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You searched for subject:(Graphical modeling Statistics ). Showing records 1 – 30 of 38139 total matches.

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Hong Kong University of Science and Technology

1. Zhao, Yuqi MATH. High dimensional graphical model for categorical variables.

Degree: 2017, Hong Kong University of Science and Technology

 We propose a graphical model associated with categorical variables and study the problem of structure learning for this model. The model is a natural generalization… (more)

Subjects/Keywords: Graphical modeling (Statistics); Categories (Mathematics)

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APA (6th Edition):

Zhao, Y. M. (2017). High dimensional graphical model for categorical variables. (Thesis). Hong Kong University of Science and Technology. Retrieved from https://doi.org/10.14711/thesis-991012564766703412 ; http://repository.ust.hk/ir/bitstream/1783.1-91181/1/th_redirect.html

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):

Zhao, Yuqi MATH. “High dimensional graphical model for categorical variables.” 2017. Thesis, Hong Kong University of Science and Technology. Accessed November 17, 2019. https://doi.org/10.14711/thesis-991012564766703412 ; http://repository.ust.hk/ir/bitstream/1783.1-91181/1/th_redirect.html.

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

MLA Handbook (7th Edition):

Zhao, Yuqi MATH. “High dimensional graphical model for categorical variables.” 2017. Web. 17 Nov 2019.

Vancouver:

Zhao YM. High dimensional graphical model for categorical variables. [Internet] [Thesis]. Hong Kong University of Science and Technology; 2017. [cited 2019 Nov 17]. Available from: https://doi.org/10.14711/thesis-991012564766703412 ; http://repository.ust.hk/ir/bitstream/1783.1-91181/1/th_redirect.html.

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

Council of Science Editors:

Zhao YM. High dimensional graphical model for categorical variables. [Thesis]. Hong Kong University of Science and Technology; 2017. Available from: https://doi.org/10.14711/thesis-991012564766703412 ; http://repository.ust.hk/ir/bitstream/1783.1-91181/1/th_redirect.html

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


Montana State University

2. Sturlaugson, Liessman Eric. Extensions to modeling and inference in continuous time Bayesian networks.

Degree: College of Engineering, 2014, Montana State University

 The continuous time Bayesian network (CTBN) enables reasoning about complex systems in continuous time by representing a system as a factored, finite-state, continuous-time Markov process.… (more)

Subjects/Keywords: Markov processes.; Computational complexity.; Graphical modeling (Statistics).

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APA (6th Edition):

Sturlaugson, L. E. (2014). Extensions to modeling and inference in continuous time Bayesian networks. (Thesis). Montana State University. Retrieved from https://scholarworks.montana.edu/xmlui/handle/1/9368

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):

Sturlaugson, Liessman Eric. “Extensions to modeling and inference in continuous time Bayesian networks.” 2014. Thesis, Montana State University. Accessed November 17, 2019. https://scholarworks.montana.edu/xmlui/handle/1/9368.

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

MLA Handbook (7th Edition):

Sturlaugson, Liessman Eric. “Extensions to modeling and inference in continuous time Bayesian networks.” 2014. Web. 17 Nov 2019.

Vancouver:

Sturlaugson LE. Extensions to modeling and inference in continuous time Bayesian networks. [Internet] [Thesis]. Montana State University; 2014. [cited 2019 Nov 17]. Available from: https://scholarworks.montana.edu/xmlui/handle/1/9368.

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

Council of Science Editors:

Sturlaugson LE. Extensions to modeling and inference in continuous time Bayesian networks. [Thesis]. Montana State University; 2014. Available from: https://scholarworks.montana.edu/xmlui/handle/1/9368

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


Massey University

3. Fitch, Anne Marie. Computationally tractable fitting of graphical models : the cost and benefits of decomposable Bayesian and penalized likelihood approaches.

Degree: PhD, Statistics, 2012, Massey University

 Gaussian graphical models are a useful tool for eliciting information about relationships in data with a multivariate normal distribution. In the rst part of this… (more)

Subjects/Keywords: Graphical modeling (Statistics); Bayesian statistical decision theory

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APA (6th Edition):

Fitch, A. M. (2012). Computationally tractable fitting of graphical models : the cost and benefits of decomposable Bayesian and penalized likelihood approaches. (Doctoral Dissertation). Massey University. Retrieved from http://hdl.handle.net/10179/3659

Chicago Manual of Style (16th Edition):

Fitch, Anne Marie. “Computationally tractable fitting of graphical models : the cost and benefits of decomposable Bayesian and penalized likelihood approaches.” 2012. Doctoral Dissertation, Massey University. Accessed November 17, 2019. http://hdl.handle.net/10179/3659.

MLA Handbook (7th Edition):

Fitch, Anne Marie. “Computationally tractable fitting of graphical models : the cost and benefits of decomposable Bayesian and penalized likelihood approaches.” 2012. Web. 17 Nov 2019.

Vancouver:

Fitch AM. Computationally tractable fitting of graphical models : the cost and benefits of decomposable Bayesian and penalized likelihood approaches. [Internet] [Doctoral dissertation]. Massey University; 2012. [cited 2019 Nov 17]. Available from: http://hdl.handle.net/10179/3659.

Council of Science Editors:

Fitch AM. Computationally tractable fitting of graphical models : the cost and benefits of decomposable Bayesian and penalized likelihood approaches. [Doctoral Dissertation]. Massey University; 2012. Available from: http://hdl.handle.net/10179/3659


Boston University

4. Kang, Xinyu. Statistical methods for topology inference, denoising, and bootstrapping in networks.

Degree: PhD, Mathematics & Statistics, 2018, Boston University

 Quite often, the data we observe can be effectively represented using graphs. The underlying structure of the resulting graph, however, might contain noise and does… (more)

Subjects/Keywords: Statistics; Network; Graphical model; Multiscale modeling

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APA (6th Edition):

Kang, X. (2018). Statistical methods for topology inference, denoising, and bootstrapping in networks. (Doctoral Dissertation). Boston University. Retrieved from http://hdl.handle.net/2144/33117

Chicago Manual of Style (16th Edition):

Kang, Xinyu. “Statistical methods for topology inference, denoising, and bootstrapping in networks.” 2018. Doctoral Dissertation, Boston University. Accessed November 17, 2019. http://hdl.handle.net/2144/33117.

