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You searched for subject:(Bayesian Inference). Showing records 1 – 30 of 823 total matches.

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1. Doraiswamy, Srikrishna. Characterization of Nonlinear Material Response in the Presence of Large Uncertainties ??? A Bayesian Approach.

Degree: 2013, Texas Digital Library

 The aim of the current work is to develop a Bayesian approach to model and simulate the behavior of materials with nonlinear mechanical response in… (more)

Subjects/Keywords: Bayesian inference

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

APA (6th Edition):

Doraiswamy, S. (2013). Characterization of Nonlinear Material Response in the Presence of Large Uncertainties ??? A Bayesian Approach. (Thesis). Texas Digital Library. Retrieved from http://hdl.handle.net/1969; http://hdl.handle.net/2249.1/66804

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):

Doraiswamy, Srikrishna. “Characterization of Nonlinear Material Response in the Presence of Large Uncertainties ??? A Bayesian Approach.” 2013. Thesis, Texas Digital Library. Accessed March 04, 2021. http://hdl.handle.net/1969; http://hdl.handle.net/2249.1/66804.

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

MLA Handbook (7th Edition):

Doraiswamy, Srikrishna. “Characterization of Nonlinear Material Response in the Presence of Large Uncertainties ??? A Bayesian Approach.” 2013. Web. 04 Mar 2021.

Vancouver:

Doraiswamy S. Characterization of Nonlinear Material Response in the Presence of Large Uncertainties ??? A Bayesian Approach. [Internet] [Thesis]. Texas Digital Library; 2013. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/1969; http://hdl.handle.net/2249.1/66804.

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

Council of Science Editors:

Doraiswamy S. Characterization of Nonlinear Material Response in the Presence of Large Uncertainties ??? A Bayesian Approach. [Thesis]. Texas Digital Library; 2013. Available from: http://hdl.handle.net/1969; http://hdl.handle.net/2249.1/66804

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

2. Sugden, Lauren Alpert. Structure, Variation, and Reproducibility: Bayesian inference in problems arising from the study of RNA and an RNA-binding protein.

Degree: PhD, Applied Mathematics, 2014, Brown University

 Far from being solely a passive messenger between DNA and protein, RNA is a complex molecule involved in regulation at many levels. While the best-known… (more)

Subjects/Keywords: Bayesian inference

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APA (6th Edition):

Sugden, L. A. (2014). Structure, Variation, and Reproducibility: Bayesian inference in problems arising from the study of RNA and an RNA-binding protein. (Doctoral Dissertation). Brown University. Retrieved from https://repository.library.brown.edu/studio/item/bdr:386269/

Chicago Manual of Style (16th Edition):

Sugden, Lauren Alpert. “Structure, Variation, and Reproducibility: Bayesian inference in problems arising from the study of RNA and an RNA-binding protein.” 2014. Doctoral Dissertation, Brown University. Accessed March 04, 2021. https://repository.library.brown.edu/studio/item/bdr:386269/.

MLA Handbook (7th Edition):

Sugden, Lauren Alpert. “Structure, Variation, and Reproducibility: Bayesian inference in problems arising from the study of RNA and an RNA-binding protein.” 2014. Web. 04 Mar 2021.

Vancouver:

Sugden LA. Structure, Variation, and Reproducibility: Bayesian inference in problems arising from the study of RNA and an RNA-binding protein. [Internet] [Doctoral dissertation]. Brown University; 2014. [cited 2021 Mar 04]. Available from: https://repository.library.brown.edu/studio/item/bdr:386269/.

Council of Science Editors:

Sugden LA. Structure, Variation, and Reproducibility: Bayesian inference in problems arising from the study of RNA and an RNA-binding protein. [Doctoral Dissertation]. Brown University; 2014. Available from: https://repository.library.brown.edu/studio/item/bdr:386269/

3. Lin, Luan. Bayesian Inference and High-D space Characterization with application in Paleoclimatology and Biology.

Degree: PhD, Applied Mathematics, 2012, Brown University

 This thesis is a mathematical study of paleoclimatology and computational biology. Part I gives an introduction and overview of this dissertation. Part II presents the… (more)

Subjects/Keywords: bayesian inference

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APA (6th Edition):

Lin, L. (2012). Bayesian Inference and High-D space Characterization with application in Paleoclimatology and Biology. (Doctoral Dissertation). Brown University. Retrieved from https://repository.library.brown.edu/studio/item/bdr:297525/

Chicago Manual of Style (16th Edition):

Lin, Luan. “Bayesian Inference and High-D space Characterization with application in Paleoclimatology and Biology.” 2012. Doctoral Dissertation, Brown University. Accessed March 04, 2021. https://repository.library.brown.edu/studio/item/bdr:297525/.

MLA Handbook (7th Edition):

Lin, Luan. “Bayesian Inference and High-D space Characterization with application in Paleoclimatology and Biology.” 2012. Web. 04 Mar 2021.

Vancouver:

Lin L. Bayesian Inference and High-D space Characterization with application in Paleoclimatology and Biology. [Internet] [Doctoral dissertation]. Brown University; 2012. [cited 2021 Mar 04]. Available from: https://repository.library.brown.edu/studio/item/bdr:297525/.

Council of Science Editors:

Lin L. Bayesian Inference and High-D space Characterization with application in Paleoclimatology and Biology. [Doctoral Dissertation]. Brown University; 2012. Available from: https://repository.library.brown.edu/studio/item/bdr:297525/


Louisiana State University

4. Bhale, Ishan Singh. Bayesian inference application to burglary detection.

Degree: MSCS, Computer Sciences, 2012, Louisiana State University

 Real time motion tracking is very important for video analytics. But very little research has been done in identifying the top-level plans behind the atomic… (more)

Subjects/Keywords: Bayesian Inference

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APA (6th Edition):

Bhale, I. S. (2012). Bayesian inference application to burglary detection. (Masters Thesis). Louisiana State University. Retrieved from etd-01222013-155000 ; https://digitalcommons.lsu.edu/gradschool_theses/2382

Chicago Manual of Style (16th Edition):

Bhale, Ishan Singh. “Bayesian inference application to burglary detection.” 2012. Masters Thesis, Louisiana State University. Accessed March 04, 2021. etd-01222013-155000 ; https://digitalcommons.lsu.edu/gradschool_theses/2382.

