vancouver:

Narayanan P. Verifiable and reusable conditioning . [Internet] [Thesis]. Indiana University; 2019. [cited 2021 May 11]. Available from: http://hdl.handle.net/2022/24645.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Narayanan P. Verifiable and reusable conditioning . [Thesis]. Indiana University; 2019. Available from: http://hdl.handle.net/2022/24645

Not specified: Masters Thesis or Doctoral Dissertation

University of Toronto

14. Lazic, Nevena. Message Passing Algorithms for Facility Location Problems.

Degree: 2011, University of Toronto

URL: http://hdl.handle.net/1807/27587

►

Discrete location analysis is one of the most widely studied branches of operations research, whose applications arise in a wide variety of settings. This thesis… (more)

Subjects/Keywords: probabilistic graphical models; facility location; MAP inference; 0544

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Lazic, N. (2011). Message Passing Algorithms for Facility Location Problems. (Doctoral Dissertation). University of Toronto. Retrieved from http://hdl.handle.net/1807/27587

Chicago Manual of Style (16^{th} Edition):

Lazic, Nevena. “Message Passing Algorithms for Facility Location Problems.” 2011. Doctoral Dissertation, University of Toronto. Accessed May 11, 2021. http://hdl.handle.net/1807/27587.

MLA Handbook (7^{th} Edition):

Lazic, Nevena. “Message Passing Algorithms for Facility Location Problems.” 2011. Web. 11 May 2021.

Vancouver:

Lazic N. Message Passing Algorithms for Facility Location Problems. [Internet] [Doctoral dissertation]. University of Toronto; 2011. [cited 2021 May 11]. Available from: http://hdl.handle.net/1807/27587.

Council of Science Editors:

Lazic N. Message Passing Algorithms for Facility Location Problems. [Doctoral Dissertation]. University of Toronto; 2011. Available from: http://hdl.handle.net/1807/27587

University of Illinois – Urbana-Champaign

15.
Kim, Minseo.
* Probabilistic* models based on experimental observations using sparse bayes methodology.

Degree: MS, 0106, 2014, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/46861

► The quality of people’s lives depends on safe and reliable infrastructure. However, there exist various types of uncertainties that may influence performance of structures, which…
(more)

Subjects/Keywords: Sparse Bayes Methodology; Bayesian Inference; Probabilistic shear strength model

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APA (6^{th} Edition):

Kim, M. (2014). Probabilistic models based on experimental observations using sparse bayes methodology. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/46861

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Kim, Minseo. “Probabilistic models based on experimental observations using sparse bayes methodology.” 2014. Thesis, University of Illinois – Urbana-Champaign. Accessed May 11, 2021. http://hdl.handle.net/2142/46861.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Kim, Minseo. “Probabilistic models based on experimental observations using sparse bayes methodology.” 2014. Web. 11 May 2021.

Vancouver:

Kim M. Probabilistic models based on experimental observations using sparse bayes methodology. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2014. [cited 2021 May 11]. Available from: http://hdl.handle.net/2142/46861.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Kim M. Probabilistic models based on experimental observations using sparse bayes methodology. [Thesis]. University of Illinois – Urbana-Champaign; 2014. Available from: http://hdl.handle.net/2142/46861

Not specified: Masters Thesis or Doctoral Dissertation

University of Southern California

16.
Ma, Nam.
Scalable exact *inference* in *probabilistic* graphical models
on multi-core platforms.

Degree: PhD, Computer Science, 2014, University of Southern California

URL: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/373594/rec/5688

► The recent switch to multi‐core computing and the emergence of machine learning applications have offered many opportunities for parallelization. However, achieving scalability with respect to…
(more)

Subjects/Keywords: parallel algorithms; probabilistic inference; scalability; graph computations; graphical models; multi‐core

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Ma, N. (2014). Scalable exact inference in probabilistic graphical models on multi-core platforms. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/373594/rec/5688

Chicago Manual of Style (16^{th} Edition):

Ma, Nam. “Scalable exact inference in probabilistic graphical models on multi-core platforms.” 2014. Doctoral Dissertation, University of Southern California. Accessed May 11, 2021. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/373594/rec/5688.

