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277 total matches.

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- 2011 – 2015 (131)
- 2006 – 2010 (44)

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- Computer Science (29)
- Statistics (13)

Degrees

- PhD (88)
- Docteur es (30)
- MS (15)

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1. Pacheco, Jason L. Variational Approximations with Diverse Applications.

Degree: PhD, Computer Science, 2016, Brown University

URL: https://repository.library.brown.edu/studio/item/bdr:674262/

► We develop a family of algorithms for statistical inference in *models* of high dimensional continuous random variables. Our approach builds on existing variational methods, which…
(more)

Subjects/Keywords: graphical models

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

APA (6^{th} Edition):

Pacheco, J. L. (2016). Variational Approximations with Diverse Applications. (Doctoral Dissertation). Brown University. Retrieved from https://repository.library.brown.edu/studio/item/bdr:674262/

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

Pacheco, Jason L. “Variational Approximations with Diverse Applications.” 2016. Doctoral Dissertation, Brown University. Accessed September 27, 2020. https://repository.library.brown.edu/studio/item/bdr:674262/.

MLA Handbook (7^{th} Edition):

Pacheco, Jason L. “Variational Approximations with Diverse Applications.” 2016. Web. 27 Sep 2020.

Vancouver:

Pacheco JL. Variational Approximations with Diverse Applications. [Internet] [Doctoral dissertation]. Brown University; 2016. [cited 2020 Sep 27]. Available from: https://repository.library.brown.edu/studio/item/bdr:674262/.

Council of Science Editors:

Pacheco JL. Variational Approximations with Diverse Applications. [Doctoral Dissertation]. Brown University; 2016. Available from: https://repository.library.brown.edu/studio/item/bdr:674262/

2. Buchanan, David W. Edge Replacement as a Model of Causal Reasoning.

Degree: PhD, Cognitive Sciences, 2011, Brown University

URL: https://repository.library.brown.edu/studio/item/bdr:11424/

► The last two decades in Cognitive Science have seen the productive application of Causal *Graphical* *Models* (Pearl, 2000; Spirtes, Glymour & Scheines, 1993) to theories…
(more)

Subjects/Keywords: causal graphical models

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APA (6^{th} Edition):

Buchanan, D. W. (2011). Edge Replacement as a Model of Causal Reasoning. (Doctoral Dissertation). Brown University. Retrieved from https://repository.library.brown.edu/studio/item/bdr:11424/

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

Buchanan, David W. “Edge Replacement as a Model of Causal Reasoning.” 2011. Doctoral Dissertation, Brown University. Accessed September 27, 2020. https://repository.library.brown.edu/studio/item/bdr:11424/.

MLA Handbook (7^{th} Edition):

Buchanan, David W. “Edge Replacement as a Model of Causal Reasoning.” 2011. Web. 27 Sep 2020.

Vancouver:

Buchanan DW. Edge Replacement as a Model of Causal Reasoning. [Internet] [Doctoral dissertation]. Brown University; 2011. [cited 2020 Sep 27]. Available from: https://repository.library.brown.edu/studio/item/bdr:11424/.

Council of Science Editors:

Buchanan DW. Edge Replacement as a Model of Causal Reasoning. [Doctoral Dissertation]. Brown University; 2011. Available from: https://repository.library.brown.edu/studio/item/bdr:11424/

Cornell University

3. Tian, Yuan. A Parallel Implementation Of Hierarchical Belief Propagation.

Degree: M.S., Electrical Engineering, Electrical Engineering, 2013, Cornell University

URL: http://hdl.handle.net/1813/34099

► Though Belief Propagation (BP) algorithms generate high quality results for a wide range of Markov Random Field (MRF) formulated energy minimization problems, they require large…
(more)

Subjects/Keywords: Belief Propagation; Graphical Models; Parallelism

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APA (6^{th} Edition):

Tian, Y. (2013). A Parallel Implementation Of Hierarchical Belief Propagation. (Masters Thesis). Cornell University. Retrieved from http://hdl.handle.net/1813/34099

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

Tian, Yuan. “A Parallel Implementation Of Hierarchical Belief Propagation.” 2013. Masters Thesis, Cornell University. Accessed September 27, 2020. http://hdl.handle.net/1813/34099.

MLA Handbook (7^{th} Edition):

Tian, Yuan. “A Parallel Implementation Of Hierarchical Belief Propagation.” 2013. Web. 27 Sep 2020.

Vancouver:

Tian Y. A Parallel Implementation Of Hierarchical Belief Propagation. [Internet] [Masters thesis]. Cornell University; 2013. [cited 2020 Sep 27]. Available from: http://hdl.handle.net/1813/34099.

Council of Science Editors:

Tian Y. A Parallel Implementation Of Hierarchical Belief Propagation. [Masters Thesis]. Cornell University; 2013. Available from: http://hdl.handle.net/1813/34099

University of Rochester

4.
Wei, Bin (1981 - ).
*Graphical**models* for heterogeneous transfer learning and
co-reference resolution.

Degree: PhD, 2011, University of Rochester

URL: http://hdl.handle.net/1802/15216

► Traditional supervised machine learning requires labeled data for a specific problem of interest. There have been many attempts to reduce this requirement such as approaches…
(more)

Subjects/Keywords: Graphical models; Transfer learning

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APA (6^{th} Edition):

Wei, B. (. -. ). (2011). Graphical models for heterogeneous transfer learning and co-reference resolution. (Doctoral Dissertation). University of Rochester. Retrieved from http://hdl.handle.net/1802/15216

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

Wei, Bin (1981 - ). “Graphical models for heterogeneous transfer learning and co-reference resolution.” 2011. Doctoral Dissertation, University of Rochester. Accessed September 27, 2020. http://hdl.handle.net/1802/15216.

MLA Handbook (7^{th} Edition):

Wei, Bin (1981 - ). “Graphical models for heterogeneous transfer learning and co-reference resolution.” 2011. Web. 27 Sep 2020.

Vancouver:

Wei B(-). Graphical models for heterogeneous transfer learning and co-reference resolution. [Internet] [Doctoral dissertation]. University of Rochester; 2011. [cited 2020 Sep 27]. Available from: http://hdl.handle.net/1802/15216.

