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You searched for `subject:(probabilistic inference)`

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Showing records 1 – 30 of
78 total matches.

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- 2010 – 2014 (31)

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Cornell University

1. Wyffels, Kevin. Precision Tracking Of Extended Objects Via Non-Traditional Sensor Models .

Degree: 2016, Cornell University

URL: http://hdl.handle.net/1813/43659

► Inspired by human perception, novel research into the sub-domain of robotic perception, known as extended object tracking, is presented. This research is motivated by the…
(more)

Subjects/Keywords: Autonomous Vehicles; Extended Object Tracking; Probabilistic Inference

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

APA (6^{th} Edition):

Wyffels, K. (2016). Precision Tracking Of Extended Objects Via Non-Traditional Sensor Models . (Thesis). Cornell University. Retrieved from http://hdl.handle.net/1813/43659

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):

Wyffels, Kevin. “Precision Tracking Of Extended Objects Via Non-Traditional Sensor Models .” 2016. Thesis, Cornell University. Accessed November 17, 2019. http://hdl.handle.net/1813/43659.

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

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Wyffels, Kevin. “Precision Tracking Of Extended Objects Via Non-Traditional Sensor Models .” 2016. Web. 17 Nov 2019.

Vancouver:

Wyffels K. Precision Tracking Of Extended Objects Via Non-Traditional Sensor Models . [Internet] [Thesis]. Cornell University; 2016. [cited 2019 Nov 17]. Available from: http://hdl.handle.net/1813/43659.

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

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wyffels K. Precision Tracking Of Extended Objects Via Non-Traditional Sensor Models . [Thesis]. Cornell University; 2016. Available from: http://hdl.handle.net/1813/43659

Not specified: Masters Thesis or Doctoral Dissertation

Cornell University

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

Degree: 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 . (Thesis). Cornell University. Retrieved from http://hdl.handle.net/1813/44364

Not specified: Masters Thesis or Doctoral Dissertation

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. Thesis, Cornell University. Accessed November 17, 2019. http://hdl.handle.net/1813/44364.

Not specified: Masters Thesis or Doctoral Dissertation

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. 17 Nov 2019.

Vancouver:

Radecki P. Applied Probabilistic Inference: Model Estimation For Hvac Predictive Controls And All-Weather Perception For Autonomous Vehicles . [Internet] [Thesis]. Cornell University; 2016. [cited 2019 Nov 17]. Available from: http://hdl.handle.net/1813/44364.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

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

Not specified: Masters Thesis or Doctoral Dissertation

ETH Zürich

3.
Gotovos, Alkis.
Sampling from *Probabilistic* Submodular Models.

Degree: 2019, ETH Zürich

URL: http://hdl.handle.net/20.500.11850/333042

► Practical problems of discrete nature are very common in machine learning; application domains include computer vision (e.g., image segmentation), sequential decision making (e.g., active learning),…
(more)

Subjects/Keywords: Approximate inference; Probabilistic models; Sampling; Submodularity

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APA (6^{th} Edition):

Gotovos, A. (2019). Sampling from Probabilistic Submodular Models. (Doctoral Dissertation). ETH Zürich. Retrieved from http://hdl.handle.net/20.500.11850/333042

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

Gotovos, Alkis. “Sampling from Probabilistic Submodular Models.” 2019. Doctoral Dissertation, ETH Zürich. Accessed November 17, 2019. http://hdl.handle.net/20.500.11850/333042.

MLA Handbook (7^{th} Edition):

Gotovos, Alkis. “Sampling from Probabilistic Submodular Models.” 2019. Web. 17 Nov 2019.

Vancouver:

Gotovos A. Sampling from Probabilistic Submodular Models. [Internet] [Doctoral dissertation]. ETH Zürich; 2019. [cited 2019 Nov 17]. Available from: http://hdl.handle.net/20.500.11850/333042.

Council of Science Editors:

Gotovos A. Sampling from Probabilistic Submodular Models. [Doctoral Dissertation]. ETH Zürich; 2019. Available from: http://hdl.handle.net/20.500.11850/333042

Australian National University

4.
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

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 November 17, 2019. http://hdl.handle.net/1885/107386.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Mohasel Afshar, Hadi. “Probabilistic Inference in Piecewise Graphical Models .” 2016. Web. 17 Nov 2019.