MLA Handbook (7th Edition):

Kang, Xinyu. “Statistical methods for topology inference, denoising, and bootstrapping in networks.” 2018. Web. 17 Nov 2019.

Vancouver:

Kang X. Statistical methods for topology inference, denoising, and bootstrapping in networks. [Internet] [Doctoral dissertation]. Boston University; 2018. [cited 2019 Nov 17]. Available from: http://hdl.handle.net/2144/33117.

Council of Science Editors:

Kang X. Statistical methods for topology inference, denoising, and bootstrapping in networks. [Doctoral Dissertation]. Boston University; 2018. Available from: http://hdl.handle.net/2144/33117


Montana State University

5. Fortier, Nathan Lee. Inference and learning in Bayesian networks using overlapping swarm intelligence.

Degree: College of Engineering, 2015, Montana State University

 While Bayesian networks provide a useful tool for reasoning under uncertainty, learning the structure of these networks and performing inference over them is NP-Hard. We… (more)

Subjects/Keywords: Artificial intelligence.; Graphical modeling (Statistics).; Algorithms.

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APA (6th Edition):

Fortier, N. L. (2015). Inference and learning in Bayesian networks using overlapping swarm intelligence. (Thesis). Montana State University. Retrieved from https://scholarworks.montana.edu/xmlui/handle/1/10134

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):

Fortier, Nathan Lee. “Inference and learning in Bayesian networks using overlapping swarm intelligence.” 2015. Thesis, Montana State University. Accessed November 17, 2019. https://scholarworks.montana.edu/xmlui/handle/1/10134.

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

MLA Handbook (7th Edition):

Fortier, Nathan Lee. “Inference and learning in Bayesian networks using overlapping swarm intelligence.” 2015. Web. 17 Nov 2019.

Vancouver:

Fortier NL. Inference and learning in Bayesian networks using overlapping swarm intelligence. [Internet] [Thesis]. Montana State University; 2015. [cited 2019 Nov 17]. Available from: https://scholarworks.montana.edu/xmlui/handle/1/10134.

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

Council of Science Editors:

Fortier NL. Inference and learning in Bayesian networks using overlapping swarm intelligence. [Thesis]. Montana State University; 2015. Available from: https://scholarworks.montana.edu/xmlui/handle/1/10134

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


Columbia University

6. Xie, Shanghong. Statistical Methods for Constructing Heterogeneous Biomarker Networks.

Degree: 2019, Columbia University

 The theme of this dissertation is to construct heterogeneous biomarker networks using graphical models for understanding disease progression and prognosis. Biomarkers may organize into networks… (more)

Subjects/Keywords: Biometry; Biochemical markers; Prognosis; Graphical modeling (Statistics)

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APA (6th Edition):

Xie, S. (2019). Statistical Methods for Constructing Heterogeneous Biomarker Networks. (Doctoral Dissertation). Columbia University. Retrieved from https://doi.org/10.7916/d8-5tzf-0747

Chicago Manual of Style (16th Edition):

Xie, Shanghong. “Statistical Methods for Constructing Heterogeneous Biomarker Networks.” 2019. Doctoral Dissertation, Columbia University. Accessed November 17, 2019. https://doi.org/10.7916/d8-5tzf-0747.

MLA Handbook (7th Edition):

Xie, Shanghong. “Statistical Methods for Constructing Heterogeneous Biomarker Networks.” 2019. Web. 17 Nov 2019.

Vancouver:

Xie S. Statistical Methods for Constructing Heterogeneous Biomarker Networks. [Internet] [Doctoral dissertation]. Columbia University; 2019. [cited 2019 Nov 17]. Available from: https://doi.org/10.7916/d8-5tzf-0747.

Council of Science Editors:

Xie S. Statistical Methods for Constructing Heterogeneous Biomarker Networks. [Doctoral Dissertation]. Columbia University; 2019. Available from: https://doi.org/10.7916/d8-5tzf-0747


Michigan State University

7. Gao, Bin, Ph. D. Graph estimation and network constrained regularization with applications in genetical genomics analysis.

Degree: 2015, Michigan State University

Thesis Ph. D. Michigan State University. Statistics 2015

Estimation and application of graphical structure are important topics in modern statistics. Graphical structure is an ideal… (more)

Subjects/Keywords: Gene regulatory networks; Genomics – Statistical methods; Graphical modeling (Statistics); Statistics

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APA (6th Edition):

Gao, Bin, P. D. (2015). Graph estimation and network constrained regularization with applications in genetical genomics analysis. (Thesis). Michigan State University. Retrieved from http://etd.lib.msu.edu/islandora/object/etd:3745

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):

Gao, Bin, Ph D. “Graph estimation and network constrained regularization with applications in genetical genomics analysis.” 2015. Thesis, Michigan State University. Accessed November 17, 2019. http://etd.lib.msu.edu/islandora/object/etd:3745.

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

MLA Handbook (7th Edition):

Gao, Bin, Ph D. “Graph estimation and network constrained regularization with applications in genetical genomics analysis.” 2015. Web. 17 Nov 2019.

Vancouver:

Gao, Bin PD. Graph estimation and network constrained regularization with applications in genetical genomics analysis. [Internet] [Thesis]. Michigan State University; 2015. [cited 2019 Nov 17]. Available from: http://etd.lib.msu.edu/islandora/object/etd:3745.

