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You searched for subject:(probabilistic inference). Showing records 1 – 30 of 78 total matches.

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Cornell University

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

Degree: 2016, Cornell University

 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 (6th 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 (16th 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 (7th 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

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

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

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):

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.

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

MLA Handbook (7th 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.

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

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


ETH Zürich

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

Degree: 2019, ETH Zürich

 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 (6th 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 (16th 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 (7th 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

 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 November 17, 2019. 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. 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.

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

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

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

MLA Handbook (7th 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

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 November 17, 2019. 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. 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

 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 (6th 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 (16th 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 (7th 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

 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

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

APA (6th Edition):

Hennig, P. (2011). Approximate inference in graphical models . (Thesis). University of Cambridge. Retrieved from http://www.dspace.cam.ac.uk/handle/1810/237251

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):

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.

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

MLA Handbook (7th 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.

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

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

 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/5681

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

MLA Handbook (7th 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

 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 (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 November 17, 2019. 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. 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.

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


George Mason University

11. 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 November 17, 2019. 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. 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.

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


University of Toronto

12. 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 November 17, 2019. http://hdl.handle.net/1807/72692.

MLA Handbook (7th 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

 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 (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 November 17, 2019. 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. 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.

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 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

 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 November 17, 2019. 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. 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.

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 Arizona

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

Degree: 2010, University of Arizona

 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 (6th 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 (16th 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 (7th 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

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

MLA Handbook (7th 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

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 (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 November 17, 2019. 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. 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.

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

18. 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 November 17, 2019. 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. 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

 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 (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 November 17, 2019. 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. 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.

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

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

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 November 17, 2019. 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. 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.

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


Duke University

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

Degree: 2017, Duke University

  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 (6th Edition):

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

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):

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.

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

MLA Handbook (7th 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.

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

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

  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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (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 November 17, 2019. http://hdl.handle.net/1842/16233.

MLA Handbook (7th 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 inference using the multivariate Generalised von Mises distribution.

Degree: PhD, 2018, University of Cambridge

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 (6th 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 (16th 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 (7th 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

 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 (6th 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

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):

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.

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

MLA Handbook (7th 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.

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

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

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

MLA Handbook (7th 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

 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

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

APA (6th 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

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):

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.

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

MLA Handbook (7th 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.

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

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

 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 November 17, 2019. 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. 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

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 (6th 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 (16th 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 (7th 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

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