Council of Science Editors:

Wei B(-). Graphical models for heterogeneous transfer learning and co-reference resolution. [Doctoral Dissertation]. University of Rochester; 2011. Available from: http://hdl.handle.net/1802/15216

University of Oregon

5.
Kelly, Austen.
Exploiting Domain Structure with Hybrid Generative-Discriminative * Models*.

Degree: MS, Department of Computer and Information Science, 2020, University of Oregon

URL: https://scholarsbank.uoregon.edu/xmlui/handle/1794/25295

► Machine learning methods often face a tradeoff between the accuracy of discriminative *models* and the lower sample complexity of their generative counterparts. This inspires a…
(more)

Subjects/Keywords: machine learning; probabilistic graphical models

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APA (6^{th} Edition):

Kelly, A. (2020). Exploiting Domain Structure with Hybrid Generative-Discriminative Models. (Masters Thesis). University of Oregon. Retrieved from https://scholarsbank.uoregon.edu/xmlui/handle/1794/25295

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

Kelly, Austen. “Exploiting Domain Structure with Hybrid Generative-Discriminative Models.” 2020. Masters Thesis, University of Oregon. Accessed September 27, 2020. https://scholarsbank.uoregon.edu/xmlui/handle/1794/25295.

MLA Handbook (7^{th} Edition):

Kelly, Austen. “Exploiting Domain Structure with Hybrid Generative-Discriminative Models.” 2020. Web. 27 Sep 2020.

Vancouver:

Kelly A. Exploiting Domain Structure with Hybrid Generative-Discriminative Models. [Internet] [Masters thesis]. University of Oregon; 2020. [cited 2020 Sep 27]. Available from: https://scholarsbank.uoregon.edu/xmlui/handle/1794/25295.

Council of Science Editors:

Kelly A. Exploiting Domain Structure with Hybrid Generative-Discriminative Models. [Masters Thesis]. University of Oregon; 2020. Available from: https://scholarsbank.uoregon.edu/xmlui/handle/1794/25295

University of New Mexico

6. Oyen, Diane. Interactive Exploration of Multitask Dependency Networks.

Degree: Department of Computer Science, 2013, University of New Mexico

URL: http://hdl.handle.net/1928/23359

► Scientists increasingly depend on machine learning algorithms to discover patterns in complex data. Two examples addressed in this dissertation are identifying how information sharing among…
(more)

Subjects/Keywords: machine learning; probabilistic graphical models

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APA (6^{th} Edition):

Oyen, D. (2013). Interactive Exploration of Multitask Dependency Networks. (Doctoral Dissertation). University of New Mexico. Retrieved from http://hdl.handle.net/1928/23359

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

Oyen, Diane. “Interactive Exploration of Multitask Dependency Networks.” 2013. Doctoral Dissertation, University of New Mexico. Accessed September 27, 2020. http://hdl.handle.net/1928/23359.

MLA Handbook (7^{th} Edition):

Oyen, Diane. “Interactive Exploration of Multitask Dependency Networks.” 2013. Web. 27 Sep 2020.

Vancouver:

Oyen D. Interactive Exploration of Multitask Dependency Networks. [Internet] [Doctoral dissertation]. University of New Mexico; 2013. [cited 2020 Sep 27]. Available from: http://hdl.handle.net/1928/23359.

Council of Science Editors:

Oyen D. Interactive Exploration of Multitask Dependency Networks. [Doctoral Dissertation]. University of New Mexico; 2013. Available from: http://hdl.handle.net/1928/23359

Georgia Tech

7. Shegheva, Snejana. A computational model for solving raven’s progressive matrices intelligence test.

Degree: MS, Computer Science, 2018, Georgia Tech

URL: http://hdl.handle.net/1853/60191

► *Graphical* *models* offer techniques for capturing the structure of many problems in real- world domains and provide means for representation, interpretation, and inference. The modeling…
(more)

Subjects/Keywords: Probabilistic graphical models; Machine learning

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APA (6^{th} Edition):

Shegheva, S. (2018). A computational model for solving raven’s progressive matrices intelligence test. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60191

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

Shegheva, Snejana. “A computational model for solving raven’s progressive matrices intelligence test.” 2018. Masters Thesis, Georgia Tech. Accessed September 27, 2020. http://hdl.handle.net/1853/60191.

MLA Handbook (7^{th} Edition):

Shegheva, Snejana. “A computational model for solving raven’s progressive matrices intelligence test.” 2018. Web. 27 Sep 2020.

Vancouver:

Shegheva S. A computational model for solving raven’s progressive matrices intelligence test. [Internet] [Masters thesis]. Georgia Tech; 2018. [cited 2020 Sep 27]. Available from: http://hdl.handle.net/1853/60191.

Council of Science Editors:

Shegheva S. A computational model for solving raven’s progressive matrices intelligence test. [Masters Thesis]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/60191

University of Florida

8.
Jalali, Peyman.
Bayesian Estimation and Model Selection for Single and Multiple *Graphical* * Models*.

Degree: PhD, Statistics, 2019, University of Florida

URL: https://ufdc.ufl.edu/UFE0054270

► Undirected *Graphical* *Models* represent a family of canonical statistical *models* for reconstructing interactions amongst a set of entities from multi-dimensional data profiles. They have numerous…
(more)

Subjects/Keywords: bayesian-graphical-models – joint-estimation-of-graphical-models – metabolomics-data

Record Details Similar Records

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

APA (6^{th} Edition):

Jalali, P. (2019). Bayesian Estimation and Model Selection for Single and Multiple Graphical Models. (Doctoral Dissertation). University of Florida. Retrieved from https://ufdc.ufl.edu/UFE0054270

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

Jalali, Peyman. “Bayesian Estimation and Model Selection for Single and Multiple Graphical Models.” 2019. Doctoral Dissertation, University of Florida. Accessed September 27, 2020. https://ufdc.ufl.edu/UFE0054270.

MLA Handbook (7^{th} Edition):

Jalali, Peyman. “Bayesian Estimation and Model Selection for Single and Multiple Graphical Models.” 2019. Web. 27 Sep 2020.

Vancouver:

Jalali P. Bayesian Estimation and Model Selection for Single and Multiple Graphical Models. [Internet] [Doctoral dissertation]. University of Florida; 2019. [cited 2020 Sep 27]. Available from: https://ufdc.ufl.edu/UFE0054270.