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

Council of Science Editors:

Gao, Bin PD. Graph estimation and network constrained regularization with applications in genetical genomics analysis. [Thesis]. Michigan State University; 2015. Available from: http://etd.lib.msu.edu/islandora/object/etd:3745

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


Stellenbosch University

8. Louw, Everhard Johann. A probabilistic graphical model approach to multiple object tracking.

Degree: MEng, Electrical and Electronic Engineering, 2018, Stellenbosch University

ENGLISH ABSTRACT: Probabilistic graphical models (PGMs) provide a framework for efficient probabilistic inference using graphs that correspond to factorised representations of high-dimensional probability distributions. The… (more)

Subjects/Keywords: Decision making with multiple objectives; Graphical modeling (Statistics)  – Probabilities; UCTD

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

APA (6th Edition):

Louw, E. J. (2018). A probabilistic graphical model approach to multiple object tracking. (Thesis). Stellenbosch University. Retrieved from http://hdl.handle.net/10019.1/103534

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):

Louw, Everhard Johann. “A probabilistic graphical model approach to multiple object tracking.” 2018. Thesis, Stellenbosch University. Accessed November 17, 2019. http://hdl.handle.net/10019.1/103534.

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

MLA Handbook (7th Edition):

Louw, Everhard Johann. “A probabilistic graphical model approach to multiple object tracking.” 2018. Web. 17 Nov 2019.

Vancouver:

Louw EJ. A probabilistic graphical model approach to multiple object tracking. [Internet] [Thesis]. Stellenbosch University; 2018. [cited 2019 Nov 17]. Available from: http://hdl.handle.net/10019.1/103534.

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

Council of Science Editors:

Louw EJ. A probabilistic graphical model approach to multiple object tracking. [Thesis]. Stellenbosch University; 2018. Available from: http://hdl.handle.net/10019.1/103534

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


Montana State University

9. Perreault, Logan Jared Perreault. On the usability of continuous time bayesian networks: improving scalability and expressiveness.

Degree: College of Engineering, 2017, Montana State University

 The Continuous Time Bayesian Network (CTBN) is a model capable of compactly representing the behavior of discrete state systems that evolve in continuous time. This… (more)

Subjects/Keywords: Graphical modeling (Statistics).; System analysis.; Mathematical optimization.; Motor vehicles.

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APA (6th Edition):

Perreault, L. J. P. (2017). On the usability of continuous time bayesian networks: improving scalability and expressiveness. (Thesis). Montana State University. Retrieved from https://scholarworks.montana.edu/xmlui/handle/1/14912

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):

Perreault, Logan Jared Perreault. “On the usability of continuous time bayesian networks: improving scalability and expressiveness.” 2017. Thesis, Montana State University. Accessed November 17, 2019. https://scholarworks.montana.edu/xmlui/handle/1/14912.

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

MLA Handbook (7th Edition):

Perreault, Logan Jared Perreault. “On the usability of continuous time bayesian networks: improving scalability and expressiveness.” 2017. Web. 17 Nov 2019.

Vancouver:

Perreault LJP. On the usability of continuous time bayesian networks: improving scalability and expressiveness. [Internet] [Thesis]. Montana State University; 2017. [cited 2019 Nov 17]. Available from: https://scholarworks.montana.edu/xmlui/handle/1/14912.

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

Council of Science Editors:

Perreault LJP. On the usability of continuous time bayesian networks: improving scalability and expressiveness. [Thesis]. Montana State University; 2017. Available from: https://scholarworks.montana.edu/xmlui/handle/1/14912

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


Montana State University

10. Scanlon, Ryan Scott. Modeling mass balance at Robertson Glacier, Alberta, Canada 1912-2012.

Degree: College of Letters & Science, 2017, Montana State University

 Glacier mass balance is important to study due to the role of glaciers in the hydrological cycle. Glacier mass balance is typically difficult to measure… (more)

Subjects/Keywords: Snowpack.; Glaciers.; Mass (Physics).; Hydrology.; Graphical modeling (Statistics).

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APA (6th Edition):

Scanlon, R. S. (2017). Modeling mass balance at Robertson Glacier, Alberta, Canada 1912-2012. (Thesis). Montana State University. Retrieved from https://scholarworks.montana.edu/xmlui/handle/1/14915

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):

Scanlon, Ryan Scott. “Modeling mass balance at Robertson Glacier, Alberta, Canada 1912-2012.” 2017. Thesis, Montana State University. Accessed November 17, 2019. https://scholarworks.montana.edu/xmlui/handle/1/14915.

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

MLA Handbook (7th Edition):

Scanlon, Ryan Scott. “Modeling mass balance at Robertson Glacier, Alberta, Canada 1912-2012.” 2017. Web. 17 Nov 2019.

Vancouver:

Scanlon RS. Modeling mass balance at Robertson Glacier, Alberta, Canada 1912-2012. [Internet] [Thesis]. Montana State University; 2017. [cited 2019 Nov 17]. Available from: https://scholarworks.montana.edu/xmlui/handle/1/14915.

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

Council of Science Editors:

Scanlon RS. Modeling mass balance at Robertson Glacier, Alberta, Canada 1912-2012. [Thesis]. Montana State University; 2017. Available from: https://scholarworks.montana.edu/xmlui/handle/1/14915

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


UCLA

11. Aragam, Nikhyl Bryon. Structure Learning of Linear Bayesian Networks in High-Dimensions.

Degree: Statistics, 2015, UCLA

 Research into graphical models is a rapidly developing enterprise, garnering significant interest from both the statistics and machine learning communities. A parallel thread in both… (more)

Subjects/Keywords: Statistics; Applied mathematics; Bayesian networks; Graphical modeling; High-dimensional statistics; Nonconvex optimization; Regularization; Structure learning

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APA (6th Edition):

Aragam, N. B. (2015). Structure Learning of Linear Bayesian Networks in High-Dimensions. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/9gs5787w

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):

Aragam, Nikhyl Bryon. “Structure Learning of Linear Bayesian Networks in High-Dimensions.” 2015. Thesis, UCLA. Accessed November 17, 2019. http://www.escholarship.org/uc/item/9gs5787w.