MLA Handbook (7th Edition):

Bhale, Ishan Singh. “Bayesian inference application to burglary detection.” 2012. Web. 04 Mar 2021.

Vancouver:

Bhale IS. Bayesian inference application to burglary detection. [Internet] [Masters thesis]. Louisiana State University; 2012. [cited 2021 Mar 04]. Available from: etd-01222013-155000 ; https://digitalcommons.lsu.edu/gradschool_theses/2382.

Council of Science Editors:

Bhale IS. Bayesian inference application to burglary detection. [Masters Thesis]. Louisiana State University; 2012. Available from: etd-01222013-155000 ; https://digitalcommons.lsu.edu/gradschool_theses/2382


University of Adelaide

5. Gray, Caitlin. On the application of Bayesian inference to network estimation problems.

Degree: 2020, University of Adelaide

 Interconnected network structures play a crucial role in many aspects of our lives. Understanding these networks and the dynamic processes that can propagate over them… (more)

Subjects/Keywords: Network inference; information cascades; Bayesian inference

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APA (6th Edition):

Gray, C. (2020). On the application of Bayesian inference to network estimation problems. (Thesis). University of Adelaide. Retrieved from http://hdl.handle.net/2440/129332

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):

Gray, Caitlin. “On the application of Bayesian inference to network estimation problems.” 2020. Thesis, University of Adelaide. Accessed March 04, 2021. http://hdl.handle.net/2440/129332.

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

MLA Handbook (7th Edition):

Gray, Caitlin. “On the application of Bayesian inference to network estimation problems.” 2020. Web. 04 Mar 2021.

Vancouver:

Gray C. On the application of Bayesian inference to network estimation problems. [Internet] [Thesis]. University of Adelaide; 2020. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/2440/129332.

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

Council of Science Editors:

Gray C. On the application of Bayesian inference to network estimation problems. [Thesis]. University of Adelaide; 2020. Available from: http://hdl.handle.net/2440/129332

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


Texas A&M University

6. Das, Roneet. Probabilistic Slope Stability Assessment of Submarine and Slides by the Use of Bayesian Inference.

Degree: MS, Civil Engineering, 2016, Texas A&M University

 Estimates of probability of slope failure based on Monte-Carlo methods depend upon the state of evidence on the slope stability model parameters. The Bayesian framework… (more)

Subjects/Keywords: Slope Stability; Bayesian Inference

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APA (6th Edition):

Das, R. (2016). Probabilistic Slope Stability Assessment of Submarine and Slides by the Use of Bayesian Inference. (Masters Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/157070

Chicago Manual of Style (16th Edition):

Das, Roneet. “Probabilistic Slope Stability Assessment of Submarine and Slides by the Use of Bayesian Inference.” 2016. Masters Thesis, Texas A&M University. Accessed March 04, 2021. http://hdl.handle.net/1969.1/157070.

MLA Handbook (7th Edition):

Das, Roneet. “Probabilistic Slope Stability Assessment of Submarine and Slides by the Use of Bayesian Inference.” 2016. Web. 04 Mar 2021.

Vancouver:

Das R. Probabilistic Slope Stability Assessment of Submarine and Slides by the Use of Bayesian Inference. [Internet] [Masters thesis]. Texas A&M University; 2016. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/1969.1/157070.

Council of Science Editors:

Das R. Probabilistic Slope Stability Assessment of Submarine and Slides by the Use of Bayesian Inference. [Masters Thesis]. Texas A&M University; 2016. Available from: http://hdl.handle.net/1969.1/157070


University of Otago

7. Holmes, Tom. Exploring GPS Signal Data and Position Estimation Through Bayesian Inference .

Degree: 2011, University of Otago

 A new approach to position determination using the Global Positioning System (GPS) has been developed where post processing of ultra-short sequences of captured GPS satellite… (more)

Subjects/Keywords: GPS; Bayesian; Animal Tracking; Inference

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APA (6th Edition):

Holmes, T. (2011). Exploring GPS Signal Data and Position Estimation Through Bayesian Inference . (Masters Thesis). University of Otago. Retrieved from http://hdl.handle.net/10523/2032

Chicago Manual of Style (16th Edition):

Holmes, Tom. “Exploring GPS Signal Data and Position Estimation Through Bayesian Inference .” 2011. Masters Thesis, University of Otago. Accessed March 04, 2021. http://hdl.handle.net/10523/2032.

MLA Handbook (7th Edition):

Holmes, Tom. “Exploring GPS Signal Data and Position Estimation Through Bayesian Inference .” 2011. Web. 04 Mar 2021.

Vancouver:

Holmes T. Exploring GPS Signal Data and Position Estimation Through Bayesian Inference . [Internet] [Masters thesis]. University of Otago; 2011. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/10523/2032.

Council of Science Editors:

Holmes T. Exploring GPS Signal Data and Position Estimation Through Bayesian Inference . [Masters Thesis]. University of Otago; 2011. Available from: http://hdl.handle.net/10523/2032


University of Miami

8. Abeyruwan, Saminda. PrOntoLearn: Unsupervised Lexico-Semantic Ontology Generation using Probabilistic Methods.

Degree: MS, Computer Science (Arts and Sciences), 2010, University of Miami

  An ontology is a formal, explicit specification of a shared conceptualization. Formalizing an ontology for a domain is a tedious and cumbersome process. It… (more)

Subjects/Keywords: An Ontology; Learning; Bayesian Inference

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APA (6th Edition):

Abeyruwan, S. (2010). PrOntoLearn: Unsupervised Lexico-Semantic Ontology Generation using Probabilistic Methods. (Thesis). University of Miami. Retrieved from https://scholarlyrepository.miami.edu/oa_theses/28

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):

Abeyruwan, Saminda. “PrOntoLearn: Unsupervised Lexico-Semantic Ontology Generation using Probabilistic Methods.” 2010. Thesis, University of Miami. Accessed March 04, 2021. https://scholarlyrepository.miami.edu/oa_theses/28.