Council of Science Editors:

Jalali P. Bayesian Estimation and Model Selection for Single and Multiple Graphical Models. [Doctoral Dissertation]. University of Florida; 2019. Available from: https://ufdc.ufl.edu/UFE0054270

University of Illinois – Urbana-Champaign

9. Mangipudi, Bhargav. Evaluating exact and approximate algorithms for integer linear programming formulations of MAP inference.

Degree: MS, Computer Science, 2017, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/99111

► Structured prediction tasks involve an inference step which allows for producing coherent label assignments to the output structure. This can be achieved by constraining the…
(more)

Subjects/Keywords: Structured inference; Constrained conditional models; Graphical models

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APA (6^{th} Edition):

Mangipudi, B. (2017). Evaluating exact and approximate algorithms for integer linear programming formulations of MAP inference. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/99111

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):

Mangipudi, Bhargav. “Evaluating exact and approximate algorithms for integer linear programming formulations of MAP inference.” 2017. Thesis, University of Illinois – Urbana-Champaign. Accessed September 27, 2020. http://hdl.handle.net/2142/99111.

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

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Mangipudi, Bhargav. “Evaluating exact and approximate algorithms for integer linear programming formulations of MAP inference.” 2017. Web. 27 Sep 2020.

Vancouver:

Mangipudi B. Evaluating exact and approximate algorithms for integer linear programming formulations of MAP inference. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2017. [cited 2020 Sep 27]. Available from: http://hdl.handle.net/2142/99111.

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

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Mangipudi B. Evaluating exact and approximate algorithms for integer linear programming formulations of MAP inference. [Thesis]. University of Illinois – Urbana-Champaign; 2017. Available from: http://hdl.handle.net/2142/99111

Not specified: Masters Thesis or Doctoral Dissertation

University of Utah

10.
Liu, Wei.
Resting state functional magnetic resonance imaging analysis by *graphical* model.

Degree: PhD, Computing (School of), 2014, University of Utah

URL: http://content.lib.utah.edu/cdm/singleitem/collection/etd3/id/3112/rec/2102

► Functional magnetic resonance imaging (fMRI) measures the change of oxygen consumption level in the blood vessels of the human brain, hence indirectly detecting the neuronal…
(more)

Subjects/Keywords: Brain connectivity; Functional MRI; Graphical models

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APA (6^{th} Edition):

Liu, W. (2014). Resting state functional magnetic resonance imaging analysis by graphical model. (Doctoral Dissertation). University of Utah. Retrieved from http://content.lib.utah.edu/cdm/singleitem/collection/etd3/id/3112/rec/2102

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

Liu, Wei. “Resting state functional magnetic resonance imaging analysis by graphical model.” 2014. Doctoral Dissertation, University of Utah. Accessed September 27, 2020. http://content.lib.utah.edu/cdm/singleitem/collection/etd3/id/3112/rec/2102.

MLA Handbook (7^{th} Edition):

Liu, Wei. “Resting state functional magnetic resonance imaging analysis by graphical model.” 2014. Web. 27 Sep 2020.

Vancouver:

Liu W. Resting state functional magnetic resonance imaging analysis by graphical model. [Internet] [Doctoral dissertation]. University of Utah; 2014. [cited 2020 Sep 27]. Available from: http://content.lib.utah.edu/cdm/singleitem/collection/etd3/id/3112/rec/2102.

Council of Science Editors:

Liu W. Resting state functional magnetic resonance imaging analysis by graphical model. [Doctoral Dissertation]. University of Utah; 2014. Available from: http://content.lib.utah.edu/cdm/singleitem/collection/etd3/id/3112/rec/2102

University of Rochester

11.
Mahalanabis, Satyaki.
Subset and sample selection for *graphical* *models*:
Gaussian processes, Ising *models* and Gaussian mixture
* models*.

Degree: PhD, 2012, University of Rochester

URL: http://hdl.handle.net/1802/25118

► Probabilistic *Graphical* *Models* are a popular method of representing complex joint distributions in which stochastic dependence between subsets of random variables is expressed in terms…
(more)

Subjects/Keywords: Graphical models; Sample selection; Subset selection

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

APA (6^{th} Edition):

Mahalanabis, S. (2012). Subset and sample selection for graphical models: Gaussian processes, Ising models and Gaussian mixture models. (Doctoral Dissertation). University of Rochester. Retrieved from http://hdl.handle.net/1802/25118

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

Mahalanabis, Satyaki. “Subset and sample selection for graphical models: Gaussian processes, Ising models and Gaussian mixture models.” 2012. Doctoral Dissertation, University of Rochester. Accessed September 27, 2020. http://hdl.handle.net/1802/25118.

MLA Handbook (7^{th} Edition):

Mahalanabis, Satyaki. “Subset and sample selection for graphical models: Gaussian processes, Ising models and Gaussian mixture models.” 2012. Web. 27 Sep 2020.

Vancouver:

Mahalanabis S. Subset and sample selection for graphical models: Gaussian processes, Ising models and Gaussian mixture models. [Internet] [Doctoral dissertation]. University of Rochester; 2012. [cited 2020 Sep 27]. Available from: http://hdl.handle.net/1802/25118.

Council of Science Editors:

Mahalanabis S. Subset and sample selection for graphical models: Gaussian processes, Ising models and Gaussian mixture models. [Doctoral Dissertation]. University of Rochester; 2012. Available from: http://hdl.handle.net/1802/25118

Australian National University

12.
Mohasel Afshar, Hadi.
Probabilistic Inference in Piecewise *Graphical* * Models*
.

Degree: 2016, Australian National University

URL: http://hdl.handle.net/1885/107386

► In many applications of probabilistic inference the *models* contain piecewise densities that are differentiable except at partition boundaries. For instance, (1) some *models* may intrinsically…
(more)

Subjects/Keywords: Piecewise; Graphical models; probabilistic inference; MCMC; sampling

Record Details Similar Records

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

APA (6^{th} Edition):

Mohasel Afshar, H. (2016). Probabilistic Inference in Piecewise Graphical Models . (Thesis). Australian National University. Retrieved from http://hdl.handle.net/1885/107386

Not specified: Masters Thesis or Doctoral Dissertation

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

Mohasel Afshar, Hadi. “Probabilistic Inference in Piecewise Graphical Models .” 2016. Thesis, Australian National University. Accessed September 27, 2020. http://hdl.handle.net/1885/107386.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Mohasel Afshar, Hadi. “Probabilistic Inference in Piecewise Graphical Models .” 2016. Web. 27 Sep 2020.