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

MLA Handbook (7th Edition):

Aragam, Nikhyl Bryon. “Structure Learning of Linear Bayesian Networks in High-Dimensions.” 2015. Web. 17 Nov 2019.

Vancouver:

Aragam NB. Structure Learning of Linear Bayesian Networks in High-Dimensions. [Internet] [Thesis]. UCLA; 2015. [cited 2019 Nov 17]. Available from: http://www.escholarship.org/uc/item/9gs5787w.

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

Council of Science Editors:

Aragam NB. Structure Learning of Linear Bayesian Networks in High-Dimensions. [Thesis]. UCLA; 2015. Available from: http://www.escholarship.org/uc/item/9gs5787w

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

12. Yellepeddi, Atulya. Graphical model driven methods in adaptive system identification.

Degree: 2016, MIT and Woods Hole Oceanographic Institution

 Identifying and tracking an unknown linear system from observations of its inputs and outputs is a problem at the heart of many different applications. Due… (more)

Subjects/Keywords: Algorithms; Graphical modeling

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APA (6th Edition):

Yellepeddi, A. (2016). Graphical model driven methods in adaptive system identification. (Thesis). MIT and Woods Hole Oceanographic Institution. Retrieved from http://hdl.handle.net/1912/8230

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):

Yellepeddi, Atulya. “Graphical model driven methods in adaptive system identification.” 2016. Thesis, MIT and Woods Hole Oceanographic Institution. Accessed November 17, 2019. http://hdl.handle.net/1912/8230.

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

MLA Handbook (7th Edition):

Yellepeddi, Atulya. “Graphical model driven methods in adaptive system identification.” 2016. Web. 17 Nov 2019.

Vancouver:

Yellepeddi A. Graphical model driven methods in adaptive system identification. [Internet] [Thesis]. MIT and Woods Hole Oceanographic Institution; 2016. [cited 2019 Nov 17]. Available from: http://hdl.handle.net/1912/8230.

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

Council of Science Editors:

Yellepeddi A. Graphical model driven methods in adaptive system identification. [Thesis]. MIT and Woods Hole Oceanographic Institution; 2016. Available from: http://hdl.handle.net/1912/8230

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


MIT

13. Yellepeddi, Atulya. Graphical model driven methods in adaptive system identification .

Degree: 2016, MIT

 Identifying and tracking an unknown linear system from observations of its inputs and outputs is a problem at the heart of many different applications. Due… (more)

Subjects/Keywords: Joint Program in Applied Ocean Science and Engineering.; Electrical Engineering and Computer Science.; Woods Hole Oceanographic Institution.; Algorithms; Graphical modeling (Statistics)

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APA (6th Edition):

Yellepeddi, A. (2016). Graphical model driven methods in adaptive system identification . (Thesis). MIT. Retrieved from http://hdl.handle.net/1721.1/107499

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):

Yellepeddi, Atulya. “Graphical model driven methods in adaptive system identification .” 2016. Thesis, MIT. Accessed November 17, 2019. http://hdl.handle.net/1721.1/107499.

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

MLA Handbook (7th Edition):

Yellepeddi, Atulya. “Graphical model driven methods in adaptive system identification .” 2016. Web. 17 Nov 2019.

Vancouver:

Yellepeddi A. Graphical model driven methods in adaptive system identification . [Internet] [Thesis]. MIT; 2016. [cited 2019 Nov 17]. Available from: http://hdl.handle.net/1721.1/107499.

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

Council of Science Editors:

Yellepeddi A. Graphical model driven methods in adaptive system identification . [Thesis]. MIT; 2016. Available from: http://hdl.handle.net/1721.1/107499

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


University of Missouri – Columbia

14. Liang, Ye. Bayesian methods on selected topics.

Degree: 2012, University of Missouri – Columbia

 Bayesian methods are widely adopted nowadays in statistical analysis. It is especially useful for the statistical inference of complex models or hierarchical models, for which… (more)

Subjects/Keywords: Bayesian statistics; spatial statistics; epidemiology; graphical model

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APA (6th Edition):

Liang, Y. (2012). Bayesian methods on selected topics. (Thesis). University of Missouri – Columbia. Retrieved from http://hdl.handle.net/10355/15884

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):

Liang, Ye. “Bayesian methods on selected topics.” 2012. Thesis, University of Missouri – Columbia. Accessed November 17, 2019. http://hdl.handle.net/10355/15884.

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

MLA Handbook (7th Edition):

Liang, Ye. “Bayesian methods on selected topics.” 2012. Web. 17 Nov 2019.

Vancouver:

Liang Y. Bayesian methods on selected topics. [Internet] [Thesis]. University of Missouri – Columbia; 2012. [cited 2019 Nov 17]. Available from: http://hdl.handle.net/10355/15884.

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

Council of Science Editors:

Liang Y. Bayesian methods on selected topics. [Thesis]. University of Missouri – Columbia; 2012. Available from: http://hdl.handle.net/10355/15884

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


Baylor University

15. [No author]. Graphical methods in prior elicitation.

Degree: 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):

author], [. (2017). Graphical methods in prior elicitation. (Thesis). Baylor University. Retrieved from http://hdl.handle.net/2104/10111

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):

author], [No. “Graphical methods in prior elicitation. ” 2017. Thesis, Baylor University. Accessed November 17, 2019. http://hdl.handle.net/2104/10111.

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

MLA Handbook (7th Edition):

author], [No. “Graphical methods in prior elicitation. ” 2017. Web. 17 Nov 2019.

Vancouver:

author] [. Graphical methods in prior elicitation. [Internet] [Thesis]. Baylor University; 2017. [cited 2019 Nov 17]. Available from: http://hdl.handle.net/2104/10111.