MLA Handbook (7^{th} Edition):

Ma, Nam. “Scalable exact inference in probabilistic graphical models on multi-core platforms.” 2014. Web. 11 May 2021.

Vancouver:

Ma N. Scalable exact inference in probabilistic graphical models on multi-core platforms. [Internet] [Doctoral dissertation]. University of Southern California; 2014. [cited 2021 May 11]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/373594/rec/5688.

Council of Science Editors:

Ma N. Scalable exact inference in probabilistic graphical models on multi-core platforms. [Doctoral Dissertation]. University of Southern California; 2014. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/373594/rec/5688

17. Salmon, Ricardo. On the relationship between satisfiability and partially observable Markov decision processes.

Degree: 2018, University of Waterloo

URL: http://hdl.handle.net/10012/13951

► Stochastic satisfiability (SSAT), Quantified Boolean Satisfiability (QBF) and decision-theoretic planning in finite horizon partially observable Markov decision processes (POMDPs) are all PSPACE-Complete problems. Since they…
(more)

Subjects/Keywords: POMDP; Stochastic SAT; Satisfiability; Planning; Probabilistic Inference; SAT; QBF

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Salmon, R. (2018). On the relationship between satisfiability and partially observable Markov decision processes. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/13951

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Salmon, Ricardo. “On the relationship between satisfiability and partially observable Markov decision processes.” 2018. Thesis, University of Waterloo. Accessed May 11, 2021. http://hdl.handle.net/10012/13951.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Salmon, Ricardo. “On the relationship between satisfiability and partially observable Markov decision processes.” 2018. Web. 11 May 2021.

Vancouver:

Salmon R. On the relationship between satisfiability and partially observable Markov decision processes. [Internet] [Thesis]. University of Waterloo; 2018. [cited 2021 May 11]. Available from: http://hdl.handle.net/10012/13951.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Salmon R. On the relationship between satisfiability and partially observable Markov decision processes. [Thesis]. University of Waterloo; 2018. Available from: http://hdl.handle.net/10012/13951

Not specified: Masters Thesis or Doctoral Dissertation

University of Guelph

18. Wang, Qian. Learning Non-Impeding Noisy-AND Tree Model Based Bayesian Networks From Data.

Degree: MS, School of Computer Science, 2020, University of Guelph

URL: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/17813

► Bayesian Networks (BNs) are a widely utilized formalism for representing knowledge in intelligent agents on partially observable and stochastic application environments. When conditional probability tables…
(more)

Subjects/Keywords: Bayesian networks; Causal independence; Probabilistic inference; Local structures

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Wang, Q. (2020). Learning Non-Impeding Noisy-AND Tree Model Based Bayesian Networks From Data. (Masters Thesis). University of Guelph. Retrieved from https://atrium.lib.uoguelph.ca/xmlui/handle/10214/17813

Chicago Manual of Style (16^{th} Edition):

Wang, Qian. “Learning Non-Impeding Noisy-AND Tree Model Based Bayesian Networks From Data.” 2020. Masters Thesis, University of Guelph. Accessed May 11, 2021. https://atrium.lib.uoguelph.ca/xmlui/handle/10214/17813.

MLA Handbook (7^{th} Edition):

Wang, Qian. “Learning Non-Impeding Noisy-AND Tree Model Based Bayesian Networks From Data.” 2020. Web. 11 May 2021.

Vancouver:

Wang Q. Learning Non-Impeding Noisy-AND Tree Model Based Bayesian Networks From Data. [Internet] [Masters thesis]. University of Guelph; 2020. [cited 2021 May 11]. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/17813.