Vancouver:

Mohasel Afshar H. Probabilistic Inference in Piecewise Graphical Models . [Internet] [Thesis]. Australian National University; 2016. [cited 2020 Sep 27]. Available from: http://hdl.handle.net/1885/107386.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Mohasel Afshar H. Probabilistic Inference in Piecewise Graphical Models . [Thesis]. Australian National University; 2016. Available from: http://hdl.handle.net/1885/107386

Not specified: Masters Thesis or Doctoral Dissertation

13. Tezcan, Hasan Gokhan. A Molecular Modeling Toolkit with Applications to Efficient Free Energy Computation.

Degree: MSin Electrical and Computer Engineering (M.S.E.C.E.), Electrical & Computer Engineering, 2010, U of Massachusetts : Masters

URL: http://scholarworks.umass.edu/theses/482

► In this thesis we develop a molecular modeling toolkit that *models* the conformation space of proteins and allows easy prototyping of algorithms on the conformation…
(more)

Subjects/Keywords: proteins; free energy; conformation space; graphical models

Record Details Similar Records

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

APA (6^{th} Edition):

Tezcan, H. G. (2010). A Molecular Modeling Toolkit with Applications to Efficient Free Energy Computation. (Masters Thesis). U of Massachusetts : Masters. Retrieved from http://scholarworks.umass.edu/theses/482

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

Tezcan, Hasan Gokhan. “A Molecular Modeling Toolkit with Applications to Efficient Free Energy Computation.” 2010. Masters Thesis, U of Massachusetts : Masters. Accessed September 27, 2020. http://scholarworks.umass.edu/theses/482.

MLA Handbook (7^{th} Edition):

Tezcan, Hasan Gokhan. “A Molecular Modeling Toolkit with Applications to Efficient Free Energy Computation.” 2010. Web. 27 Sep 2020.

Vancouver:

Tezcan HG. A Molecular Modeling Toolkit with Applications to Efficient Free Energy Computation. [Internet] [Masters thesis]. U of Massachusetts : Masters; 2010. [cited 2020 Sep 27]. Available from: http://scholarworks.umass.edu/theses/482.

Council of Science Editors:

Tezcan HG. A Molecular Modeling Toolkit with Applications to Efficient Free Energy Computation. [Masters Thesis]. U of Massachusetts : Masters; 2010. Available from: http://scholarworks.umass.edu/theses/482

14. Charbonnier, Camille. Inférence de réseaux de régulation génétique à partir de données du transcriptome non indépendamment et indentiquement distribuées : Inference of gene regulatory networks from non independently and identically distributed transcriptomic data.

Degree: Docteur es, Mathématiques appliquées, 2012, Evry-Val d'Essonne

URL: http://www.theses.fr/2012EVRY0022

►

Cette thèse étudie l'inférence de modèles graphiques Gaussiens en grande dimension à partir de données du transcriptome non indépendamment et identiquement distribuées dans l'objectif d'estimer… (more)

Subjects/Keywords: Modèles graphiques Gaussiens; Gaussian graphical models

Record Details Similar Records

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

APA (6^{th} Edition):

Charbonnier, C. (2012). Inférence de réseaux de régulation génétique à partir de données du transcriptome non indépendamment et indentiquement distribuées : Inference of gene regulatory networks from non independently and identically distributed transcriptomic data. (Doctoral Dissertation). Evry-Val d'Essonne. Retrieved from http://www.theses.fr/2012EVRY0022

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

Charbonnier, Camille. “Inférence de réseaux de régulation génétique à partir de données du transcriptome non indépendamment et indentiquement distribuées : Inference of gene regulatory networks from non independently and identically distributed transcriptomic data.” 2012. Doctoral Dissertation, Evry-Val d'Essonne. Accessed September 27, 2020. http://www.theses.fr/2012EVRY0022.

MLA Handbook (7^{th} Edition):

Charbonnier, Camille. “Inférence de réseaux de régulation génétique à partir de données du transcriptome non indépendamment et indentiquement distribuées : Inference of gene regulatory networks from non independently and identically distributed transcriptomic data.” 2012. Web. 27 Sep 2020.

Vancouver:

Charbonnier C. Inférence de réseaux de régulation génétique à partir de données du transcriptome non indépendamment et indentiquement distribuées : Inference of gene regulatory networks from non independently and identically distributed transcriptomic data. [Internet] [Doctoral dissertation]. Evry-Val d'Essonne; 2012. [cited 2020 Sep 27]. Available from: http://www.theses.fr/2012EVRY0022.

Council of Science Editors:

Charbonnier C. Inférence de réseaux de régulation génétique à partir de données du transcriptome non indépendamment et indentiquement distribuées : Inference of gene regulatory networks from non independently and identically distributed transcriptomic data. [Doctoral Dissertation]. Evry-Val d'Essonne; 2012. Available from: http://www.theses.fr/2012EVRY0022

University of Adelaide

15. Webb, Michael Roy. New methodologies for modelling individual differences in cognition.

Degree: 2010, University of Adelaide

URL: http://hdl.handle.net/2440/62779

► Many evaluations of cognitive *models* rely on data that have been averaged or aggregated across all experimental subjects, and so fail to consider the possibility…
(more)

Subjects/Keywords: cognitive models; individual differences; Bayesian graphical modelling

Record Details Similar Records

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

APA (6^{th} Edition):

Webb, M. R. (2010). New methodologies for modelling individual differences in cognition. (Thesis). University of Adelaide. Retrieved from http://hdl.handle.net/2440/62779

Not specified: Masters Thesis or Doctoral Dissertation

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

Webb, Michael Roy. “New methodologies for modelling individual differences in cognition.” 2010. Thesis, University of Adelaide. Accessed September 27, 2020. http://hdl.handle.net/2440/62779.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Webb, Michael Roy. “New methodologies for modelling individual differences in cognition.” 2010. Web. 27 Sep 2020.