Vancouver:

Mohasel Afshar H. Probabilistic Inference in Piecewise Graphical Models . [Internet] [Thesis]. Australian National University; 2016. [cited 2019 Nov 17]. Available from: http://hdl.handle.net/1885/107386.

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

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 (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 November 17, 2019. http://hdl.handle.net/1911/88182.

MLA Handbook (7^{th} Edition):

Vasudeva Raju, Rajkumar. “Inference by Reparameterization using Neural Population Codes.” 2015. Web. 17 Nov 2019.

Vancouver:

Vasudeva Raju R. Inference by Reparameterization using Neural Population Codes. [Internet] [Masters thesis]. Rice University; 2015. [cited 2019 Nov 17]. 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 November 17, 2019. 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. 17 Nov 2019.

Vancouver:

Burgwal MDvd. Treecost-based Preprocessing for Probabilistic Networks. [Internet] [Masters thesis]. Universiteit Utrecht; 2015. [cited 2019 Nov 17]. 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 Cambridge

7.
Hennig, Philipp.
Approximate *inference* in graphical models.

Degree: PhD, 2011, University of Cambridge

URL: https://www.repository.cam.ac.uk/handle/1810/237251 ; http://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://www.repository.cam.ac.uk/handle/1810/237251 ; http://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 November 17, 2019. https://www.repository.cam.ac.uk/handle/1810/237251 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.541854.

MLA Handbook (7^{th} Edition):

Hennig, Philipp. “Approximate inference in graphical models.” 2011. Web. 17 Nov 2019.

Vancouver:

Hennig P. Approximate inference in graphical models. [Internet] [Doctoral dissertation]. University of Cambridge; 2011. [cited 2019 Nov 17]. Available from: https://www.repository.cam.ac.uk/handle/1810/237251 ; http://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://www.repository.cam.ac.uk/handle/1810/237251 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.541854

University of Cambridge

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

Degree: 2011, University of Cambridge

URL: http://www.dspace.cam.ac.uk/handle/1810/237251

► 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: Applied mathematics; Computer science; Probability theory; Probabilistic inference; Graphical models; Approximate inference

Record Details Similar Records

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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 . (Thesis). University of Cambridge. Retrieved from http://www.dspace.cam.ac.uk/handle/1810/237251

Not specified: Masters Thesis or Doctoral Dissertation

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

Hennig, Philipp. “Approximate inference in graphical models .” 2011. Thesis, University of Cambridge. Accessed November 17, 2019. http://www.dspace.cam.ac.uk/handle/1810/237251.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Hennig, Philipp. “Approximate inference in graphical models .” 2011. Web. 17 Nov 2019.

Vancouver:

Hennig P. Approximate inference in graphical models . [Internet] [Thesis]. University of Cambridge; 2011. [cited 2019 Nov 17]. Available from: http://www.dspace.cam.ac.uk/handle/1810/237251.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Hennig P. Approximate inference in graphical models . [Thesis]. University of Cambridge; 2011. Available from: http://www.dspace.cam.ac.uk/handle/1810/237251

Not specified: Masters Thesis or Doctoral Dissertation

University of Southern California

9.
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/5681

► 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/5681

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 November 17, 2019. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/373594/rec/5681.

MLA Handbook (7^{th} Edition):

Ma, Nam. “Scalable exact inference in probabilistic graphical models on multi-core platforms.” 2014. Web. 17 Nov 2019.

Vancouver:

Ma N. Scalable exact inference in probabilistic graphical models on multi-core platforms. [Internet] [Doctoral dissertation]. University of Southern California; 2014. [cited 2019 Nov 17]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/373594/rec/5681.

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/5681

King Abdullah University of Science and Technology

10.
Elkantassi, Soumaya.
* Probabilistic* Forecast of Wind Power Generation by Stochastic Differential Equation Models.

Degree: 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

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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 November 17, 2019. 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. 17 Nov 2019.

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 2019 Nov 17]. 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

George Mason University

11.
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 (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 November 17, 2019. 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. 17 Nov 2019.

Vancouver:

Sun W. Efficient Inference For Hybrid Bayesian Networks . [Internet] [Thesis]. George Mason University; 2007. [cited 2019 Nov 17]. 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

University of Toronto

12.
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

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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 November 17, 2019. 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. 17 Nov 2019.