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

MLA Handbook (7th Edition):

Abeyruwan, Saminda. “PrOntoLearn: Unsupervised Lexico-Semantic Ontology Generation using Probabilistic Methods.” 2010. Web. 04 Mar 2021.

Vancouver:

Abeyruwan S. PrOntoLearn: Unsupervised Lexico-Semantic Ontology Generation using Probabilistic Methods. [Internet] [Thesis]. University of Miami; 2010. [cited 2021 Mar 04]. Available from: https://scholarlyrepository.miami.edu/oa_theses/28.

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

Council of Science Editors:

Abeyruwan S. PrOntoLearn: Unsupervised Lexico-Semantic Ontology Generation using Probabilistic Methods. [Thesis]. University of Miami; 2010. Available from: https://scholarlyrepository.miami.edu/oa_theses/28

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


University of Cambridge

9. O'Keeffe, Jonathan. Mathematical models of the representation of faces in humans.

Degree: PhD, 2020, University of Cambridge

 The representation of faces is a crucial function of the human CNS, as demonstrated by the severe social difficulties experienced when people lack this ability… (more)

Subjects/Keywords: Faces; computer vision; Bayesian inference

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APA (6th Edition):

O'Keeffe, J. (2020). Mathematical models of the representation of faces in humans. (Doctoral Dissertation). University of Cambridge. Retrieved from https://doi.org/10.17863/CAM.46702 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.793085

Chicago Manual of Style (16th Edition):

O'Keeffe, Jonathan. “Mathematical models of the representation of faces in humans.” 2020. Doctoral Dissertation, University of Cambridge. Accessed March 04, 2021. https://doi.org/10.17863/CAM.46702 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.793085.

MLA Handbook (7th Edition):

O'Keeffe, Jonathan. “Mathematical models of the representation of faces in humans.” 2020. Web. 04 Mar 2021.

Vancouver:

O'Keeffe J. Mathematical models of the representation of faces in humans. [Internet] [Doctoral dissertation]. University of Cambridge; 2020. [cited 2021 Mar 04]. Available from: https://doi.org/10.17863/CAM.46702 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.793085.

Council of Science Editors:

O'Keeffe J. Mathematical models of the representation of faces in humans. [Doctoral Dissertation]. University of Cambridge; 2020. Available from: https://doi.org/10.17863/CAM.46702 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.793085


University of Toronto

10. Cheng, Vincent. Modeling the Climatology of Tornado Occurrence using Bayesian Inference.

Degree: PhD, 2014, University of Toronto

 Our mechanistic understanding of tornadic environments has significantly improved by the recent technological enhancements in the detection of tornadoes as well as the advances of… (more)

Subjects/Keywords: Bayesian Inference; Tornadoes; 0368

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APA (6th Edition):

Cheng, V. (2014). Modeling the Climatology of Tornado Occurrence using Bayesian Inference. (Doctoral Dissertation). University of Toronto. Retrieved from http://hdl.handle.net/1807/68452

Chicago Manual of Style (16th Edition):

Cheng, Vincent. “Modeling the Climatology of Tornado Occurrence using Bayesian Inference.” 2014. Doctoral Dissertation, University of Toronto. Accessed March 04, 2021. http://hdl.handle.net/1807/68452.

MLA Handbook (7th Edition):

Cheng, Vincent. “Modeling the Climatology of Tornado Occurrence using Bayesian Inference.” 2014. Web. 04 Mar 2021.

Vancouver:

Cheng V. Modeling the Climatology of Tornado Occurrence using Bayesian Inference. [Internet] [Doctoral dissertation]. University of Toronto; 2014. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/1807/68452.

Council of Science Editors:

Cheng V. Modeling the Climatology of Tornado Occurrence using Bayesian Inference. [Doctoral Dissertation]. University of Toronto; 2014. Available from: http://hdl.handle.net/1807/68452


University of Melbourne

11. 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 March 04, 2021. http://hdl.handle.net/11343/227106.

MLA Handbook (7th Edition):

Li, Yuan. “Probabilistic models for aggregating crowdsourced annotations.” 2019. Web. 04 Mar 2021.

Vancouver:

Li Y. Probabilistic models for aggregating crowdsourced annotations. [Internet] [Doctoral dissertation]. University of Melbourne; 2019. [cited 2021 Mar 04]. 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


University of Sydney

12. Abeywardana, Sachinthaka. Variational Inference in Generalised Hyperbolic and von Mises-Fisher Distributions .

Degree: 2015, University of Sydney

 Most real world data are skewed, contain more than the set of real numbers, and have higher probabilities of extreme events occurring compared to a… (more)

Subjects/Keywords: machine learning; Bayesian inference

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APA (6th Edition):

Abeywardana, S. (2015). Variational Inference in Generalised Hyperbolic and von Mises-Fisher Distributions . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/16504

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):

Abeywardana, Sachinthaka. “Variational Inference in Generalised Hyperbolic and von Mises-Fisher Distributions .” 2015. Thesis, University of Sydney. Accessed March 04, 2021. http://hdl.handle.net/2123/16504.

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

MLA Handbook (7th Edition):

Abeywardana, Sachinthaka. “Variational Inference in Generalised Hyperbolic and von Mises-Fisher Distributions .” 2015. Web. 04 Mar 2021.

Vancouver:

Abeywardana S. Variational Inference in Generalised Hyperbolic and von Mises-Fisher Distributions . [Internet] [Thesis]. University of Sydney; 2015. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/2123/16504.