Vancouver:

Webb MR. New methodologies for modelling individual differences in cognition. [Internet] [Thesis]. University of Adelaide; 2010. [cited 2020 Sep 27]. Available from: http://hdl.handle.net/2440/62779.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Webb MR. New methodologies for modelling individual differences in cognition. [Thesis]. University of Adelaide; 2010. Available from: http://hdl.handle.net/2440/62779

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

Record Details Similar Records

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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 September 27, 2020. http://hdl.handle.net/11299/165147.

MLA Handbook (7^{th} Edition):

Zhu, Yunzhang. “Grouping penalties and its applications to high-dimensional models.” 2014. Web. 27 Sep 2020.

Vancouver:

Zhu Y. Grouping penalties and its applications to high-dimensional models. [Internet] [Doctoral dissertation]. University of Minnesota; 2014. [cited 2020 Sep 27]. 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

Texas A&M University

17.
Talluri, Rajesh.
Bayesian Gaussian *Graphical* *models* using sparse selection priors and their mixtures.

Degree: PhD, Statistics, 2012, Texas A&M University

URL: http://hdl.handle.net/1969.1/ETD-TAMU-2011-08-9828

► We propose Bayesian methods for estimating the precision matrix in Gaussian *graphical* *models*. The methods lead to sparse and adaptively shrunk estimators of the precision…
(more)

Subjects/Keywords: Bayesian; Gaussian Graphical Models; Covariance Selection; Mixture Models

Record Details Similar Records

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

APA (6^{th} Edition):

Talluri, R. (2012). Bayesian Gaussian Graphical models using sparse selection priors and their mixtures. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/ETD-TAMU-2011-08-9828

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

Talluri, Rajesh. “Bayesian Gaussian Graphical models using sparse selection priors and their mixtures.” 2012. Doctoral Dissertation, Texas A&M University. Accessed September 27, 2020. http://hdl.handle.net/1969.1/ETD-TAMU-2011-08-9828.

MLA Handbook (7^{th} Edition):

Talluri, Rajesh. “Bayesian Gaussian Graphical models using sparse selection priors and their mixtures.” 2012. Web. 27 Sep 2020.

Vancouver:

Talluri R. Bayesian Gaussian Graphical models using sparse selection priors and their mixtures. [Internet] [Doctoral dissertation]. Texas A&M University; 2012. [cited 2020 Sep 27]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2011-08-9828.

Council of Science Editors:

Talluri R. Bayesian Gaussian Graphical models using sparse selection priors and their mixtures. [Doctoral Dissertation]. Texas A&M University; 2012. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2011-08-9828

18.
-4493-3358.
Appropriate, accessible and appealing probabilistic *graphical* * models*.

Degree: PhD, Computer Science, 2017, University of Texas – Austin

URL: http://hdl.handle.net/2152/62986

► Appropriate - Many multivariate probabilistic *models* either use independent distributions or dependent Gaussian distributions. Yet, many real-world datasets contain count-valued or non-negative skewed data, e.g.…
(more)

Subjects/Keywords: Graphical models; Topic models; Poisson; Count data; Visualization; Human computer interaction

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

APA (6^{th} Edition):

-4493-3358. (2017). Appropriate, accessible and appealing probabilistic graphical models. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/62986

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

Author name may be incomplete

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

-4493-3358. “Appropriate, accessible and appealing probabilistic graphical models.” 2017. Doctoral Dissertation, University of Texas – Austin. Accessed September 27, 2020. http://hdl.handle.net/2152/62986.

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

Author name may be incomplete

MLA Handbook (7^{th} Edition):

-4493-3358. “Appropriate, accessible and appealing probabilistic graphical models.” 2017. Web. 27 Sep 2020.

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

Author name may be incomplete

Vancouver:

-4493-3358. Appropriate, accessible and appealing probabilistic graphical models. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2017. [cited 2020 Sep 27]. Available from: http://hdl.handle.net/2152/62986.

Author name may be incomplete

Council of Science Editors:

-4493-3358. Appropriate, accessible and appealing probabilistic graphical models. [Doctoral Dissertation]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/62986

Author name may be incomplete

University of Illinois – Urbana-Champaign

19.
Huang, David.
A version control interface for *graphical* discrete-event * models*.

Degree: MS, Electrical & Computer Engr, 2019, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/104946

► Möbius is a discrete event modeling tool used for the creation and analysis of complex system *models*. Such *models* are typically *graphical* in nature and…
(more)

Subjects/Keywords: Discrete-event models Version control Graphical models Visualization

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

APA (6^{th} Edition):

Huang, D. (2019). A version control interface for graphical discrete-event models. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/104946

Not specified: Masters Thesis or Doctoral Dissertation

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

Huang, David. “A version control interface for graphical discrete-event models.” 2019. Thesis, University of Illinois – Urbana-Champaign. Accessed September 27, 2020. http://hdl.handle.net/2142/104946.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Huang, David. “A version control interface for graphical discrete-event models.” 2019. Web. 27 Sep 2020.

Vancouver:

Huang D. A version control interface for graphical discrete-event models. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2019. [cited 2020 Sep 27]. Available from: http://hdl.handle.net/2142/104946.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Huang D. A version control interface for graphical discrete-event models. [Thesis]. University of Illinois – Urbana-Champaign; 2019. Available from: http://hdl.handle.net/2142/104946

Not specified: Masters Thesis or Doctoral Dissertation

Penn State University

20.
Butt, Asad Anwar.
Multi-target Tracking Using Higher-order Motion * Models*.

Degree: 2013, Penn State University

URL: https://submit-etda.libraries.psu.edu/catalog/19121

► Multi-target tracking is a significant and challenging problem. In its general form it is known to be NP-hard, and many approximate sub-optimal solution methods have…
(more)

Subjects/Keywords: multi-target tracking; graphical models; higher-order models; motion models; min-cost network flow

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

APA (6^{th} Edition):

Butt, A. A. (2013). Multi-target Tracking Using Higher-order Motion Models. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/19121

Not specified: Masters Thesis or Doctoral Dissertation

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

Butt, Asad Anwar. “Multi-target Tracking Using Higher-order Motion Models.” 2013. Thesis, Penn State University. Accessed September 27, 2020. https://submit-etda.libraries.psu.edu/catalog/19121.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Butt, Asad Anwar. “Multi-target Tracking Using Higher-order Motion Models.” 2013. Web. 27 Sep 2020.