Vancouver:

Dhoot A. Wind Farm Layout Optimization Using Approximate Inference in Graphical Models. [Internet] [Masters thesis]. University of Toronto; 2016. [cited 2019 Nov 17]. 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

University of Waterloo

13. 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

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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 November 17, 2019. 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. 17 Nov 2019.

Vancouver:

Salmon R. On the relationship between satisfiability and partially observable Markov decision processes. [Internet] [Thesis]. University of Waterloo; 2018. [cited 2019 Nov 17]. 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 Illinois – Urbana-Champaign

14.
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 November 17, 2019. 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. 17 Nov 2019.

Vancouver:

Kim M. Probabilistic models based on experimental observations using sparse bayes methodology. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2014. [cited 2019 Nov 17]. 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 Arizona

15. Gabbur, Prasad. Machine Learning Methods for Microarray Data Analysis .

Degree: 2010, University of Arizona

URL: http://hdl.handle.net/10150/195829

► Microarrays emerged in the 1990s as a consequence of the efforts to speed up the process of drug discovery. They revolutionized molecular biological research by…
(more)

Subjects/Keywords: Bayesian Inference; Gene Expression; Gene Ontology; Machine Learning; Microarray; Probabilistic Modeling

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

APA (6^{th} Edition):

Gabbur, P. (2010). Machine Learning Methods for Microarray Data Analysis . (Doctoral Dissertation). University of Arizona. Retrieved from http://hdl.handle.net/10150/195829

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

Gabbur, Prasad. “Machine Learning Methods for Microarray Data Analysis .” 2010. Doctoral Dissertation, University of Arizona. Accessed November 17, 2019. http://hdl.handle.net/10150/195829.

MLA Handbook (7^{th} Edition):

Gabbur, Prasad. “Machine Learning Methods for Microarray Data Analysis .” 2010. Web. 17 Nov 2019.

Vancouver:

Gabbur P. Machine Learning Methods for Microarray Data Analysis . [Internet] [Doctoral dissertation]. University of Arizona; 2010. [cited 2019 Nov 17]. Available from: http://hdl.handle.net/10150/195829.

Council of Science Editors:

Gabbur P. Machine Learning Methods for Microarray Data Analysis . [Doctoral Dissertation]. University of Arizona; 2010. Available from: http://hdl.handle.net/10150/195829

University of Edinburgh

16.
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

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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 November 17, 2019. http://hdl.handle.net/1842/28993.

MLA Handbook (7^{th} Edition):

Szymczak, Marcin. “Programming language semantics as a foundation for Bayesian inference.” 2018. Web. 17 Nov 2019.

Vancouver:

Szymczak M. Programming language semantics as a foundation for Bayesian inference. [Internet] [Doctoral dissertation]. University of Edinburgh; 2018. [cited 2019 Nov 17]. 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 California – Irvine

17.
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

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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 November 17, 2019. 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. 17 Nov 2019.

Vancouver:

Liu Q. Reasoning and Decisions in Probabilistic Graphical Models - A Unified Framework. [Internet] [Thesis]. University of California – Irvine; 2014. [cited 2019 Nov 17]. 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

18.
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

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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 November 17, 2019. 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. 17 Nov 2019.

Vancouver:

Ness RDO. Bayesian causal inference of cell signal transduction from proteomics experiments. [Internet] [Doctoral dissertation]. Purdue University; 2016. [cited 2019 Nov 17]. 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

19.
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

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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 November 17, 2019. 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. 17 Nov 2019.

Vancouver:

Chatterjee S. Efficient inference algorithms for near-deterministic systems. [Internet] [Thesis]. University of California – Berkeley; 2013. [cited 2019 Nov 17]. 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

20.
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

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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 November 17, 2019. 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. 17 Nov 2019.