Council of Science Editors:

Wang Q. Learning Non-Impeding Noisy-AND Tree Model Based Bayesian Networks From Data. [Masters Thesis]. University of Guelph; 2020. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/17813

University of Maryland

19. Shakarian, Paulo. Spatio-Temporal Reasoning About Agent Behavior.

Degree: Computer Science, 2011, University of Maryland

URL: http://hdl.handle.net/1903/11673

► There are many applications where we wish to reason about spatio-temporal aspects of an agent's behavior. This dissertation examines several facets of this type of…
(more)

Subjects/Keywords: Computer Science; Abductive inference; Geospatial reasoning; Logic programming; Probabilistic reasoning

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Shakarian, P. (2011). Spatio-Temporal Reasoning About Agent Behavior. (Thesis). University of Maryland. Retrieved from http://hdl.handle.net/1903/11673

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Shakarian, Paulo. “Spatio-Temporal Reasoning About Agent Behavior.” 2011. Thesis, University of Maryland. Accessed May 11, 2021. http://hdl.handle.net/1903/11673.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Shakarian, Paulo. “Spatio-Temporal Reasoning About Agent Behavior.” 2011. Web. 11 May 2021.

Vancouver:

Shakarian P. Spatio-Temporal Reasoning About Agent Behavior. [Internet] [Thesis]. University of Maryland; 2011. [cited 2021 May 11]. Available from: http://hdl.handle.net/1903/11673.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Shakarian P. Spatio-Temporal Reasoning About Agent Behavior. [Thesis]. University of Maryland; 2011. Available from: http://hdl.handle.net/1903/11673

Not specified: Masters Thesis or Doctoral Dissertation

King Abdullah University of Science and Technology

20.
Elkantassi, Soumaya.
* Probabilistic* Forecast of Wind Power Generation by Stochastic Differential Equation Models.

Degree: Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, 2017, King Abdullah University of Science and Technology

URL: http://hdl.handle.net/10754/623461

► Reliable forecasting of wind power generation is crucial to optimal control of costs in generation of electricity with respect to the electricity demand. Here, we…
(more)

Subjects/Keywords: Indirect inference; wind power; probabilistic forecasting; model selection; sensitivity

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Elkantassi, S. (2017). Probabilistic Forecast of Wind Power Generation by Stochastic Differential Equation Models. (Thesis). King Abdullah University of Science and Technology. Retrieved from http://hdl.handle.net/10754/623461

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Elkantassi, Soumaya. “Probabilistic Forecast of Wind Power Generation by Stochastic Differential Equation Models.” 2017. Thesis, King Abdullah University of Science and Technology. Accessed May 11, 2021. http://hdl.handle.net/10754/623461.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Elkantassi, Soumaya. “Probabilistic Forecast of Wind Power Generation by Stochastic Differential Equation Models.” 2017. Web. 11 May 2021.

Vancouver:

Elkantassi S. Probabilistic Forecast of Wind Power Generation by Stochastic Differential Equation Models. [Internet] [Thesis]. King Abdullah University of Science and Technology; 2017. [cited 2021 May 11]. Available from: http://hdl.handle.net/10754/623461.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Elkantassi S. Probabilistic Forecast of Wind Power Generation by Stochastic Differential Equation Models. [Thesis]. King Abdullah University of Science and Technology; 2017. Available from: http://hdl.handle.net/10754/623461

Not specified: Masters Thesis or Doctoral Dissertation

University of Edinburgh

21.
Szymczak, Marcin.
Programming language semantics as a foundation for Bayesian * inference*.

Degree: PhD, 2018, University of Edinburgh

URL: http://hdl.handle.net/1842/28993

► Bayesian modelling, in which our prior belief about the distribution on model parameters is updated by observed data, is a popular approach to statistical data…
(more)

Subjects/Keywords: probabilistic programming paradigm; Bayesian modelling; Tabular; probabilistic languages; inference algorithms; Markov chain Monte Carlo; Metropolis-Hastings

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Szymczak, M. (2018). Programming language semantics as a foundation for Bayesian inference. (Doctoral Dissertation). University of Edinburgh. Retrieved from http://hdl.handle.net/1842/28993

Chicago Manual of Style (16^{th} Edition):

Szymczak, Marcin. “Programming language semantics as a foundation for Bayesian inference.” 2018. Doctoral Dissertation, University of Edinburgh. Accessed May 11, 2021. http://hdl.handle.net/1842/28993.

MLA Handbook (7^{th} Edition):

Szymczak, Marcin. “Programming language semantics as a foundation for Bayesian inference.” 2018. Web. 11 May 2021.