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

Council of Science Editors:

author] [. Graphical methods in prior elicitation. [Thesis]. Baylor University; 2017. Available from: http://hdl.handle.net/2104/10111

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


University of Minnesota

16. Zhu, Yunzhang. Grouping penalties and its applications to high-dimensional models.

Degree: PhD, Statistics, 2014, University of Minnesota

 Part I: In high-dimensional regression, grouping pursuit and feature selection have their own merits while complementing each other in battling the curse of dimensionality. To… (more)

Subjects/Keywords: Graphical models; Grouping penalty; High-dimensional statistics

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APA (6th Edition):

Zhu, Y. (2014). Grouping penalties and its applications to high-dimensional models. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/165147

Chicago Manual of Style (16th Edition):

Zhu, Yunzhang. “Grouping penalties and its applications to high-dimensional models.” 2014. Doctoral Dissertation, University of Minnesota. Accessed November 17, 2019. http://hdl.handle.net/11299/165147.

MLA Handbook (7th Edition):

Zhu, Yunzhang. “Grouping penalties and its applications to high-dimensional models.” 2014. Web. 17 Nov 2019.

Vancouver:

Zhu Y. Grouping penalties and its applications to high-dimensional models. [Internet] [Doctoral dissertation]. University of Minnesota; 2014. [cited 2019 Nov 17]. Available from: http://hdl.handle.net/11299/165147.

Council of Science Editors:

Zhu Y. Grouping penalties and its applications to high-dimensional models. [Doctoral Dissertation]. University of Minnesota; 2014. Available from: http://hdl.handle.net/11299/165147


University of Michigan

17. Hornstein, Michael. Joint Mean and Covariance Modeling of Matrix-Variate Data.

Degree: PhD, Statistics, 2018, University of Michigan

 This dissertation addresses theory, methodology, and applications for joint mean and covariance estimation with matrix-variate data. Chapters 2 and 3 consider joint mean and covariance… (more)

Subjects/Keywords: two-group comparison; sparsity; genomics; generalized least squares; graphical modeling; phonetics pitch curves; Mathematics; Statistics and Numeric Data; Health Sciences; Science; Social Sciences

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APA (6th Edition):

Hornstein, M. (2018). Joint Mean and Covariance Modeling of Matrix-Variate Data. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/145953

Chicago Manual of Style (16th Edition):

Hornstein, Michael. “Joint Mean and Covariance Modeling of Matrix-Variate Data.” 2018. Doctoral Dissertation, University of Michigan. Accessed November 17, 2019. http://hdl.handle.net/2027.42/145953.

MLA Handbook (7th Edition):

Hornstein, Michael. “Joint Mean and Covariance Modeling of Matrix-Variate Data.” 2018. Web. 17 Nov 2019.

Vancouver:

Hornstein M. Joint Mean and Covariance Modeling of Matrix-Variate Data. [Internet] [Doctoral dissertation]. University of Michigan; 2018. [cited 2019 Nov 17]. Available from: http://hdl.handle.net/2027.42/145953.

Council of Science Editors:

Hornstein M. Joint Mean and Covariance Modeling of Matrix-Variate Data. [Doctoral Dissertation]. University of Michigan; 2018. Available from: http://hdl.handle.net/2027.42/145953


University of Washington

18. Li, Zehang. Bayesian Methods for Graphical Models with Limited Data.

Degree: PhD, 2018, University of Washington

 Scientific studies in many fields involve understanding and characterizing dependence relationships among large numbers of variables. This can be challenging in settings where data is… (more)

Subjects/Keywords: Bayesian methods; Graphical model; Spike-and-slab; Verbal Autopsy; Statistics; Statistics

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APA (6th Edition):

Li, Z. (2018). Bayesian Methods for Graphical Models with Limited Data. (Doctoral Dissertation). University of Washington. Retrieved from http://hdl.handle.net/1773/43158

Chicago Manual of Style (16th Edition):

Li, Zehang. “Bayesian Methods for Graphical Models with Limited Data.” 2018. Doctoral Dissertation, University of Washington. Accessed November 17, 2019. http://hdl.handle.net/1773/43158.

MLA Handbook (7th Edition):

Li, Zehang. “Bayesian Methods for Graphical Models with Limited Data.” 2018. Web. 17 Nov 2019.

Vancouver:

Li Z. Bayesian Methods for Graphical Models with Limited Data. [Internet] [Doctoral dissertation]. University of Washington; 2018. [cited 2019 Nov 17]. Available from: http://hdl.handle.net/1773/43158.

Council of Science Editors:

Li Z. Bayesian Methods for Graphical Models with Limited Data. [Doctoral Dissertation]. University of Washington; 2018. Available from: http://hdl.handle.net/1773/43158


University of Washington

19. Theobald, Roderick Jenkins. Lord's Paradox and Targeted Interventions: The Case of Special Education.

Degree: PhD, 2015, University of Washington

 Lord (1967) describes a hypothetical “paradox” in which two statisticians, analyzing the same dataset using different but defensible methods, come to very different conclusions about… (more)

Subjects/Keywords: causal inference; graphical methods; instrumental variables; special education; Statistics; Education; statistics

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APA (6th Edition):

Theobald, R. J. (2015). Lord's Paradox and Targeted Interventions: The Case of Special Education. (Doctoral Dissertation). University of Washington. Retrieved from http://hdl.handle.net/1773/34191

Chicago Manual of Style (16th Edition):

Theobald, Roderick Jenkins. “Lord's Paradox and Targeted Interventions: The Case of Special Education.” 2015. Doctoral Dissertation, University of Washington. Accessed November 17, 2019. http://hdl.handle.net/1773/34191.

MLA Handbook (7th Edition):

Theobald, Roderick Jenkins. “Lord's Paradox and Targeted Interventions: The Case of Special Education.” 2015. Web. 17 Nov 2019.

