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You searched for subject:(probabilistic inference). Showing records 1 – 30 of 106 total matches.

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University of Melbourne

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

Degree: 2019, University of Melbourne

 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 (6th 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 (16th 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 (7th 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

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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Rice University

4. Vasudeva Raju, Rajkumar. Inferring Implicit Inference.

Degree: PhD, Engineering, 2019, Rice University

 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 (6th Edition):

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

Chicago Manual of Style (16th Edition):

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

MLA Handbook (7th 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

 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 (6th 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 (16th 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 (7th 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

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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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 inference .

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

 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 (6th 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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

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.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th 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.

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

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

 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 (6th Edition):

Shi, J. (2018). A framework for integrating influence diagrams and POMDPs. (Doctoral Dissertation). Mississippi State University. Retrieved from http://sun.library.msstate.edu/ETD-db/theses/available/etd-03022018-153923/ ;

Chicago Manual of Style (16th Edition):

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

MLA Handbook (7th Edition):

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

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 (6th 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 (16th 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 (7th 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

 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 (6th Edition):

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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th 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.

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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Indiana University

13. Narayanan, Praveen. Verifiable and reusable conditioning .

Degree: 2019, Indiana University

 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 (6th Edition):

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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th 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.

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

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

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 (6th 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 (16th 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 (7th 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

 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 (6th 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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

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.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th 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.

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

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

 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 (6th 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 (16th 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 (7th 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

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

APA (6th 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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

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.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th 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.

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

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

 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

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APA (6th 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 (16th 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 (7th 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

 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

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

APA (6th Edition):

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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th 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.

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

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

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

APA (6th 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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

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.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th 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.

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

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

 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 (6th 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 (16th 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 (7th 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

 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

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APA (6th 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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

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.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th 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.

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

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

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 (6th 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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Liu, 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.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th 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.

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

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

  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 (6th 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 (16th 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 (7th 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

 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 (6th 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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

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.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th 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.

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

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

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 (6th 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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Lee, 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.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th 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.

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

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

 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

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APA (6th 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 (16th 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 (7th 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

 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

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

APA (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

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