Vancouver:

Szymczak M. Programming language semantics as a foundation for Bayesian inference. [Internet] [Doctoral dissertation]. University of Edinburgh; 2018. [cited 2021 May 11]. Available from: http://hdl.handle.net/1842/28993.

Council of Science Editors:

Szymczak M. Programming language semantics as a foundation for Bayesian inference. [Doctoral Dissertation]. University of Edinburgh; 2018. Available from: http://hdl.handle.net/1842/28993

University of Maryland

22. Kanagal Shamanna, Bhargav. Scalable Statistical Modeling and Query Processing over Large Scale Uncertain Databases.

Degree: Computer Science, 2011, University of Maryland

URL: http://hdl.handle.net/1903/11977

► The past decade has witnessed a large number of novel applications that generate imprecise, uncertain and incomplete data. Examples include monitoring infrastructures such as RFIDs,…
(more)

Subjects/Keywords: Computer science; Statistics; Computer engineering; Graphical Models; Index; Probabilistic Databases; Probabilistic Inference; Query Processing; Statistical Modeling

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Kanagal Shamanna, B. (2011). Scalable Statistical Modeling and Query Processing over Large Scale Uncertain Databases. (Thesis). University of Maryland. Retrieved from http://hdl.handle.net/1903/11977

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Kanagal Shamanna, Bhargav. “Scalable Statistical Modeling and Query Processing over Large Scale Uncertain Databases.” 2011. Thesis, University of Maryland. Accessed May 11, 2021. http://hdl.handle.net/1903/11977.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Kanagal Shamanna, Bhargav. “Scalable Statistical Modeling and Query Processing over Large Scale Uncertain Databases.” 2011. Web. 11 May 2021.

Vancouver:

Kanagal Shamanna B. Scalable Statistical Modeling and Query Processing over Large Scale Uncertain Databases. [Internet] [Thesis]. University of Maryland; 2011. [cited 2021 May 11]. Available from: http://hdl.handle.net/1903/11977.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Kanagal Shamanna B. Scalable Statistical Modeling and Query Processing over Large Scale Uncertain Databases. [Thesis]. University of Maryland; 2011. Available from: http://hdl.handle.net/1903/11977

Not specified: Masters Thesis or Doctoral Dissertation

University of California – Irvine

23.
Liu, Qiang.
Reasoning and Decisions in *Probabilistic* Graphical Models - A Unified Framework.

Degree: Computer Science, 2014, University of California – Irvine

URL: http://www.escholarship.org/uc/item/92p8w3xb

► *Probabilistic* graphical models such as Markov random fields, Bayesian networks and decision networks (a.k.a. influence diagrams) provide powerful frameworks for representing and exploiting dependence structures…
(more)

Subjects/Keywords: Computer science; Artificial intelligence; Approximate inference; Belief propagation; Graphical models; Influence diagrams; Probabilistic inference; Variational methods

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Liu, Q. (2014). Reasoning and Decisions in Probabilistic Graphical Models - A Unified Framework. (Thesis). University of California – Irvine. Retrieved from http://www.escholarship.org/uc/item/92p8w3xb

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Liu, Qiang. “Reasoning and Decisions in Probabilistic Graphical Models - A Unified Framework.” 2014. Thesis, University of California – Irvine. Accessed May 11, 2021. http://www.escholarship.org/uc/item/92p8w3xb.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Liu, Qiang. “Reasoning and Decisions in Probabilistic Graphical Models - A Unified Framework.” 2014. Web. 11 May 2021.