Vancouver:

Theobald RJ. Lord's Paradox and Targeted Interventions: The Case of Special Education. [Internet] [Doctoral dissertation]. University of Washington; 2015. [cited 2019 Nov 17]. Available from: http://hdl.handle.net/1773/34191.

Council of Science Editors:

Theobald RJ. Lord's Paradox and Targeted Interventions: The Case of Special Education. [Doctoral Dissertation]. University of Washington; 2015. Available from: http://hdl.handle.net/1773/34191


University of Washington

20. Lin, Lina. Methods for estimation and inference for high-dimensional models.

Degree: PhD, 2018, University of Washington

 This thesis tackles three different problems in high-dimensional statistics. The first two parts of the thesis focus on estimation of sparse high-dimensional undirected graphical models… (more)

Subjects/Keywords: Graphical models; High-dimensional statistics; Linear mixed effect models; Regularization; Statistics; Statistics

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APA (6th Edition):

Lin, L. (2018). Methods for estimation and inference for high-dimensional models. (Doctoral Dissertation). University of Washington. Retrieved from http://hdl.handle.net/1773/40975

Chicago Manual of Style (16th Edition):

Lin, Lina. “Methods for estimation and inference for high-dimensional models.” 2018. Doctoral Dissertation, University of Washington. Accessed November 17, 2019. http://hdl.handle.net/1773/40975.

MLA Handbook (7th Edition):

Lin, Lina. “Methods for estimation and inference for high-dimensional models.” 2018. Web. 17 Nov 2019.

Vancouver:

Lin L. Methods for estimation and inference for high-dimensional models. [Internet] [Doctoral dissertation]. University of Washington; 2018. [cited 2019 Nov 17]. Available from: http://hdl.handle.net/1773/40975.

Council of Science Editors:

Lin L. Methods for estimation and inference for high-dimensional models. [Doctoral Dissertation]. University of Washington; 2018. Available from: http://hdl.handle.net/1773/40975


Queens University

21. Eloumri, Miloud Salem S. GRAPHICAL EDITORS GENERATION WITH THE GRAPHICAL MODELING FRAMEWORK: A CASE STUDY .

Degree: Computing, 2011, Queens University

 Domain Specific Modeling (DSM) aims to increase productivity of software development by raising the level of abstraction beyond code concepts and using domain concepts. By… (more)

Subjects/Keywords: Domain Specific Modeling (DSM); Graphical Modeling Framework (GMF); Eclipse Modeling; State Machine Compiler (SMC)

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APA (6th Edition):

Eloumri, M. S. S. (2011). GRAPHICAL EDITORS GENERATION WITH THE GRAPHICAL MODELING FRAMEWORK: A CASE STUDY . (Thesis). Queens University. Retrieved from http://hdl.handle.net/1974/6366

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):

Eloumri, Miloud Salem S. “GRAPHICAL EDITORS GENERATION WITH THE GRAPHICAL MODELING FRAMEWORK: A CASE STUDY .” 2011. Thesis, Queens University. Accessed November 17, 2019. http://hdl.handle.net/1974/6366.

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

MLA Handbook (7th Edition):

Eloumri, Miloud Salem S. “GRAPHICAL EDITORS GENERATION WITH THE GRAPHICAL MODELING FRAMEWORK: A CASE STUDY .” 2011. Web. 17 Nov 2019.

Vancouver:

Eloumri MSS. GRAPHICAL EDITORS GENERATION WITH THE GRAPHICAL MODELING FRAMEWORK: A CASE STUDY . [Internet] [Thesis]. Queens University; 2011. [cited 2019 Nov 17]. Available from: http://hdl.handle.net/1974/6366.

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

Council of Science Editors:

Eloumri MSS. GRAPHICAL EDITORS GENERATION WITH THE GRAPHICAL MODELING FRAMEWORK: A CASE STUDY . [Thesis]. Queens University; 2011. Available from: http://hdl.handle.net/1974/6366

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


University of California – Berkeley

22. Loh, Po-LIng. High-dimensional statistics with systematically corrupted data.

Degree: Statistics, 2014, University of California – Berkeley

 Noisy and missing data are prevalent in many real-world statistical estimation problems. Popular techniques for handling nonidealities in data, such as imputation and expectation-maximization, are… (more)

Subjects/Keywords: Statistics; Computer science; Electrical engineering; graphical models; high-dimensional statistics; machine learning; optimization

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APA (6th Edition):

Loh, P. (2014). High-dimensional statistics with systematically corrupted data. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/8j49c5n4

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):

Loh, Po-LIng. “High-dimensional statistics with systematically corrupted data.” 2014. Thesis, University of California – Berkeley. Accessed November 17, 2019. http://www.escholarship.org/uc/item/8j49c5n4.

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

MLA Handbook (7th Edition):

Loh, Po-LIng. “High-dimensional statistics with systematically corrupted data.” 2014. Web. 17 Nov 2019.

Vancouver:

Loh P. High-dimensional statistics with systematically corrupted data. [Internet] [Thesis]. University of California – Berkeley; 2014. [cited 2019 Nov 17]. Available from: http://www.escholarship.org/uc/item/8j49c5n4.

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

Council of Science Editors:

Loh P. High-dimensional statistics with systematically corrupted data. [Thesis]. University of California – Berkeley; 2014. Available from: http://www.escholarship.org/uc/item/8j49c5n4

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


Rice University

23. Peterson, Christine. Bayesian graphical models for biological network inference.

Degree: PhD, Engineering, 2013, Rice University

 In this work, we propose approaches for the inference of graphical models in the Bayesian framework. Graphical models, which use a network structure to represent… (more)

Subjects/Keywords: Statistics; Graphical models; Bayesian inference; Informative priors; Biological networks

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APA (6th Edition):

Peterson, C. (2013). Bayesian graphical models for biological network inference. (Doctoral Dissertation). Rice University. Retrieved from http://hdl.handle.net/1911/77444

Chicago Manual of Style (16th Edition):

Peterson, Christine. “Bayesian graphical models for biological network inference.” 2013. Doctoral Dissertation, Rice University. Accessed November 17, 2019. http://hdl.handle.net/1911/77444.