Vancouver:

Butt AA. Multi-target Tracking Using Higher-order Motion Models. [Internet] [Thesis]. Penn State University; 2013. [cited 2020 Sep 27]. Available from: https://submit-etda.libraries.psu.edu/catalog/19121.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Butt AA. Multi-target Tracking Using Higher-order Motion Models. [Thesis]. Penn State University; 2013. Available from: https://submit-etda.libraries.psu.edu/catalog/19121

Not specified: Masters Thesis or Doctoral Dissertation

21. Belilovsky, Eugene. Apprentissage de graphes structuré et parcimonieux dans des données de haute dimension avec applications à l’imagerie cérébrale : Structured Sparse Learning on Graphs in High-Dimensional Data with Applications to NeuroImaging.

Degree: Docteur es, Traitement du signal et des images, 2018, Université Paris-Saclay (ComUE); Katholieke Universiteit Leuven

URL: http://www.theses.fr/2018SACLC027

►

Cette thèse présente de nouvelles méthodes d’apprentissage structuré et parcimonieux sur les graphes, ce qui permet de résoudre une large variété de problèmes d’imagerie cérébrale,… (more)

Subjects/Keywords: Apprentissage statistique; Gaussian graphical models; Neuroimagerie; Apprentissage profond; Machine learning; Gaussian graphical models; Neuroimaging; Deep learning

Record Details Similar Records

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

APA (6^{th} Edition):

Belilovsky, E. (2018). Apprentissage de graphes structuré et parcimonieux dans des données de haute dimension avec applications à l’imagerie cérébrale : Structured Sparse Learning on Graphs in High-Dimensional Data with Applications to NeuroImaging. (Doctoral Dissertation). Université Paris-Saclay (ComUE); Katholieke Universiteit Leuven. Retrieved from http://www.theses.fr/2018SACLC027

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

Belilovsky, Eugene. “Apprentissage de graphes structuré et parcimonieux dans des données de haute dimension avec applications à l’imagerie cérébrale : Structured Sparse Learning on Graphs in High-Dimensional Data with Applications to NeuroImaging.” 2018. Doctoral Dissertation, Université Paris-Saclay (ComUE); Katholieke Universiteit Leuven. Accessed September 27, 2020. http://www.theses.fr/2018SACLC027.

MLA Handbook (7^{th} Edition):

Belilovsky, Eugene. “Apprentissage de graphes structuré et parcimonieux dans des données de haute dimension avec applications à l’imagerie cérébrale : Structured Sparse Learning on Graphs in High-Dimensional Data with Applications to NeuroImaging.” 2018. Web. 27 Sep 2020.

Vancouver:

Belilovsky E. Apprentissage de graphes structuré et parcimonieux dans des données de haute dimension avec applications à l’imagerie cérébrale : Structured Sparse Learning on Graphs in High-Dimensional Data with Applications to NeuroImaging. [Internet] [Doctoral dissertation]. Université Paris-Saclay (ComUE); Katholieke Universiteit Leuven; 2018. [cited 2020 Sep 27]. Available from: http://www.theses.fr/2018SACLC027.

Council of Science Editors:

Belilovsky E. Apprentissage de graphes structuré et parcimonieux dans des données de haute dimension avec applications à l’imagerie cérébrale : Structured Sparse Learning on Graphs in High-Dimensional Data with Applications to NeuroImaging. [Doctoral Dissertation]. Université Paris-Saclay (ComUE); Katholieke Universiteit Leuven; 2018. Available from: http://www.theses.fr/2018SACLC027

22.
Grzegorczyk, Marco.
Comparative evaluation of
different *graphical* *models* for the analysis of gene expression
data.

Degree: 2006, Technische Universität Dortmund

URL: http://hdl.handle.net/2003/22855

► An important problem in systems biology is to infer the architecture of gene regulatory networks and biochemical pathways from postgenomic data. Various reverse engineering methods…
(more)

Subjects/Keywords: Bayesian networks; Gene regulatory networks; Graphical Gaussian models; Relevance networks; 310

Record Details Similar Records

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

APA (6^{th} Edition):

Grzegorczyk, M. (2006). Comparative evaluation of different graphical models for the analysis of gene expression data. (Thesis). Technische Universität Dortmund. Retrieved from http://hdl.handle.net/2003/22855

Not specified: Masters Thesis or Doctoral Dissertation

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

Grzegorczyk, Marco. “Comparative evaluation of different graphical models for the analysis of gene expression data.” 2006. Thesis, Technische Universität Dortmund. Accessed September 27, 2020. http://hdl.handle.net/2003/22855.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Grzegorczyk, Marco. “Comparative evaluation of different graphical models for the analysis of gene expression data.” 2006. Web. 27 Sep 2020.

Vancouver:

Grzegorczyk M. Comparative evaluation of different graphical models for the analysis of gene expression data. [Internet] [Thesis]. Technische Universität Dortmund; 2006. [cited 2020 Sep 27]. Available from: http://hdl.handle.net/2003/22855.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Grzegorczyk M. Comparative evaluation of different graphical models for the analysis of gene expression data. [Thesis]. Technische Universität Dortmund; 2006. Available from: http://hdl.handle.net/2003/22855

Not specified: Masters Thesis or Doctoral Dissertation

The Ohio State University

23.
Ma, Yi.
Learning for Spoken Dialog Systems with Discriminative
*Graphical* * Models*.

Degree: PhD, Computer Science and Engineering, 2015, The Ohio State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1440166760

► A statistical spoken dialog system must keep track of what the user wants at any point during a dialog. The system has the ability to…
(more)

Subjects/Keywords: Computer Science; spoken dialog systems; discriminative graphical models

Record Details Similar Records

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

APA (6^{th} Edition):

Ma, Y. (2015). Learning for Spoken Dialog Systems with Discriminative Graphical Models. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1440166760

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

Ma, Yi. “Learning for Spoken Dialog Systems with Discriminative Graphical Models.” 2015. Doctoral Dissertation, The Ohio State University. Accessed September 27, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1440166760.