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

Council of Science Editors:

Abeywardana S. Variational Inference in Generalised Hyperbolic and von Mises-Fisher Distributions . [Thesis]. University of Sydney; 2015. Available from: http://hdl.handle.net/2123/16504

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


University of Sydney

13. McCalman, Lachlan Robert. Function Embeddings for Multi-modal Bayesian Inference .

Degree: 2013, University of Sydney

 Tractable Bayesian inference is a fundamental challenge in robotics and machine learning. Standard approaches such as Gaussian process regression and Kalman filtering make strong Gaussianity… (more)

Subjects/Keywords: Statistics; Inference; Bayesian; Machine Learning

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APA (6th Edition):

McCalman, L. R. (2013). Function Embeddings for Multi-modal Bayesian Inference . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/12031

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):

McCalman, Lachlan Robert. “Function Embeddings for Multi-modal Bayesian Inference .” 2013. Thesis, University of Sydney. Accessed March 04, 2021. http://hdl.handle.net/2123/12031.

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

MLA Handbook (7th Edition):

McCalman, Lachlan Robert. “Function Embeddings for Multi-modal Bayesian Inference .” 2013. Web. 04 Mar 2021.

Vancouver:

McCalman LR. Function Embeddings for Multi-modal Bayesian Inference . [Internet] [Thesis]. University of Sydney; 2013. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/2123/12031.

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

Council of Science Editors:

McCalman LR. Function Embeddings for Multi-modal Bayesian Inference . [Thesis]. University of Sydney; 2013. Available from: http://hdl.handle.net/2123/12031

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


University of Alberta

14. Gonzalez, Ruben. Bayesian Methods for On-Line Gross Error Detection and Compensation.

Degree: MS, Department of Chemical and Materials Engineering, 2010, University of Alberta

 Data reconciliation and gross error detection are traditional methods toward detecting mass balance inconsistency within process instrument data. These methods use a static approach for… (more)

Subjects/Keywords: Gross Error Detection; Bayesian Inference; Data Reconciliation

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APA (6th Edition):

Gonzalez, R. (2010). Bayesian Methods for On-Line Gross Error Detection and Compensation. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/w6634487q

Chicago Manual of Style (16th Edition):

Gonzalez, Ruben. “Bayesian Methods for On-Line Gross Error Detection and Compensation.” 2010. Masters Thesis, University of Alberta. Accessed March 04, 2021. https://era.library.ualberta.ca/files/w6634487q.

MLA Handbook (7th Edition):

Gonzalez, Ruben. “Bayesian Methods for On-Line Gross Error Detection and Compensation.” 2010. Web. 04 Mar 2021.

Vancouver:

Gonzalez R. Bayesian Methods for On-Line Gross Error Detection and Compensation. [Internet] [Masters thesis]. University of Alberta; 2010. [cited 2021 Mar 04]. Available from: https://era.library.ualberta.ca/files/w6634487q.

Council of Science Editors:

Gonzalez R. Bayesian Methods for On-Line Gross Error Detection and Compensation. [Masters Thesis]. University of Alberta; 2010. Available from: https://era.library.ualberta.ca/files/w6634487q


Cornell University

15. Wan, Muting. Model-Based Classification With Applications To High-Dimensional Data In Bioinformatics.

Degree: PhD, Statistics, 2015, Cornell University

 In recent years, sparse classification problems have emerged in many fields of study. Finite mixture models have been developed to facilitate Bayesian inference where parameter… (more)

Subjects/Keywords: Bayesian inference; Linear mixed models; Bioinformatics

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APA (6th Edition):

Wan, M. (2015). Model-Based Classification With Applications To High-Dimensional Data In Bioinformatics. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/39389

Chicago Manual of Style (16th Edition):

Wan, Muting. “Model-Based Classification With Applications To High-Dimensional Data In Bioinformatics.” 2015. Doctoral Dissertation, Cornell University. Accessed March 04, 2021. http://hdl.handle.net/1813/39389.

MLA Handbook (7th Edition):

Wan, Muting. “Model-Based Classification With Applications To High-Dimensional Data In Bioinformatics.” 2015. Web. 04 Mar 2021.

Vancouver:

Wan M. Model-Based Classification With Applications To High-Dimensional Data In Bioinformatics. [Internet] [Doctoral dissertation]. Cornell University; 2015. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/1813/39389.

Council of Science Editors:

Wan M. Model-Based Classification With Applications To High-Dimensional Data In Bioinformatics. [Doctoral Dissertation]. Cornell University; 2015. Available from: http://hdl.handle.net/1813/39389


Penn State University

16. Schall, Megan Victoria. Comparative Bioenergetics of Two Lake Trout Morphotypes.

Degree: 2013, Penn State University

 Lake trout (Salvelinus namaycush) are deep water apex predators native to North America that inhabit glacially formed lakes, including the Laurentian Great Lakes. Lake trout… (more)

Subjects/Keywords: bioenergetics modeling; lake trout; Bayesian inference

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APA (6th Edition):

Schall, M. V. (2013). Comparative Bioenergetics of Two Lake Trout Morphotypes. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/18506

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):

Schall, Megan Victoria. “Comparative Bioenergetics of Two Lake Trout Morphotypes.” 2013. Thesis, Penn State University. Accessed March 04, 2021. https://submit-etda.libraries.psu.edu/catalog/18506.

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

MLA Handbook (7th Edition):

Schall, Megan Victoria. “Comparative Bioenergetics of Two Lake Trout Morphotypes.” 2013. Web. 04 Mar 2021.

Vancouver:

Schall MV. Comparative Bioenergetics of Two Lake Trout Morphotypes. [Internet] [Thesis]. Penn State University; 2013. [cited 2021 Mar 04]. Available from: https://submit-etda.libraries.psu.edu/catalog/18506.

