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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

URL: https://doi.org/10.14711/thesis-991012564766703412 ; http://repository.ust.hk/ir/bitstream/1783.1-91181/1/th_redirect.html

► 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)

Record Details Similar Records

❌

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

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

URL: https://scholarworks.montana.edu/xmlui/handle/1/9368

► 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).

Record Details Similar Records

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

APA (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

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

URL: http://hdl.handle.net/10179/3659

► 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

Record Details Similar Records

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://hdl.handle.net/2144/33117

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: https://scholarworks.montana.edu/xmlui/handle/1/10134

► 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.

Record Details Similar Records

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

APA (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

Not specified: Masters Thesis or Doctoral Dissertation

Columbia University

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

Degree: 2019, Columbia University

URL: https://doi.org/10.7916/d8-5tzf-0747

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://etd.lib.msu.edu/islandora/object/etd:3745

►

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

Record Details Similar Records

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

APA (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

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

URL: http://hdl.handle.net/10019.1/103534

►

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

Record Details Similar Records

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

APA (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

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

URL: https://scholarworks.montana.edu/xmlui/handle/1/14912

► 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.

Record Details Similar Records

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

APA (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

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

URL: https://scholarworks.montana.edu/xmlui/handle/1/14915

► 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).

Record Details Similar Records

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

APA (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

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

URL: http://www.escholarship.org/uc/item/9gs5787w

► 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

Record Details Similar Records

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

APA (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

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

URL: http://hdl.handle.net/1912/8230

► 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

Record Details Similar Records

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

APA (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

Not specified: Masters Thesis or Doctoral Dissertation

MIT

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

Degree: 2016, MIT

URL: http://hdl.handle.net/1721.1/107499

► 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)

Record Details Similar Records

❌

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

APA (6^{th} Edition):

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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

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

URL: http://hdl.handle.net/10355/15884

► 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

Record Details Similar Records

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

APA (6^{th} Edition):

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

Not specified: Masters Thesis or Doctoral Dissertation

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

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

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

Not specified: Masters Thesis or Doctoral Dissertation

Baylor University

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

Degree: 2017, Baylor University

URL: http://hdl.handle.net/2104/10111

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

APA (6^{th} Edition):

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

Not specified: Masters Thesis or Doctoral Dissertation

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

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

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

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

URL: http://hdl.handle.net/11299/165147

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://hdl.handle.net/2027.42/145953

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://hdl.handle.net/1773/43158

► 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 (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://hdl.handle.net/1773/34191

► 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 (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://hdl.handle.net/1773/40975

► 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 (6^{th} 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 (16^{th} 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 (7^{th} 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

Degree: Computing, 2011, Queens University

URL: http://hdl.handle.net/1974/6366

► 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 (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

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

URL: http://www.escholarship.org/uc/item/8j49c5n4

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

APA (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

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

URL: http://hdl.handle.net/1911/77444

► 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 (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://hdl.handle.net/1813/56786

► 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 (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

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

URL: https://www.repository.cam.ac.uk/handle/1810/287479 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.763924

► 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

Record Details Similar Records

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

APA (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1530480688061936

► 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 (6^{th} 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 (16^{th} 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 (7^{th} 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

URL: http://hdl.handle.net/1920/8991

► 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

Record Details Similar Records

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APA (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

Not specified: Masters Thesis or Doctoral Dissertation

UCLA

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

Degree: Statistics, 2019, UCLA

URL: http://www.escholarship.org/uc/item/9pt7t93c

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

APA (6^{th} Edition):

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

Not specified: Masters Thesis or Doctoral Dissertation

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

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

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

Not specified: Masters Thesis or Doctoral Dissertation

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

Degree: 2016, University of Missouri – Columbia

URL: http://hdl.handle.net/10355/57017

► 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…

Record Details Similar Records

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

APA (6^{th} Edition):

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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

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

URL: http://hdl.handle.net/2104/8019

► 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 (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} 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.

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

Not specified: Masters Thesis or Doctoral Dissertation