Vancouver:

Lee J. Compiling Probabilistic Conformant Planning into Mixed Dynamic Bayesian Network. [Internet] [Thesis]. University of California – Irvine; 2014. [cited 2019 Nov 17]. 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

Duke University

21. Oh, Hanna. Cognitive and Neural Mechanisms of Adaptive Satisficing Decision Making .

Degree: 2017, Duke University

URL: http://hdl.handle.net/10161/16276

► Much of our real-life decision making is bounded by uncertain information, limitations in cognitive resources, and a lack of time to allocate to the…
(more)

Subjects/Keywords: Cognitive psychology; Neurosciences; bounded rationality; decision-making; heuristics; multi-cue integration; probabilistic inference; satisficing

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

APA (6^{th} Edition):

Oh, H. (2017). Cognitive and Neural Mechanisms of Adaptive Satisficing Decision Making . (Thesis). Duke University. Retrieved from http://hdl.handle.net/10161/16276

Not specified: Masters Thesis or Doctoral Dissertation

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

Oh, Hanna. “Cognitive and Neural Mechanisms of Adaptive Satisficing Decision Making .” 2017. Thesis, Duke University. Accessed November 17, 2019. http://hdl.handle.net/10161/16276.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Oh, Hanna. “Cognitive and Neural Mechanisms of Adaptive Satisficing Decision Making .” 2017. Web. 17 Nov 2019.

Vancouver:

Oh H. Cognitive and Neural Mechanisms of Adaptive Satisficing Decision Making . [Internet] [Thesis]. Duke University; 2017. [cited 2019 Nov 17]. Available from: http://hdl.handle.net/10161/16276.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Oh H. Cognitive and Neural Mechanisms of Adaptive Satisficing Decision Making . [Thesis]. Duke University; 2017. Available from: http://hdl.handle.net/10161/16276

Not specified: Masters Thesis or Doctoral Dissertation

Georgia State University

22. He, Zaobo. Privacy Preserving Data Publishing.

Degree: PhD, Computer Science, 2018, Georgia State University

URL: https://scholarworks.gsu.edu/cs_diss/141

► Recent years have witnessed increasing interest among researchers in protecting individual privacy in the big data era, involving social media, genomics, and Internet of…
(more)

Subjects/Keywords: Inference Attack; Data Sanitization; Differential Privacy; SNP-Trait Association; Belief Propagation; Probabilistic Graphical Model

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

APA (6^{th} Edition):

He, Z. (2018). Privacy Preserving Data Publishing. (Doctoral Dissertation). Georgia State University. Retrieved from https://scholarworks.gsu.edu/cs_diss/141

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

He, Zaobo. “Privacy Preserving Data Publishing.” 2018. Doctoral Dissertation, Georgia State University. Accessed November 17, 2019. https://scholarworks.gsu.edu/cs_diss/141.

MLA Handbook (7^{th} Edition):

He, Zaobo. “Privacy Preserving Data Publishing.” 2018. Web. 17 Nov 2019.

Vancouver:

He Z. Privacy Preserving Data Publishing. [Internet] [Doctoral dissertation]. Georgia State University; 2018. [cited 2019 Nov 17]. Available from: https://scholarworks.gsu.edu/cs_diss/141.

Council of Science Editors:

He Z. Privacy Preserving Data Publishing. [Doctoral Dissertation]. Georgia State University; 2018. Available from: https://scholarworks.gsu.edu/cs_diss/141

Virginia Tech

23. Vasavada, Yash M. An Iterative Confidence Passing Approach for Parameter Estimation and Its Applications to MIMO Systems.

Degree: PhD, Electrical and Computer Engineering, 2012, Virginia Tech

URL: http://hdl.handle.net/10919/28192

► This dissertation proposes an iterative confidence passing (ICP) approach for parameter estimation. The dissertation describes three different algorithms that follow from this ICP approach. These…
(more)

Subjects/Keywords: Probabilistic Inference; Beamforming; Optimum Diversity Combining; MMSE; Least Squares; Bayesian Belief Theory; Iterative Estimation

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

APA (6^{th} Edition):

Vasavada, Y. M. (2012). An Iterative Confidence Passing Approach for Parameter Estimation and Its Applications to MIMO Systems. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/28192

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

Vasavada, Yash M. “An Iterative Confidence Passing Approach for Parameter Estimation and Its Applications to MIMO Systems.” 2012. Doctoral Dissertation, Virginia Tech. Accessed November 17, 2019. http://hdl.handle.net/10919/28192.