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

Council of Science Editors:

Schall MV. Comparative Bioenergetics of Two Lake Trout Morphotypes. [Thesis]. Penn State University; 2013. Available from: https://submit-etda.libraries.psu.edu/catalog/18506

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


North Carolina State University

17. DiCasoli, Carl Matthew. Bayesian Regression Methods for Crossing Survival Curves.

Degree: PhD, Statistics, 2009, North Carolina State University

 In survival data analysis, the proportional hazards (PH), accelerated failure time (AFT), and proportional odds (PO) models are commonly used semiparametric models for the comparison… (more)

Subjects/Keywords: variational methods; Bayesian inference; survival analysis

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APA (6th Edition):

DiCasoli, C. M. (2009). Bayesian Regression Methods for Crossing Survival Curves. (Doctoral Dissertation). North Carolina State University. Retrieved from http://www.lib.ncsu.edu/resolver/1840.16/4743

Chicago Manual of Style (16th Edition):

DiCasoli, Carl Matthew. “Bayesian Regression Methods for Crossing Survival Curves.” 2009. Doctoral Dissertation, North Carolina State University. Accessed March 04, 2021. http://www.lib.ncsu.edu/resolver/1840.16/4743.

MLA Handbook (7th Edition):

DiCasoli, Carl Matthew. “Bayesian Regression Methods for Crossing Survival Curves.” 2009. Web. 04 Mar 2021.

Vancouver:

DiCasoli CM. Bayesian Regression Methods for Crossing Survival Curves. [Internet] [Doctoral dissertation]. North Carolina State University; 2009. [cited 2021 Mar 04]. Available from: http://www.lib.ncsu.edu/resolver/1840.16/4743.

Council of Science Editors:

DiCasoli CM. Bayesian Regression Methods for Crossing Survival Curves. [Doctoral Dissertation]. North Carolina State University; 2009. Available from: http://www.lib.ncsu.edu/resolver/1840.16/4743

18. Feldman, Naomi H. Interactions between word and speech sound categorization in language acquisition.

Degree: PhD, Cognitive Sciences, 2011, Brown University

 Infants learn to segment words from fluent speech during the same period as they learn native language phonetic categories, yet accounts of phonetic category acquisition… (more)

Subjects/Keywords: language acquisition; phonetic category learning; Bayesian inference

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

APA (6th Edition):

Feldman, N. H. (2011). Interactions between word and speech sound categorization in language acquisition. (Doctoral Dissertation). Brown University. Retrieved from https://repository.library.brown.edu/studio/item/bdr:11219/

Chicago Manual of Style (16th Edition):

Feldman, Naomi H. “Interactions between word and speech sound categorization in language acquisition.” 2011. Doctoral Dissertation, Brown University. Accessed March 04, 2021. https://repository.library.brown.edu/studio/item/bdr:11219/.

MLA Handbook (7th Edition):

Feldman, Naomi H. “Interactions between word and speech sound categorization in language acquisition.” 2011. Web. 04 Mar 2021.

Vancouver:

Feldman NH. Interactions between word and speech sound categorization in language acquisition. [Internet] [Doctoral dissertation]. Brown University; 2011. [cited 2021 Mar 04]. Available from: https://repository.library.brown.edu/studio/item/bdr:11219/.

Council of Science Editors:

Feldman NH. Interactions between word and speech sound categorization in language acquisition. [Doctoral Dissertation]. Brown University; 2011. Available from: https://repository.library.brown.edu/studio/item/bdr:11219/


Tampere University

19. Lu, Chien. An Improved Nearest Neighbor Based Entropy Estimator with Local Ellipsoid Correction and its Application to Evaluation of MCMC Posterior Samples .

Degree: 2018, Tampere University

 Entropy estimation is an important technique to summarize the uncertainty of a distribution underlying a set of samples. It ties to important research problems in… (more)

Subjects/Keywords: entropy estimation; nonparametric estimator; Bayesian inference

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APA (6th Edition):

Lu, C. (2018). An Improved Nearest Neighbor Based Entropy Estimator with Local Ellipsoid Correction and its Application to Evaluation of MCMC Posterior Samples . (Masters Thesis). Tampere University. Retrieved from https://trepo.tuni.fi/handle/10024/104066

Chicago Manual of Style (16th Edition):

Lu, Chien. “An Improved Nearest Neighbor Based Entropy Estimator with Local Ellipsoid Correction and its Application to Evaluation of MCMC Posterior Samples .” 2018. Masters Thesis, Tampere University. Accessed March 04, 2021. https://trepo.tuni.fi/handle/10024/104066.

MLA Handbook (7th Edition):

Lu, Chien. “An Improved Nearest Neighbor Based Entropy Estimator with Local Ellipsoid Correction and its Application to Evaluation of MCMC Posterior Samples .” 2018. Web. 04 Mar 2021.

Vancouver:

Lu C. An Improved Nearest Neighbor Based Entropy Estimator with Local Ellipsoid Correction and its Application to Evaluation of MCMC Posterior Samples . [Internet] [Masters thesis]. Tampere University; 2018. [cited 2021 Mar 04]. Available from: https://trepo.tuni.fi/handle/10024/104066.

Council of Science Editors:

Lu C. An Improved Nearest Neighbor Based Entropy Estimator with Local Ellipsoid Correction and its Application to Evaluation of MCMC Posterior Samples . [Masters Thesis]. Tampere University; 2018. Available from: https://trepo.tuni.fi/handle/10024/104066


Baylor University

20. Casement, Christopher James, 1987-. Graphical methods in prior elicitation.

Degree: PhD, Baylor University. Dept. of Statistical Sciences., 2017, Baylor University

 Prior elicitation is the process of quantifying an expert's belief in the form of a probability distribution on a parameter(s) to be used in a… (more)

Subjects/Keywords: Bayesian statistics. Prior elicitation. Graphical inference.