Vancouver:

Liu Q. Reasoning and Decisions in Probabilistic Graphical Models - A Unified Framework. [Internet] [Thesis]. University of California – Irvine; 2014. [cited 2021 May 11]. Available from: http://www.escholarship.org/uc/item/92p8w3xb.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Liu Q. Reasoning and Decisions in Probabilistic Graphical Models - A Unified Framework. [Thesis]. University of California – Irvine; 2014. Available from: http://www.escholarship.org/uc/item/92p8w3xb

Not specified: Masters Thesis or Doctoral Dissertation

Purdue University

24.
Ness, Robert D. O.
Bayesian causal *inference* of cell signal transduction from proteomics experiments.

Degree: PhD, Statistics, 2016, Purdue University

URL: https://docs.lib.purdue.edu/open_access_dissertations/979

► Cell signal transduction describes how a cell senses and processes signals from the environment using networks of interacting proteins. In computational systems biology, investigators…
(more)

Subjects/Keywords: Biological sciences; Active learning; Bayesian inference; Causal inference; Cell signal transduction; Graphical model; Probabilistic programming; Bioinformatics

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Ness, R. D. O. (2016). Bayesian causal inference of cell signal transduction from proteomics experiments. (Doctoral Dissertation). Purdue University. Retrieved from https://docs.lib.purdue.edu/open_access_dissertations/979

Chicago Manual of Style (16^{th} Edition):

Ness, Robert D O. “Bayesian causal inference of cell signal transduction from proteomics experiments.” 2016. Doctoral Dissertation, Purdue University. Accessed May 11, 2021. https://docs.lib.purdue.edu/open_access_dissertations/979.

MLA Handbook (7^{th} Edition):

Ness, Robert D O. “Bayesian causal inference of cell signal transduction from proteomics experiments.” 2016. Web. 11 May 2021.

Vancouver:

Ness RDO. Bayesian causal inference of cell signal transduction from proteomics experiments. [Internet] [Doctoral dissertation]. Purdue University; 2016. [cited 2021 May 11]. Available from: https://docs.lib.purdue.edu/open_access_dissertations/979.

Council of Science Editors:

Ness RDO. Bayesian causal inference of cell signal transduction from proteomics experiments. [Doctoral Dissertation]. Purdue University; 2016. Available from: https://docs.lib.purdue.edu/open_access_dissertations/979

University of California – Berkeley

25.
Chatterjee, Shaunak.
Efficient *inference* algorithms for near-deterministic systems.

Degree: Electrical Engineering & Computer Sciences, 2013, University of California – Berkeley

URL: http://www.escholarship.org/uc/item/0g63029f

► This thesis addresses the problem of performing *probabilistic* *inference* in stochastic systems where the probability mass is far from uniformly distributed among all possible outcomes.…
(more)

Subjects/Keywords: Artificial intelligence; Eigenanalysis; Graphical models; MCMC; Near-deterministic systems; Probabilistic inference; Viterbi

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Chatterjee, S. (2013). Efficient inference algorithms for near-deterministic systems. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/0g63029f

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Chatterjee, Shaunak. “Efficient inference algorithms for near-deterministic systems.” 2013. Thesis, University of California – Berkeley. Accessed May 11, 2021. http://www.escholarship.org/uc/item/0g63029f.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Chatterjee, Shaunak. “Efficient inference algorithms for near-deterministic systems.” 2013. Web. 11 May 2021.

Vancouver:

Chatterjee S. Efficient inference algorithms for near-deterministic systems. [Internet] [Thesis]. University of California – Berkeley; 2013. [cited 2021 May 11]. Available from: http://www.escholarship.org/uc/item/0g63029f.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Chatterjee S. Efficient inference algorithms for near-deterministic systems. [Thesis]. University of California – Berkeley; 2013. Available from: http://www.escholarship.org/uc/item/0g63029f

Not specified: Masters Thesis or Doctoral Dissertation

University of California – Irvine

26.
Lee, Junkyu.
Compiling *Probabilistic* Conformant Planning into Mixed Dynamic Bayesian Network.

Degree: Computer Science, 2014, University of California – Irvine

URL: http://www.escholarship.org/uc/item/9xk0s6jb

► *Probabilistic* conformant planning is a task of finding a plan that achieves the goal without sensing, where the outcome of an action is *probabilistic* and…
(more)

Subjects/Keywords: Computer science; Artificial intelligence; Conformant Planning; Dynamic Bayesian Network; Graphical Model; Probabilistic Inference

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Lee, J. (2014). Compiling Probabilistic Conformant Planning into Mixed Dynamic Bayesian Network. (Thesis). University of California – Irvine. Retrieved from http://www.escholarship.org/uc/item/9xk0s6jb

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Lee, Junkyu. “Compiling Probabilistic Conformant Planning into Mixed Dynamic Bayesian Network.” 2014. Thesis, University of California – Irvine. Accessed May 11, 2021. http://www.escholarship.org/uc/item/9xk0s6jb.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Lee, Junkyu. “Compiling Probabilistic Conformant Planning into Mixed Dynamic Bayesian Network.” 2014. Web. 11 May 2021.

