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You searched for subject:(Markov Random Fields ). Showing records 1 – 30 of 16155 total matches.

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Michigan State University

1. Kinateder, Kimberly Kay Johannes. Strong Markov properties for Markov random fields.

Degree: PhD, Department of Statistics and Probability, 1990, Michigan State University

Subjects/Keywords: Markov random fields; Markov processes

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

APA (6th Edition):

Kinateder, K. K. J. (1990). Strong Markov properties for Markov random fields. (Doctoral Dissertation). Michigan State University. Retrieved from http://etd.lib.msu.edu/islandora/object/etd:22785

Chicago Manual of Style (16th Edition):

Kinateder, Kimberly Kay Johannes. “Strong Markov properties for Markov random fields.” 1990. Doctoral Dissertation, Michigan State University. Accessed November 30, 2020. http://etd.lib.msu.edu/islandora/object/etd:22785.

MLA Handbook (7th Edition):

Kinateder, Kimberly Kay Johannes. “Strong Markov properties for Markov random fields.” 1990. Web. 30 Nov 2020.

Vancouver:

Kinateder KKJ. Strong Markov properties for Markov random fields. [Internet] [Doctoral dissertation]. Michigan State University; 1990. [cited 2020 Nov 30]. Available from: http://etd.lib.msu.edu/islandora/object/etd:22785.

Council of Science Editors:

Kinateder KKJ. Strong Markov properties for Markov random fields. [Doctoral Dissertation]. Michigan State University; 1990. Available from: http://etd.lib.msu.edu/islandora/object/etd:22785

2. Besbes, Ahmed. Image segmentation using MRFs and statistical shape modeling : Segmentation d'images avec des champs de Markov et modélisation statistique de formes.

Degree: Docteur es, Mathématiques appliquées, 2010, Châtenay-Malabry, Ecole centrale de Paris

Nous présentons dans cette thèse un nouveau modèle statistique de forme et l'utilisons pour la segmentation d'images avec a priori. Ce modèle est représenté par… (more)

Subjects/Keywords: Modélisation de formes; Segmentation; Champs de Markov; Shape Modeling; Segmentation; Markov Random Fields

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APA (6th Edition):

Besbes, A. (2010). Image segmentation using MRFs and statistical shape modeling : Segmentation d'images avec des champs de Markov et modélisation statistique de formes. (Doctoral Dissertation). Châtenay-Malabry, Ecole centrale de Paris. Retrieved from http://www.theses.fr/2010ECAP0024

Chicago Manual of Style (16th Edition):

Besbes, Ahmed. “Image segmentation using MRFs and statistical shape modeling : Segmentation d'images avec des champs de Markov et modélisation statistique de formes.” 2010. Doctoral Dissertation, Châtenay-Malabry, Ecole centrale de Paris. Accessed November 30, 2020. http://www.theses.fr/2010ECAP0024.

MLA Handbook (7th Edition):

Besbes, Ahmed. “Image segmentation using MRFs and statistical shape modeling : Segmentation d'images avec des champs de Markov et modélisation statistique de formes.” 2010. Web. 30 Nov 2020.

Vancouver:

Besbes A. Image segmentation using MRFs and statistical shape modeling : Segmentation d'images avec des champs de Markov et modélisation statistique de formes. [Internet] [Doctoral dissertation]. Châtenay-Malabry, Ecole centrale de Paris; 2010. [cited 2020 Nov 30]. Available from: http://www.theses.fr/2010ECAP0024.

Council of Science Editors:

Besbes A. Image segmentation using MRFs and statistical shape modeling : Segmentation d'images avec des champs de Markov et modélisation statistique de formes. [Doctoral Dissertation]. Châtenay-Malabry, Ecole centrale de Paris; 2010. Available from: http://www.theses.fr/2010ECAP0024

3. Parisot, Sarah. Understanding, Modeling and Detecting Brain Tumors : Graphical Models and Concurrent Segmentation/Registration methods : Compréhension, modélisation et détection de tumeurs cérébrales : modèles graphiques et méthodes de recalage/segmentation simultanés.