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APA (6th Edition):

Casement, Christopher James, 1. (2017). Graphical methods in prior elicitation. (Doctoral Dissertation). Baylor University. Retrieved from http://hdl.handle.net/2104/10111

Chicago Manual of Style (16th Edition):

Casement, Christopher James, 1987-. “Graphical methods in prior elicitation.” 2017. Doctoral Dissertation, Baylor University. Accessed March 04, 2021. http://hdl.handle.net/2104/10111.

MLA Handbook (7th Edition):

Casement, Christopher James, 1987-. “Graphical methods in prior elicitation.” 2017. Web. 04 Mar 2021.

Vancouver:

Casement, Christopher James 1. Graphical methods in prior elicitation. [Internet] [Doctoral dissertation]. Baylor University; 2017. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/2104/10111.

Council of Science Editors:

Casement, Christopher James 1. Graphical methods in prior elicitation. [Doctoral Dissertation]. Baylor University; 2017. Available from: http://hdl.handle.net/2104/10111


University of Houston

21. Bhardwaj, Manisha 1986-. Visual decision making in the presence of stimulus and measurement correlations.

Degree: PhD, Mathematics, 2013, University of Houston

 Our brains process sensory information to infer the state of the world. However, the input from our senses is noisy, which may lead to errors… (more)

Subjects/Keywords: Bayesian inference; Model comparison; Correlations; Target detection

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APA (6th Edition):

Bhardwaj, M. 1. (2013). Visual decision making in the presence of stimulus and measurement correlations. (Doctoral Dissertation). University of Houston. Retrieved from http://hdl.handle.net/10657/1239

Chicago Manual of Style (16th Edition):

Bhardwaj, Manisha 1986-. “Visual decision making in the presence of stimulus and measurement correlations.” 2013. Doctoral Dissertation, University of Houston. Accessed March 04, 2021. http://hdl.handle.net/10657/1239.

MLA Handbook (7th Edition):

Bhardwaj, Manisha 1986-. “Visual decision making in the presence of stimulus and measurement correlations.” 2013. Web. 04 Mar 2021.

Vancouver:

Bhardwaj M1. Visual decision making in the presence of stimulus and measurement correlations. [Internet] [Doctoral dissertation]. University of Houston; 2013. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/10657/1239.

Council of Science Editors:

Bhardwaj M1. Visual decision making in the presence of stimulus and measurement correlations. [Doctoral Dissertation]. University of Houston; 2013. Available from: http://hdl.handle.net/10657/1239


University of Cambridge

22. Graff, Philip B. Bayesian methods for gravitational waves and neural networks.

Degree: PhD, 2012, University of Cambridge

 Einstein’s general theory of relativity has withstood 100 years of testing and will soon be facing one of its toughest challenges. In a few years… (more)

Subjects/Keywords: 530; Bayesian inference; Gravitational waves; Machine learning

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APA (6th Edition):

Graff, P. B. (2012). Bayesian methods for gravitational waves and neural networks. (Doctoral Dissertation). University of Cambridge. Retrieved from https://doi.org/10.17863/CAM.16592 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.566190

Chicago Manual of Style (16th Edition):

Graff, Philip B. “Bayesian methods for gravitational waves and neural networks.” 2012. Doctoral Dissertation, University of Cambridge. Accessed March 04, 2021. https://doi.org/10.17863/CAM.16592 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.566190.

MLA Handbook (7th Edition):

Graff, Philip B. “Bayesian methods for gravitational waves and neural networks.” 2012. Web. 04 Mar 2021.

Vancouver:

Graff PB. Bayesian methods for gravitational waves and neural networks. [Internet] [Doctoral dissertation]. University of Cambridge; 2012. [cited 2021 Mar 04]. Available from: https://doi.org/10.17863/CAM.16592 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.566190.

Council of Science Editors:

Graff PB. Bayesian methods for gravitational waves and neural networks. [Doctoral Dissertation]. University of Cambridge; 2012. Available from: https://doi.org/10.17863/CAM.16592 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.566190


University of Cambridge

23. Jin, Yingyan. Supervised learning for back analysis of excavations in the observational method.

Degree: PhD, 2018, University of Cambridge

 In the past few decades, demand for construction in underground spaces has increased dramatically in urban areas with high population densities. However, the impact of… (more)

Subjects/Keywords: Bayesian inference; Excavations; Observational method; Back analysis

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APA (6th Edition):

Jin, Y. (2018). Supervised learning for back analysis of excavations in the observational method. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/275587

Chicago Manual of Style (16th Edition):

Jin, Yingyan. “Supervised learning for back analysis of excavations in the observational method.” 2018. Doctoral Dissertation, University of Cambridge. Accessed March 04, 2021. https://www.repository.cam.ac.uk/handle/1810/275587.

MLA Handbook (7th Edition):

Jin, Yingyan. “Supervised learning for back analysis of excavations in the observational method.” 2018. Web. 04 Mar 2021.

Vancouver:

Jin Y. Supervised learning for back analysis of excavations in the observational method. [Internet] [Doctoral dissertation]. University of Cambridge; 2018. [cited 2021 Mar 04]. Available from: https://www.repository.cam.ac.uk/handle/1810/275587.