MLA Handbook (7^{th} Edition):

Vasavada, Yash M. “An Iterative Confidence Passing Approach for Parameter Estimation and Its Applications to MIMO Systems.” 2012. Web. 17 Nov 2019.

Vancouver:

Vasavada YM. An Iterative Confidence Passing Approach for Parameter Estimation and Its Applications to MIMO Systems. [Internet] [Doctoral dissertation]. Virginia Tech; 2012. [cited 2019 Nov 17]. Available from: http://hdl.handle.net/10919/28192.

Council of Science Editors:

Vasavada YM. An Iterative Confidence Passing Approach for Parameter Estimation and Its Applications to MIMO Systems. [Doctoral Dissertation]. Virginia Tech; 2012. Available from: http://hdl.handle.net/10919/28192

University of Edinburgh

24. 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

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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 November 17, 2019. 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. 17 Nov 2019.

Vancouver:

Acerbi L. Complex internal representations in sensorimotor decision making : a Bayesian investigation. [Internet] [Doctoral dissertation]. University of Edinburgh; 2015. [cited 2019 Nov 17]. 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 Cambridge

25.
Wu Navarro, Alexandre Khae.
* Probabilistic* machine learning for circular statistics : models and

Degree: PhD, 2018, University of Cambridge

URL: https://www.repository.cam.ac.uk/handle/1810/279067 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.753478

► *Probabilistic* machine learning and circular statistics—the branch of statistics concerned with data as angles and directions—are two research communities that have grown mostly in isolation…
(more)

Subjects/Keywords: Machine Learning; Circular Statistics; von Mises distribution; Gaussian Processes; Probabilistic models; Approximate Inference

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

APA (6^{th} Edition):

Wu Navarro, A. K. (2018). Probabilistic machine learning for circular statistics : models and inference using the multivariate Generalised von Mises distribution. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/279067 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.753478

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

Wu Navarro, Alexandre Khae. “Probabilistic machine learning for circular statistics : models and inference using the multivariate Generalised von Mises distribution.” 2018. Doctoral Dissertation, University of Cambridge. Accessed November 17, 2019. https://www.repository.cam.ac.uk/handle/1810/279067 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.753478.

MLA Handbook (7^{th} Edition):

Wu Navarro, Alexandre Khae. “Probabilistic machine learning for circular statistics : models and inference using the multivariate Generalised von Mises distribution.” 2018. Web. 17 Nov 2019.

Vancouver:

Wu Navarro AK. Probabilistic machine learning for circular statistics : models and inference using the multivariate Generalised von Mises distribution. [Internet] [Doctoral dissertation]. University of Cambridge; 2018. [cited 2019 Nov 17]. Available from: https://www.repository.cam.ac.uk/handle/1810/279067 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.753478.

Council of Science Editors:

Wu Navarro AK. Probabilistic machine learning for circular statistics : models and inference using the multivariate Generalised von Mises distribution. [Doctoral Dissertation]. University of Cambridge; 2018. Available from: https://www.repository.cam.ac.uk/handle/1810/279067 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.753478

University of California – Santa Cruz

26.
Tomkins, Sabina.
* Probabilistic* Methods for Data-Driven Social Good.

Degree: Technology and Information Management, 2018, University of California – Santa Cruz

URL: http://www.escholarship.org/uc/item/1z70t8vs

► Computational techniques have much to offer in addressing questions of societal significance. Many such question can be framed as prediction problems, and approached with data-driven…
(more)

Subjects/Keywords: Artificial intelligence; Collective Inference; Education; Malicious Behavior; Probabilistic Graphical Models; Social Networks; Spatio-temporal

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

APA (6^{th} Edition):

Tomkins, S. (2018). Probabilistic Methods for Data-Driven Social Good. (Thesis). University of California – Santa Cruz. Retrieved from http://www.escholarship.org/uc/item/1z70t8vs

Not specified: Masters Thesis or Doctoral Dissertation

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

Tomkins, Sabina. “Probabilistic Methods for Data-Driven Social Good.” 2018. Thesis, University of California – Santa Cruz. Accessed November 17, 2019. http://www.escholarship.org/uc/item/1z70t8vs.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Tomkins, Sabina. “Probabilistic Methods for Data-Driven Social Good.” 2018. Web. 17 Nov 2019.