Vancouver:

Lee J. Compiling Probabilistic Conformant Planning into Mixed Dynamic Bayesian Network. [Internet] [Thesis]. University of California – Irvine; 2014. [cited 2021 May 11]. Available from: http://www.escholarship.org/uc/item/9xk0s6jb.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lee J. Compiling Probabilistic Conformant Planning into Mixed Dynamic Bayesian Network. [Thesis]. University of California – Irvine; 2014. Available from: http://www.escholarship.org/uc/item/9xk0s6jb

Not specified: Masters Thesis or Doctoral Dissertation

Brigham Young University

27.
Seaman, Iris Rubi.
* Probabilistic* Programming for Theory of Mind for Autonomous Decision Making.

Degree: MS, 2018, Brigham Young University

URL: https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=7826&context=etd

► As autonomous agents (such as unmanned aerial vehicles, or UAVs) become more ubiquitous, they are being used for increasingly complex tasks. Eventually, they will have…
(more)

Subjects/Keywords: probabilistic programming; autonomous; decision making; planning; nested inference; Theory of Mind; Computer Sciences

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Seaman, I. R. (2018). Probabilistic Programming for Theory of Mind for Autonomous Decision Making. (Masters Thesis). Brigham Young University. Retrieved from https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=7826&context=etd

Chicago Manual of Style (16^{th} Edition):

Seaman, Iris Rubi. “Probabilistic Programming for Theory of Mind for Autonomous Decision Making.” 2018. Masters Thesis, Brigham Young University. Accessed May 11, 2021. https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=7826&context=etd.

MLA Handbook (7^{th} Edition):

Seaman, Iris Rubi. “Probabilistic Programming for Theory of Mind for Autonomous Decision Making.” 2018. Web. 11 May 2021.

Vancouver:

Seaman IR. Probabilistic Programming for Theory of Mind for Autonomous Decision Making. [Internet] [Masters thesis]. Brigham Young University; 2018. [cited 2021 May 11]. Available from: https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=7826&context=etd.

Council of Science Editors:

Seaman IR. Probabilistic Programming for Theory of Mind for Autonomous Decision Making. [Masters Thesis]. Brigham Young University; 2018. Available from: https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=7826&context=etd

University of Manchester

28. Yao, Shuaiyu. Investigation into rule-based inferential modelling and prediction with application in healthcare.

Degree: PhD, 2019, University of Manchester

URL: https://www.research.manchester.ac.uk/portal/en/theses/investigation-into-rulebased-inferential-modelling-and-prediction-with-application-in-healthcare(e73ae49a-887e-4305-8973-728c1bbe251e).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.779642

► Sepsis is a serious disease that can cause death. It is important to evaluate patients' sepsis risk during diagnostic decisions within the early stages after…
(more)

Subjects/Keywords: Evidential Reasoning; Data Discretization; Statistical Analysis; Probabilistic Inference; Machine Learning; Prediction; Decision Making

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Yao, S. (2019). Investigation into rule-based inferential modelling and prediction with application in healthcare. (Doctoral Dissertation). University of Manchester. Retrieved from https://www.research.manchester.ac.uk/portal/en/theses/investigation-into-rulebased-inferential-modelling-and-prediction-with-application-in-healthcare(e73ae49a-887e-4305-8973-728c1bbe251e).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.779642

Chicago Manual of Style (16^{th} Edition):

Yao, Shuaiyu. “Investigation into rule-based inferential modelling and prediction with application in healthcare.” 2019. Doctoral Dissertation, University of Manchester. Accessed May 11, 2021. https://www.research.manchester.ac.uk/portal/en/theses/investigation-into-rulebased-inferential-modelling-and-prediction-with-application-in-healthcare(e73ae49a-887e-4305-8973-728c1bbe251e).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.779642.