MLA Handbook (7th Edition):

Peterson, Christine. “Bayesian graphical models for biological network inference.” 2013. Web. 17 Nov 2019.

Vancouver:

Peterson C. Bayesian graphical models for biological network inference. [Internet] [Doctoral dissertation]. Rice University; 2013. [cited 2019 Nov 17]. Available from: http://hdl.handle.net/1911/77444.

Council of Science Editors:

Peterson C. Bayesian graphical models for biological network inference. [Doctoral Dissertation]. Rice University; 2013. Available from: http://hdl.handle.net/1911/77444


Cornell University

24. Sinclair, David Giles. Model selection results for latent high-dimensional graphical models on binary and count data with applications to fMRI and Genomics .

Degree: 2017, Cornell University

 This dissertation explores the undirected graphical model framework. We explore applications of highly dependent binary data and count data in order to determine to determine… (more)

Subjects/Keywords: Statistics; fMRI; Graphical Models; Expectation Maximzation; Latent Network; miRNA; lasso

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APA (6th Edition):

Sinclair, D. G. (2017). Model selection results for latent high-dimensional graphical models on binary and count data with applications to fMRI and Genomics . (Thesis). Cornell University. Retrieved from http://hdl.handle.net/1813/56786

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):

Sinclair, David Giles. “Model selection results for latent high-dimensional graphical models on binary and count data with applications to fMRI and Genomics .” 2017. Thesis, Cornell University. Accessed November 17, 2019. http://hdl.handle.net/1813/56786.

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

MLA Handbook (7th Edition):

Sinclair, David Giles. “Model selection results for latent high-dimensional graphical models on binary and count data with applications to fMRI and Genomics .” 2017. Web. 17 Nov 2019.

Vancouver:

Sinclair DG. Model selection results for latent high-dimensional graphical models on binary and count data with applications to fMRI and Genomics . [Internet] [Thesis]. Cornell University; 2017. [cited 2019 Nov 17]. Available from: http://hdl.handle.net/1813/56786.

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

Council of Science Editors:

Sinclair DG. Model selection results for latent high-dimensional graphical models on binary and count data with applications to fMRI and Genomics . [Thesis]. Cornell University; 2017. Available from: http://hdl.handle.net/1813/56786

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


University of Cambridge

25. Rowland, Mark. Structure in machine learning : graphical models and Monte Carlo methods.

Degree: PhD, 2018, University of Cambridge

 This thesis is concerned with two main areas: approximate inference in discrete graphical models, and random embeddings for dimensionality reduction and approximate inference in kernel… (more)

Subjects/Keywords: Mathematics; Statistics; Machine Learning; Graphical Models; Monte Carlo Methods; Kernel Methods

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APA (6th Edition):

Rowland, M. (2018). Structure in machine learning : graphical models and Monte Carlo methods. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/287479 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.763924

Chicago Manual of Style (16th Edition):

Rowland, Mark. “Structure in machine learning : graphical models and Monte Carlo methods.” 2018. Doctoral Dissertation, University of Cambridge. Accessed November 17, 2019. https://www.repository.cam.ac.uk/handle/1810/287479 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.763924.

MLA Handbook (7th Edition):

Rowland, Mark. “Structure in machine learning : graphical models and Monte Carlo methods.” 2018. Web. 17 Nov 2019.

Vancouver:

Rowland M. Structure in machine learning : graphical models and Monte Carlo methods. [Internet] [Doctoral dissertation]. University of Cambridge; 2018. [cited 2019 Nov 17]. Available from: https://www.repository.cam.ac.uk/handle/1810/287479 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.763924.

Council of Science Editors:

Rowland M. Structure in machine learning : graphical models and Monte Carlo methods. [Doctoral Dissertation]. University of Cambridge; 2018. Available from: https://www.repository.cam.ac.uk/handle/1810/287479 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.763924


Bowling Green State University

26. Kreuz, Sarah, Kreuz. An Analysis of the Variation in Dressage Judge Scoring.

Degree: MS, Applied Statistics (Math), 2018, Bowling Green State University

 In any subjectively scored sport, there is always the possibility of judge bias. After events at the 2008 Olympics at Beijing caused the scoring methods… (more)

Subjects/Keywords: Statistics; statistics; Bayes; multilevel modeling; dressage

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APA (6th Edition):

Kreuz, Sarah, K. (2018). An Analysis of the Variation in Dressage Judge Scoring. (Masters Thesis). Bowling Green State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1530480688061936

Chicago Manual of Style (16th Edition):

Kreuz, Sarah, Kreuz. “An Analysis of the Variation in Dressage Judge Scoring.” 2018. Masters Thesis, Bowling Green State University. Accessed November 17, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1530480688061936.

MLA Handbook (7th Edition):

Kreuz, Sarah, Kreuz. “An Analysis of the Variation in Dressage Judge Scoring.” 2018. Web. 17 Nov 2019.

Vancouver:

Kreuz, Sarah K. An Analysis of the Variation in Dressage Judge Scoring. [Internet] [Masters thesis]. Bowling Green State University; 2018. [cited 2019 Nov 17]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1530480688061936.

Council of Science Editors:

Kreuz, Sarah K. An Analysis of the Variation in Dressage Judge Scoring. [Masters Thesis]. Bowling Green State University; 2018. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1530480688061936


George Mason University

27. Heim, Krista. Visualization and Modeling for Crime Data Indexed by Road Segments .

Degree: 2014, George Mason University

 This research develops crime hotspot analysis and visualization methodology that use street segments as the basic study unit. This incorporates the distance between points along… (more)

Subjects/Keywords: Statistics; crime; mapping; modeling; statistics; visualization

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APA (6th Edition):

Heim, K. (2014). Visualization and Modeling for Crime Data Indexed by Road Segments . (Thesis). George Mason University. Retrieved from http://hdl.handle.net/1920/8991

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):

Heim, Krista. “Visualization and Modeling for Crime Data Indexed by Road Segments .” 2014. Thesis, George Mason University. Accessed November 17, 2019. http://hdl.handle.net/1920/8991.

