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You searched for subject:(Graphical Models). Showing records 1 – 30 of 277 total matches.

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

Degree: PhD, Computer Science, 2016, Brown University

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

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 (6th 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 (16th 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 (7th 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

 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

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APA (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

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

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

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APA (6th Edition):

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

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

Chicago Manual of Style (16th Edition):

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

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

MLA Handbook (7th Edition):

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

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

Council of Science Editors:

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

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

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

 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

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APA (6th 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 (16th 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 (7th 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

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

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APA (6th 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 (16th 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 (7th 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

 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

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APA (6th 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

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

Chicago Manual of Style (16th Edition):

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.

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

MLA Handbook (7th 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.

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

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


University of Minnesota

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

Degree: PhD, Statistics, 2014, University of Minnesota

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

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

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APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

Zhu, Yunzhang. “Grouping penalties and its applications to high-dimensional models.” 2014. Doctoral Dissertation, University of Minnesota. Accessed September 27, 2020. http://hdl.handle.net/11299/165147.

MLA Handbook (7th 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

 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

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

APA (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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.

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

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

 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 (6th 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

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

Chicago Manual of Style (16th Edition):

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.

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

MLA Handbook (7th 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.

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

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

 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 (6th 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

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

Chicago Manual of Style (16th Edition):

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.

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

MLA Handbook (7th 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.

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

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

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

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

APA (6th 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 (16th 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 (7th 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

 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

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

APA (6th 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

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

Chicago Manual of Style (16th Edition):

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.

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

MLA Handbook (7th 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.

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

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

 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

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

APA (6th 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 (16th 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 (7th 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

 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

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

APA (6th Edition):

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

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

Chicago Manual of Style (16th Edition):

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

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

MLA Handbook (7th 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.

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

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

 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

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

APA (6th 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

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

Chicago Manual of Style (16th Edition):

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.

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

MLA Handbook (7th 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.

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

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

 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

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APA (6th 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 (16th 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 (7th 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

 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

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APA (6th 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 (16th 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 (7th 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

 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.

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

APA (6th Edition):

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

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

Chicago Manual of Style (16th Edition):

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.

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

MLA Handbook (7th 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.

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

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

 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

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

APA (6th Edition):

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

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

Chicago Manual of Style (16th Edition):

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

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

MLA Handbook (7th 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.

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

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

 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

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

APA (6th 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

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

Chicago Manual of Style (16th Edition):

Lee, 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.

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

MLA Handbook (7th 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.

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

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

[1] [2] [3] [4] [5] [6] [7] [8] [9] [10]

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