Council of Science Editors:

Jin Y. Supervised learning for back analysis of excavations in the observational method. [Doctoral Dissertation]. University of Cambridge; 2018. Available from: https://www.repository.cam.ac.uk/handle/1810/275587

24. Graff, Philip B. Bayesian methods for gravitational waves and neural networks.

Degree: PhD, 2012, University of Cambridge

 Einstein’s general theory of relativity has withstood 100 years of testing and will soon be facing one of its toughest challenges. In a few years… (more)

Subjects/Keywords: Bayesian inference; Gravitational waves; Machine learning

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APA (6th Edition):

Graff, P. B. (2012). Bayesian methods for gravitational waves and neural networks. (Doctoral Dissertation). University of Cambridge. Retrieved from http://www.dspace.cam.ac.uk/handle/1810/244270https://www.repository.cam.ac.uk/bitstream/1810/244270/2/license.txt ; https://www.repository.cam.ac.uk/bitstream/1810/244270/3/license_url ; https://www.repository.cam.ac.uk/bitstream/1810/244270/4/license_text ; https://www.repository.cam.ac.uk/bitstream/1810/244270/5/license_rdf ; https://www.repository.cam.ac.uk/bitstream/1810/244270/8/thesis.pdf.txt ; https://www.repository.cam.ac.uk/bitstream/1810/244270/9/thesis.pdf.jpg

Chicago Manual of Style (16th Edition):

Graff, Philip B. “Bayesian methods for gravitational waves and neural networks.” 2012. Doctoral Dissertation, University of Cambridge. Accessed March 04, 2021. http://www.dspace.cam.ac.uk/handle/1810/244270https://www.repository.cam.ac.uk/bitstream/1810/244270/2/license.txt ; https://www.repository.cam.ac.uk/bitstream/1810/244270/3/license_url ; https://www.repository.cam.ac.uk/bitstream/1810/244270/4/license_text ; https://www.repository.cam.ac.uk/bitstream/1810/244270/5/license_rdf ; https://www.repository.cam.ac.uk/bitstream/1810/244270/8/thesis.pdf.txt ; https://www.repository.cam.ac.uk/bitstream/1810/244270/9/thesis.pdf.jpg.

MLA Handbook (7th Edition):

Graff, Philip B. “Bayesian methods for gravitational waves and neural networks.” 2012. Web. 04 Mar 2021.

Vancouver:

Graff PB. Bayesian methods for gravitational waves and neural networks. [Internet] [Doctoral dissertation]. University of Cambridge; 2012. [cited 2021 Mar 04]. Available from: http://www.dspace.cam.ac.uk/handle/1810/244270https://www.repository.cam.ac.uk/bitstream/1810/244270/2/license.txt ; https://www.repository.cam.ac.uk/bitstream/1810/244270/3/license_url ; https://www.repository.cam.ac.uk/bitstream/1810/244270/4/license_text ; https://www.repository.cam.ac.uk/bitstream/1810/244270/5/license_rdf ; https://www.repository.cam.ac.uk/bitstream/1810/244270/8/thesis.pdf.txt ; https://www.repository.cam.ac.uk/bitstream/1810/244270/9/thesis.pdf.jpg.

Council of Science Editors:

Graff PB. Bayesian methods for gravitational waves and neural networks. [Doctoral Dissertation]. University of Cambridge; 2012. Available from: http://www.dspace.cam.ac.uk/handle/1810/244270https://www.repository.cam.ac.uk/bitstream/1810/244270/2/license.txt ; https://www.repository.cam.ac.uk/bitstream/1810/244270/3/license_url ; https://www.repository.cam.ac.uk/bitstream/1810/244270/4/license_text ; https://www.repository.cam.ac.uk/bitstream/1810/244270/5/license_rdf ; https://www.repository.cam.ac.uk/bitstream/1810/244270/8/thesis.pdf.txt ; https://www.repository.cam.ac.uk/bitstream/1810/244270/9/thesis.pdf.jpg


University of Adelaide

25. Walker, James. Bayesian Inference and Model Selection for Partially-Observed, Continuous-Time, Stochastic Epidemic Models.

Degree: 2019, University of Adelaide

 Emerging infectious diseases are an ongoing threat to the health of populations around the world. In response, countries such as the USA, UK and Australia,… (more)

Subjects/Keywords: Epidemiology; Bayesian Inference; Model Selection; Stochastic Modelling

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APA (6th Edition):

Walker, J. (2019). Bayesian Inference and Model Selection for Partially-Observed, Continuous-Time, Stochastic Epidemic Models. (Thesis). University of Adelaide. Retrieved from http://hdl.handle.net/2440/124703

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):

Walker, James. “Bayesian Inference and Model Selection for Partially-Observed, Continuous-Time, Stochastic Epidemic Models.” 2019. Thesis, University of Adelaide. Accessed March 04, 2021. http://hdl.handle.net/2440/124703.

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

MLA Handbook (7th Edition):

Walker, James. “Bayesian Inference and Model Selection for Partially-Observed, Continuous-Time, Stochastic Epidemic Models.” 2019. Web. 04 Mar 2021.

Vancouver:

Walker J. Bayesian Inference and Model Selection for Partially-Observed, Continuous-Time, Stochastic Epidemic Models. [Internet] [Thesis]. University of Adelaide; 2019. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/2440/124703.

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

Council of Science Editors:

Walker J. Bayesian Inference and Model Selection for Partially-Observed, Continuous-Time, Stochastic Epidemic Models. [Thesis]. University of Adelaide; 2019. Available from: http://hdl.handle.net/2440/124703

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


Princeton University

26. Ranganath, Rajesh. Black Box Variational Inference: Scalable, Generic Bayesian Computation and its Applications .

Degree: PhD, 2017, Princeton University

 Probabilistic generative models are robust to noise, uncover unseen patterns, and make predictions about the future. These models have been used successfully to solve problems… (more)

Subjects/Keywords: Bayesian Statistics; Machine Learning; Variational Inference

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APA (6th Edition):

Ranganath, R. (2017). Black Box Variational Inference: Scalable, Generic Bayesian Computation and its Applications . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp01pr76f608w

Chicago Manual of Style (16th Edition):

Ranganath, Rajesh. “Black Box Variational Inference: Scalable, Generic Bayesian Computation and its Applications .” 2017. Doctoral Dissertation, Princeton University. Accessed March 04, 2021. http://arks.princeton.edu/ark:/88435/dsp01pr76f608w.

MLA Handbook (7th Edition):

Ranganath, Rajesh. “Black Box Variational Inference: Scalable, Generic Bayesian Computation and its Applications .” 2017. Web. 04 Mar 2021.