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

MLA Handbook (7th Edition):

Heim, Krista. “Visualization and Modeling for Crime Data Indexed by Road Segments .” 2014. Web. 17 Nov 2019.

Vancouver:

Heim K. Visualization and Modeling for Crime Data Indexed by Road Segments . [Internet] [Thesis]. George Mason University; 2014. [cited 2019 Nov 17]. Available from: http://hdl.handle.net/1920/8991.

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

Council of Science Editors:

Heim K. Visualization and Modeling for Crime Data Indexed by Road Segments . [Thesis]. George Mason University; 2014. Available from: http://hdl.handle.net/1920/8991

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


UCLA

28. Jiang, Ziyi. How Did Trump Win "Bigly" in 2016.

Degree: Statistics, 2019, UCLA

 Controversial election results are quite common in the US politics. But the election of 2016 will be studied over and over. The victory of Donald… (more)

Subjects/Keywords: Statistics; Election; Modeling; Republican; Statistics; Trump

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APA (6th Edition):

Jiang, Z. (2019). How Did Trump Win "Bigly" in 2016. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/9pt7t93c

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):

Jiang, Ziyi. “How Did Trump Win "Bigly" in 2016.” 2019. Thesis, UCLA. Accessed November 17, 2019. http://www.escholarship.org/uc/item/9pt7t93c.

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

MLA Handbook (7th Edition):

Jiang, Ziyi. “How Did Trump Win "Bigly" in 2016.” 2019. Web. 17 Nov 2019.

Vancouver:

Jiang Z. How Did Trump Win "Bigly" in 2016. [Internet] [Thesis]. UCLA; 2019. [cited 2019 Nov 17]. Available from: http://www.escholarship.org/uc/item/9pt7t93c.

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

Council of Science Editors:

Jiang Z. How Did Trump Win "Bigly" in 2016. [Thesis]. UCLA; 2019. Available from: http://www.escholarship.org/uc/item/9pt7t93c

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

29. Bhattacharya, Debswapna. Probabilistic graphical models for protein structure prediction.

Degree: 2016, University of Missouri – Columbia

 Computationally predicting the folded and functional three-dimensional structure of a protein molecule from its amino acid sequence with high degree of accuracy is critically important… (more)

Subjects/Keywords: Proteins  – Structure; Proteins  – Structure  – Mathematical models; Proteins  – Conformation; Machine learning; Graphical modeling (Statistics)

modeling of united-residue polypeptide conformational space… …Carlo MQAP: Model Quality Assessment Program MAE: Mean Absolute Error FM: Free Modeling TBM… …Template Based Modeling EM: Expectation Maximization AIC: Akaike Information Criterion RMSD: Root… …developing novel probabilistic graphical models and experimentally motivated probabilistic sampling… …structured as follows. In chapter 2, we propose a novel generative, probabilistic graphical model… 

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APA (6th Edition):

Bhattacharya, D. (2016). Probabilistic graphical models for protein structure prediction. (Thesis). University of Missouri – Columbia. Retrieved from http://hdl.handle.net/10355/57017

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):

Bhattacharya, Debswapna. “Probabilistic graphical models for protein structure prediction.” 2016. Thesis, University of Missouri – Columbia. Accessed November 17, 2019. http://hdl.handle.net/10355/57017.

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

MLA Handbook (7th Edition):

Bhattacharya, Debswapna. “Probabilistic graphical models for protein structure prediction.” 2016. Web. 17 Nov 2019.

Vancouver:

Bhattacharya D. Probabilistic graphical models for protein structure prediction. [Internet] [Thesis]. University of Missouri – Columbia; 2016. [cited 2019 Nov 17]. Available from: http://hdl.handle.net/10355/57017.

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

Council of Science Editors:

Bhattacharya D. Probabilistic graphical models for protein structure prediction. [Thesis]. University of Missouri – Columbia; 2016. Available from: http://hdl.handle.net/10355/57017

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


Baylor University

30. Atkinson, William H., 1980-. Spatial Poisson regression : Bayesian approach correcting for measurement error with applications.

Degree: Statistical Sciences., 2010, Baylor University

 Under and over reporting is a common problem in social science research, adverse events associated with drug use, and many other areas of research. Furthermore,… (more)

Subjects/Keywords: Statistics.; Statistical modeling.; Bayesian methods.

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APA (6th Edition):

Atkinson, William H., 1. (2010). Spatial Poisson regression : Bayesian approach correcting for measurement error with applications. (Thesis). Baylor University. Retrieved from http://hdl.handle.net/2104/8019

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):

Atkinson, William H., 1980-. “Spatial Poisson regression : Bayesian approach correcting for measurement error with applications. ” 2010. Thesis, Baylor University. Accessed November 17, 2019. http://hdl.handle.net/2104/8019.

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

MLA Handbook (7th Edition):

Atkinson, William H., 1980-. “Spatial Poisson regression : Bayesian approach correcting for measurement error with applications. ” 2010. Web. 17 Nov 2019.

Vancouver:

Atkinson, William H. 1. Spatial Poisson regression : Bayesian approach correcting for measurement error with applications. [Internet] [Thesis]. Baylor University; 2010. [cited 2019 Nov 17]. Available from: http://hdl.handle.net/2104/8019.

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

Council of Science Editors:

Atkinson, William H. 1. Spatial Poisson regression : Bayesian approach correcting for measurement error with applications. [Thesis]. Baylor University; 2010. Available from: http://hdl.handle.net/2104/8019

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

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