MLA Handbook (7^{th} Edition):

Yao, Shuaiyu. “Investigation into rule-based inferential modelling and prediction with application in healthcare.” 2019. Web. 11 May 2021.

Vancouver:

Yao S. Investigation into rule-based inferential modelling and prediction with application in healthcare. [Internet] [Doctoral dissertation]. University of Manchester; 2019. [cited 2021 May 11]. Available from: https://www.research.manchester.ac.uk/portal/en/theses/investigation-into-rulebased-inferential-modelling-and-prediction-with-application-in-healthcare(e73ae49a-887e-4305-8973-728c1bbe251e).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.779642.

Council of Science Editors:

Yao S. Investigation into rule-based inferential modelling and prediction with application in healthcare. [Doctoral Dissertation]. University of Manchester; 2019. Available from: https://www.research.manchester.ac.uk/portal/en/theses/investigation-into-rulebased-inferential-modelling-and-prediction-with-application-in-healthcare(e73ae49a-887e-4305-8973-728c1bbe251e).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.779642

University of Edinburgh

29. Acerbi, Luigi. Complex internal representations in sensorimotor decision making : a Bayesian investigation.

Degree: PhD, 2015, University of Edinburgh

URL: http://hdl.handle.net/1842/16233

► The past twenty years have seen a successful formalization of the idea that perception is a form of *probabilistic* *inference*. Bayesian Decision Theory (BDT) provides…
(more)

Subjects/Keywords: 612.8; Bayesian brain; probabilistic inference; psychophysics; sensorimotor estimation; sensorimotor learning; time perception

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Acerbi, L. (2015). Complex internal representations in sensorimotor decision making : a Bayesian investigation. (Doctoral Dissertation). University of Edinburgh. Retrieved from http://hdl.handle.net/1842/16233

Chicago Manual of Style (16^{th} Edition):

Acerbi, Luigi. “Complex internal representations in sensorimotor decision making : a Bayesian investigation.” 2015. Doctoral Dissertation, University of Edinburgh. Accessed May 11, 2021. http://hdl.handle.net/1842/16233.

MLA Handbook (7^{th} Edition):

Acerbi, Luigi. “Complex internal representations in sensorimotor decision making : a Bayesian investigation.” 2015. Web. 11 May 2021.

Vancouver:

Acerbi L. Complex internal representations in sensorimotor decision making : a Bayesian investigation. [Internet] [Doctoral dissertation]. University of Edinburgh; 2015. [cited 2021 May 11]. Available from: http://hdl.handle.net/1842/16233.

Council of Science Editors:

Acerbi L. Complex internal representations in sensorimotor decision making : a Bayesian investigation. [Doctoral Dissertation]. University of Edinburgh; 2015. Available from: http://hdl.handle.net/1842/16233

University of Illinois – Urbana-Champaign

30. Bean, Andrew J. Message passing algorithms - methods and applications.

Degree: PhD, Electrical & Computer Engr, 2015, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/78397

► Algorithms on graphs are used extensively in many applications and research areas. Such applications include machine learning, artificial intelligence, communications, image processing, state tracking, sensor…
(more)

Subjects/Keywords: probabilistic graphical models; approximate inference; universal portfolios; message passing; analog to digital converters (ADC)

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Bean, A. J. (2015). Message passing algorithms - methods and applications. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/78397

Chicago Manual of Style (16^{th} Edition):

Bean, Andrew J. “Message passing algorithms - methods and applications.” 2015. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed May 11, 2021. http://hdl.handle.net/2142/78397.

MLA Handbook (7^{th} Edition):

Bean, Andrew J. “Message passing algorithms - methods and applications.” 2015. Web. 11 May 2021.

Vancouver:

Bean AJ. Message passing algorithms - methods and applications. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2015. [cited 2021 May 11]. Available from: http://hdl.handle.net/2142/78397.

Council of Science Editors:

Bean AJ. Message passing algorithms - methods and applications. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2015. Available from: http://hdl.handle.net/2142/78397