Vancouver:

Tomkins S. Probabilistic Methods for Data-Driven Social Good. [Internet] [Thesis]. University of California – Santa Cruz; 2018. [cited 2019 Nov 17]. Available from: http://www.escholarship.org/uc/item/1z70t8vs.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Tomkins S. Probabilistic Methods for Data-Driven Social Good. [Thesis]. University of California – Santa Cruz; 2018. Available from: http://www.escholarship.org/uc/item/1z70t8vs

Not specified: Masters Thesis or Doctoral Dissertation

University of Illinois – Urbana-Champaign

27. 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)

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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 November 17, 2019. http://hdl.handle.net/2142/78397.

MLA Handbook (7^{th} Edition):

Bean, Andrew J. “Message passing algorithms - methods and applications.” 2015. Web. 17 Nov 2019.

Vancouver:

Bean AJ. Message passing algorithms - methods and applications. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2015. [cited 2019 Nov 17]. 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

University of Illinois – Urbana-Champaign

28. Guo, Ying. Rationality or irrationality of preferences? A quantitative test of intransitive decision heuristics.

Degree: MS, Psychology, 2018, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/101046

► In this paper, I present a comprehensive analysis of two decision heuristics that permit intransitive preferences: the lexicographic semiorder model and the similarity model. I…
(more)

Subjects/Keywords: Transitivity of preference; Probabilistic model; Order-Constrained inference; lexicographic semiorder; similarity model; linear order

Record Details Similar Records

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

APA (6^{th} Edition):

Guo, Y. (2018). Rationality or irrationality of preferences? A quantitative test of intransitive decision heuristics. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/101046

Not specified: Masters Thesis or Doctoral Dissertation

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

Guo, Ying. “Rationality or irrationality of preferences? A quantitative test of intransitive decision heuristics.” 2018. Thesis, University of Illinois – Urbana-Champaign. Accessed November 17, 2019. http://hdl.handle.net/2142/101046.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Guo, Ying. “Rationality or irrationality of preferences? A quantitative test of intransitive decision heuristics.” 2018. Web. 17 Nov 2019.

Vancouver:

Guo Y. Rationality or irrationality of preferences? A quantitative test of intransitive decision heuristics. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2018. [cited 2019 Nov 17]. Available from: http://hdl.handle.net/2142/101046.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Guo Y. Rationality or irrationality of preferences? A quantitative test of intransitive decision heuristics. [Thesis]. University of Illinois – Urbana-Champaign; 2018. Available from: http://hdl.handle.net/2142/101046

Not specified: Masters Thesis or Doctoral Dissertation

Brigham Young University

29.
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 November 17, 2019. 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. 17 Nov 2019.

Vancouver:

Seaman IR. Probabilistic Programming for Theory of Mind for Autonomous Decision Making. [Internet] [Masters thesis]. Brigham Young University; 2018. [cited 2019 Nov 17]. 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 Southern California

30.
Xia, Yinglong.
Exploration of parallelism for *probabilistic* graphical
models.

Degree: PhD, Computer Science, 2010, University of Southern California

URL: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/420474/rec/2645

► *Probabilistic* graphical models such as Bayesian networks and junction trees are widely used to compactly represent joint probability distributions. They have found applications in a…
(more)

Subjects/Keywords: parallel computing; parallel algorithm; probabilistic graphical model; exact inference; multicore processor; scheduler

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

APA (6^{th} Edition):

Xia, Y. (2010). Exploration of parallelism for probabilistic graphical models. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/420474/rec/2645

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

Xia, Yinglong. “Exploration of parallelism for probabilistic graphical models.” 2010. Doctoral Dissertation, University of Southern California. Accessed November 17, 2019. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/420474/rec/2645.

MLA Handbook (7^{th} Edition):

Xia, Yinglong. “Exploration of parallelism for probabilistic graphical models.” 2010. Web. 17 Nov 2019.

Vancouver:

Xia Y. Exploration of parallelism for probabilistic graphical models. [Internet] [Doctoral dissertation]. University of Southern California; 2010. [cited 2019 Nov 17]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/420474/rec/2645.

Council of Science Editors:

Xia Y. Exploration of parallelism for probabilistic graphical models. [Doctoral Dissertation]. University of Southern California; 2010. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/420474/rec/2645