MLA Handbook (7^{th} Edition):

Ma, Yi. “Learning for Spoken Dialog Systems with Discriminative Graphical Models.” 2015. Web. 27 Sep 2020.

Vancouver:

Ma Y. Learning for Spoken Dialog Systems with Discriminative Graphical Models. [Internet] [Doctoral dissertation]. The Ohio State University; 2015. [cited 2020 Sep 27]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1440166760.

Council of Science Editors:

Ma Y. Learning for Spoken Dialog Systems with Discriminative Graphical Models. [Doctoral Dissertation]. The Ohio State University; 2015. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1440166760

Carnegie Mellon University

24.
Wei, Wei.
Probabilistic *Models* of Topics and Social Events.

Degree: 2016, Carnegie Mellon University

URL: http://repository.cmu.edu/dissertations/941

► Structured probabilistic inference has shown to be useful in modeling complex latent structures of data. One successful way in which this technique has been applied…
(more)

Subjects/Keywords: Machine Learning; Topic Modeling; Graphical Models; Non-parametric Bayesian; Text Mining

Record Details Similar Records

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

APA (6^{th} Edition):

Wei, W. (2016). Probabilistic Models of Topics and Social Events. (Thesis). Carnegie Mellon University. Retrieved from http://repository.cmu.edu/dissertations/941

Not specified: Masters Thesis or Doctoral Dissertation

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

Wei, Wei. “Probabilistic Models of Topics and Social Events.” 2016. Thesis, Carnegie Mellon University. Accessed September 27, 2020. http://repository.cmu.edu/dissertations/941.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Wei, Wei. “Probabilistic Models of Topics and Social Events.” 2016. Web. 27 Sep 2020.

Vancouver:

Wei W. Probabilistic Models of Topics and Social Events. [Internet] [Thesis]. Carnegie Mellon University; 2016. [cited 2020 Sep 27]. Available from: http://repository.cmu.edu/dissertations/941.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wei W. Probabilistic Models of Topics and Social Events. [Thesis]. Carnegie Mellon University; 2016. Available from: http://repository.cmu.edu/dissertations/941

Not specified: Masters Thesis or Doctoral Dissertation

University of California – Santa Cruz

25. Ng, Sam. Identifying Key Pathways in Multiple Cancers with Multi-omics Pathway Analysis.

Degree: Biomolecular Engineering and Bioinformatics, 2015, University of California – Santa Cruz

URL: http://www.escholarship.org/uc/item/7j0151v4

► Since response to therapy can differ greatly between cancer patients, a precision medicine approach to treating cancer based on the uniqueness of patient tumors could…
(more)

Subjects/Keywords: Bioinformatics; Bioinformatics; Cancer; Graphical Models; Precision Medicine; Systems Biology; TCGA

Record Details Similar Records

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

APA (6^{th} Edition):

Ng, S. (2015). Identifying Key Pathways in Multiple Cancers with Multi-omics Pathway Analysis. (Thesis). University of California – Santa Cruz. Retrieved from http://www.escholarship.org/uc/item/7j0151v4

Not specified: Masters Thesis or Doctoral Dissertation

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

Ng, Sam. “Identifying Key Pathways in Multiple Cancers with Multi-omics Pathway Analysis.” 2015. Thesis, University of California – Santa Cruz. Accessed September 27, 2020. http://www.escholarship.org/uc/item/7j0151v4.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Ng, Sam. “Identifying Key Pathways in Multiple Cancers with Multi-omics Pathway Analysis.” 2015. Web. 27 Sep 2020.

Vancouver:

Ng S. Identifying Key Pathways in Multiple Cancers with Multi-omics Pathway Analysis. [Internet] [Thesis]. University of California – Santa Cruz; 2015. [cited 2020 Sep 27]. Available from: http://www.escholarship.org/uc/item/7j0151v4.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ng S. Identifying Key Pathways in Multiple Cancers with Multi-omics Pathway Analysis. [Thesis]. University of California – Santa Cruz; 2015. Available from: http://www.escholarship.org/uc/item/7j0151v4

Not specified: Masters Thesis or Doctoral Dissertation

Cornell University

26. Fix, Alexander Jobe. GRAPH CUTS, SUM-OF-SUBMODULAR FLOW, AND LINEAR PROGRAMMING: EFFECTIVE INFERENCE IN HIGHER-ORDER MARKOV RANDOM FIELDS.

Degree: PhD, Computer Science, 2017, Cornell University

URL: http://hdl.handle.net/1813/51592

► Optimization algorithms have a long history of success in computer vision, providing effective algorithms for tasks as varied as segmentation, stereo estimation, image denoising and…
(more)

Subjects/Keywords: Graphical Models; Markov Random Fields; Optimization; Computer science

Record Details Similar Records

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

APA (6^{th} Edition):

Fix, A. J. (2017). GRAPH CUTS, SUM-OF-SUBMODULAR FLOW, AND LINEAR PROGRAMMING: EFFECTIVE INFERENCE IN HIGHER-ORDER MARKOV RANDOM FIELDS. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/51592

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

Fix, Alexander Jobe. “GRAPH CUTS, SUM-OF-SUBMODULAR FLOW, AND LINEAR PROGRAMMING: EFFECTIVE INFERENCE IN HIGHER-ORDER MARKOV RANDOM FIELDS.” 2017. Doctoral Dissertation, Cornell University. Accessed September 27, 2020. http://hdl.handle.net/1813/51592.

MLA Handbook (7^{th} Edition):

Fix, Alexander Jobe. “GRAPH CUTS, SUM-OF-SUBMODULAR FLOW, AND LINEAR PROGRAMMING: EFFECTIVE INFERENCE IN HIGHER-ORDER MARKOV RANDOM FIELDS.” 2017. Web. 27 Sep 2020.

Vancouver:

Fix AJ. GRAPH CUTS, SUM-OF-SUBMODULAR FLOW, AND LINEAR PROGRAMMING: EFFECTIVE INFERENCE IN HIGHER-ORDER MARKOV RANDOM FIELDS. [Internet] [Doctoral dissertation]. Cornell University; 2017. [cited 2020 Sep 27]. Available from: http://hdl.handle.net/1813/51592.