Vancouver:

Ranganath R. Black Box Variational Inference: Scalable, Generic Bayesian Computation and its Applications . [Internet] [Doctoral dissertation]. Princeton University; 2017. [cited 2021 Mar 04]. Available from: http://arks.princeton.edu/ark:/88435/dsp01pr76f608w.

Council of Science Editors:

Ranganath R. Black Box Variational Inference: Scalable, Generic Bayesian Computation and its Applications . [Doctoral Dissertation]. Princeton University; 2017. Available from: http://arks.princeton.edu/ark:/88435/dsp01pr76f608w


Universitat de Valencia

27. Martínez Minaya, Joaquín. Recent statistical advances and applications of species distribution modeling .

Degree: 2019, Universitat de Valencia

 En el mundo en que vivimos, producimos aproximadamente 2.5 quintillones de bytes de datos por día. Esta enorme cantidad de datos proviene de las redes… (more)

Subjects/Keywords: bayesian inference; inla; species distribution models; geostatistics

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APA (6th Edition):

Martínez Minaya, J. (2019). Recent statistical advances and applications of species distribution modeling . (Doctoral Dissertation). Universitat de Valencia. Retrieved from http://hdl.handle.net/10550/71315

Chicago Manual of Style (16th Edition):

Martínez Minaya, Joaquín. “Recent statistical advances and applications of species distribution modeling .” 2019. Doctoral Dissertation, Universitat de Valencia. Accessed March 04, 2021. http://hdl.handle.net/10550/71315.

MLA Handbook (7th Edition):

Martínez Minaya, Joaquín. “Recent statistical advances and applications of species distribution modeling .” 2019. Web. 04 Mar 2021.

Vancouver:

Martínez Minaya J. Recent statistical advances and applications of species distribution modeling . [Internet] [Doctoral dissertation]. Universitat de Valencia; 2019. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/10550/71315.

Council of Science Editors:

Martínez Minaya J. Recent statistical advances and applications of species distribution modeling . [Doctoral Dissertation]. Universitat de Valencia; 2019. Available from: http://hdl.handle.net/10550/71315


University of Sydney

28. Babbar, Sakshi. Inferring Anomalies from Data using Bayesian Networks .

Degree: 2013, University of Sydney

 Existing studies on data mining has largely focused on the design of measures and algorithms to identify outliers in large and high dimensional categorical and… (more)

Subjects/Keywords: Bayesian networks; outlier; causal inference; knowledge discovery

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APA (6th Edition):

Babbar, S. (2013). Inferring Anomalies from Data using Bayesian Networks . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/9371

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):

Babbar, Sakshi. “Inferring Anomalies from Data using Bayesian Networks .” 2013. Thesis, University of Sydney. Accessed March 04, 2021. http://hdl.handle.net/2123/9371.

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

MLA Handbook (7th Edition):

Babbar, Sakshi. “Inferring Anomalies from Data using Bayesian Networks .” 2013. Web. 04 Mar 2021.

Vancouver:

Babbar S. Inferring Anomalies from Data using Bayesian Networks . [Internet] [Thesis]. University of Sydney; 2013. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/2123/9371.

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

Council of Science Editors:

Babbar S. Inferring Anomalies from Data using Bayesian Networks . [Thesis]. University of Sydney; 2013. Available from: http://hdl.handle.net/2123/9371

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


University of Sydney

29. Tompkins, Anthony. Bayesian Spatio-Temporal Modelling with Fourier Features .

Degree: 2018, University of Sydney

 One of the most powerful machine learning techniques is \emph{Gaussian Processes} (GPs) which incur an O(N3) complexity in the number of data samples. In regression… (more)

Subjects/Keywords: Gaussian Processes; bayesian inference; periodicity; Timeseries

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APA (6th Edition):

Tompkins, A. (2018). Bayesian Spatio-Temporal Modelling with Fourier Features . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/21328

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):

Tompkins, Anthony. “Bayesian Spatio-Temporal Modelling with Fourier Features .” 2018. Thesis, University of Sydney. Accessed March 04, 2021. http://hdl.handle.net/2123/21328.

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

MLA Handbook (7th Edition):

Tompkins, Anthony. “Bayesian Spatio-Temporal Modelling with Fourier Features .” 2018. Web. 04 Mar 2021.

Vancouver:

Tompkins A. Bayesian Spatio-Temporal Modelling with Fourier Features . [Internet] [Thesis]. University of Sydney; 2018. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/2123/21328.

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

Council of Science Editors:

Tompkins A. Bayesian Spatio-Temporal Modelling with Fourier Features . [Thesis]. University of Sydney; 2018. Available from: http://hdl.handle.net/2123/21328

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


University of Texas – Austin

30. Zhang, Michael Minyi. Scalable inference for Bayesian non-parametrics.

Degree: PhD, Statistics, 2018, University of Texas – Austin

Bayesian non-parametric models, despite their theoretical elegance, face a serious computational burden that prevents their use in serious "big data'' scenarios. Furthermore, we cannot expect… (more)

Subjects/Keywords: Bayesian non-parametrics; Scalable inference; Machine learning

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APA (6th Edition):

Zhang, M. M. (2018). Scalable inference for Bayesian non-parametrics. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/65734

Chicago Manual of Style (16th Edition):

Zhang, Michael Minyi. “Scalable inference for Bayesian non-parametrics.” 2018. Doctoral Dissertation, University of Texas – Austin. Accessed March 04, 2021. http://hdl.handle.net/2152/65734.

MLA Handbook (7th Edition):

Zhang, Michael Minyi. “Scalable inference for Bayesian non-parametrics.” 2018. Web. 04 Mar 2021.

Vancouver:

Zhang MM. Scalable inference for Bayesian non-parametrics. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2018. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/2152/65734.

Council of Science Editors:

Zhang MM. Scalable inference for Bayesian non-parametrics. [Doctoral Dissertation]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/65734

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