Council of Science Editors:

Fix AJ. GRAPH CUTS, SUM-OF-SUBMODULAR FLOW, AND LINEAR PROGRAMMING: EFFECTIVE INFERENCE IN HIGHER-ORDER MARKOV RANDOM FIELDS. [Doctoral Dissertation]. Cornell University; 2017. Available from: http://hdl.handle.net/1813/51592

Cornell University

27.
Logsdon, Benjamin.
Sparse Model Building From Genome-Wide Variation With *Graphical* * Models*.

Degree: PhD, Computational Biology, 2011, Cornell University

URL: http://hdl.handle.net/1813/29227

► High throughput sequencing and expression characterization have lead to an explosion of phenotypic and genotypic molecular data underlying both experimental studies and outbred populations. We…
(more)

Subjects/Keywords: Variational Bayes; Gene expression network reconstruction; Graphical models

Record Details Similar Records

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

APA (6^{th} Edition):

Logsdon, B. (2011). Sparse Model Building From Genome-Wide Variation With Graphical Models. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/29227

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

Logsdon, Benjamin. “Sparse Model Building From Genome-Wide Variation With Graphical Models.” 2011. Doctoral Dissertation, Cornell University. Accessed September 27, 2020. http://hdl.handle.net/1813/29227.

MLA Handbook (7^{th} Edition):

Logsdon, Benjamin. “Sparse Model Building From Genome-Wide Variation With Graphical Models.” 2011. Web. 27 Sep 2020.

Vancouver:

Logsdon B. Sparse Model Building From Genome-Wide Variation With Graphical Models. [Internet] [Doctoral dissertation]. Cornell University; 2011. [cited 2020 Sep 27]. Available from: http://hdl.handle.net/1813/29227.

Council of Science Editors:

Logsdon B. Sparse Model Building From Genome-Wide Variation With Graphical Models. [Doctoral Dissertation]. Cornell University; 2011. Available from: http://hdl.handle.net/1813/29227

Penn State University

28.
Srinivas, Umamahesh.
Discriminative *models* for robust image classification.

Degree: 2013, Penn State University

URL: https://submit-etda.libraries.psu.edu/catalog/19014

► A variety of real-world tasks involve the classification of images into pre-determined categories. Designing image classification algorithms that exhibit robustness to acquisition noise and image…
(more)

Subjects/Keywords: Robust image classification; image processing; graphical models; sparse signal representations.

Record Details Similar Records

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

APA (6^{th} Edition):

Srinivas, U. (2013). Discriminative models for robust image classification. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/19014

Not specified: Masters Thesis or Doctoral Dissertation

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

Srinivas, Umamahesh. “Discriminative models for robust image classification.” 2013. Thesis, Penn State University. Accessed September 27, 2020. https://submit-etda.libraries.psu.edu/catalog/19014.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Srinivas, Umamahesh. “Discriminative models for robust image classification.” 2013. Web. 27 Sep 2020.

Vancouver:

Srinivas U. Discriminative models for robust image classification. [Internet] [Thesis]. Penn State University; 2013. [cited 2020 Sep 27]. Available from: https://submit-etda.libraries.psu.edu/catalog/19014.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Srinivas U. Discriminative models for robust image classification. [Thesis]. Penn State University; 2013. Available from: https://submit-etda.libraries.psu.edu/catalog/19014

Not specified: Masters Thesis or Doctoral Dissertation

University of Waterloo

29. Jaini, Priyank. Likelihood-based Density Estimation using Deep Architectures.

Degree: 2019, University of Waterloo

URL: http://hdl.handle.net/10012/15356

► Multivariate density estimation is a central problem in unsupervised machine learning that has been studied immensely in both statistics and machine learning. Several methods have…
(more)

Subjects/Keywords: machine learning; unsupervised learning; deep learning; probabilitic graphical models

Record Details Similar Records

❌

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

APA (6^{th} Edition):

Jaini, P. (2019). Likelihood-based Density Estimation using Deep Architectures. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/15356

Not specified: Masters Thesis or Doctoral Dissertation

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

Jaini, Priyank. “Likelihood-based Density Estimation using Deep Architectures.” 2019. Thesis, University of Waterloo. Accessed September 27, 2020. http://hdl.handle.net/10012/15356.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Jaini, Priyank. “Likelihood-based Density Estimation using Deep Architectures.” 2019. Web. 27 Sep 2020.

Vancouver:

Jaini P. Likelihood-based Density Estimation using Deep Architectures. [Internet] [Thesis]. University of Waterloo; 2019. [cited 2020 Sep 27]. Available from: http://hdl.handle.net/10012/15356.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Jaini P. Likelihood-based Density Estimation using Deep Architectures. [Thesis]. University of Waterloo; 2019. Available from: http://hdl.handle.net/10012/15356

Not specified: Masters Thesis or Doctoral Dissertation

Penn State University

30. Lee, Sanghack. Causal Discovery from Relational Data: Theory and Practice.

Degree: 2018, Penn State University

URL: https://submit-etda.libraries.psu.edu/catalog/14976sxl439

► Discovery of causal relationships from observational and experimental data is a central problem with applications across multiple areas of scientific endeavor. There has been considerable…
(more)

Subjects/Keywords: Causality; Causal Model; Graphical Models; Relational Data; Relational Model

Record Details Similar Records

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

APA (6^{th} Edition):

Lee, S. (2018). Causal Discovery from Relational Data: Theory and Practice. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/14976sxl439

Not specified: Masters Thesis or Doctoral Dissertation

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

Lee, Sanghack. “Causal Discovery from Relational Data: Theory and Practice.” 2018. Thesis, Penn State University. Accessed September 27, 2020. https://submit-etda.libraries.psu.edu/catalog/14976sxl439.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Lee, Sanghack. “Causal Discovery from Relational Data: Theory and Practice.” 2018. Web. 27 Sep 2020.

Vancouver:

Lee S. Causal Discovery from Relational Data: Theory and Practice. [Internet] [Thesis]. Penn State University; 2018. [cited 2020 Sep 27]. Available from: https://submit-etda.libraries.psu.edu/catalog/14976sxl439.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lee S. Causal Discovery from Relational Data: Theory and Practice. [Thesis]. Penn State University; 2018. Available from: https://submit-etda.libraries.psu.edu/catalog/14976sxl439

Not specified: Masters Thesis or Doctoral Dissertation