Degree: Docteur es, Mathématiques appliquées, 2013, Châtenay-Malabry, Ecole centrale de Paris

L'objectif principal de cette thèse est la modélisation, compréhension et segmentation automatique de tumeurs diffuses et infiltrantes appelées Gliomes Diffus de Bas Grade. Deux approches… (more)

Subjects/Keywords: Champs de Markov Aléatoires; Recalage; Atlas du cerveau; Markov Random Fields; Registration; Brain atlas

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APA (6th Edition):

Parisot, S. (2013). Understanding, Modeling and Detecting Brain Tumors : Graphical Models and Concurrent Segmentation/Registration methods : Compréhension, modélisation et détection de tumeurs cérébrales : modèles graphiques et méthodes de recalage/segmentation simultanés. (Doctoral Dissertation). Châtenay-Malabry, Ecole centrale de Paris. Retrieved from http://www.theses.fr/2013ECAP0064

Chicago Manual of Style (16th Edition):

Parisot, Sarah. “Understanding, Modeling and Detecting Brain Tumors : Graphical Models and Concurrent Segmentation/Registration methods : Compréhension, modélisation et détection de tumeurs cérébrales : modèles graphiques et méthodes de recalage/segmentation simultanés.” 2013. Doctoral Dissertation, Châtenay-Malabry, Ecole centrale de Paris. Accessed November 30, 2020. http://www.theses.fr/2013ECAP0064.

MLA Handbook (7th Edition):

Parisot, Sarah. “Understanding, Modeling and Detecting Brain Tumors : Graphical Models and Concurrent Segmentation/Registration methods : Compréhension, modélisation et détection de tumeurs cérébrales : modèles graphiques et méthodes de recalage/segmentation simultanés.” 2013. Web. 30 Nov 2020.

Vancouver:

Parisot S. Understanding, Modeling and Detecting Brain Tumors : Graphical Models and Concurrent Segmentation/Registration methods : Compréhension, modélisation et détection de tumeurs cérébrales : modèles graphiques et méthodes de recalage/segmentation simultanés. [Internet] [Doctoral dissertation]. Châtenay-Malabry, Ecole centrale de Paris; 2013. [cited 2020 Nov 30]. Available from: http://www.theses.fr/2013ECAP0064.

Council of Science Editors:

Parisot S. Understanding, Modeling and Detecting Brain Tumors : Graphical Models and Concurrent Segmentation/Registration methods : Compréhension, modélisation et détection de tumeurs cérébrales : modèles graphiques et méthodes de recalage/segmentation simultanés. [Doctoral Dissertation]. Châtenay-Malabry, Ecole centrale de Paris; 2013. Available from: http://www.theses.fr/2013ECAP0064


UCLA

4. Xie, Jianwen. Learning Inhomogeneous FRAME Models for Object Patterns.

Degree: Statistics, 2014, UCLA

 This research investigates an inhomogeneous version of the FRAME (Filters, Random field, And Maximum Entropy) model and apply it to modeling object patterns. The inhomogeneous… (more)

Subjects/Keywords: Statistics; Computer science; Generative models; Markov random fields

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APA (6th Edition):

Xie, J. (2014). Learning Inhomogeneous FRAME Models for Object Patterns. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/4367r57k

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):

Xie, Jianwen. “Learning Inhomogeneous FRAME Models for Object Patterns.” 2014. Thesis, UCLA. Accessed November 30, 2020. http://www.escholarship.org/uc/item/4367r57k.

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

MLA Handbook (7th Edition):

Xie, Jianwen. “Learning Inhomogeneous FRAME Models for Object Patterns.” 2014. Web. 30 Nov 2020.

Vancouver:

Xie J. Learning Inhomogeneous FRAME Models for Object Patterns. [Internet] [Thesis]. UCLA; 2014. [cited 2020 Nov 30]. Available from: http://www.escholarship.org/uc/item/4367r57k.

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

Council of Science Editors:

Xie J. Learning Inhomogeneous FRAME Models for Object Patterns. [Thesis]. UCLA; 2014. Available from: http://www.escholarship.org/uc/item/4367r57k

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


Cornell University

5. 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 November 30, 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. 30 Nov 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 Nov 30]. 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

6. Suwanwimolkul, Suwichaya. Adaptive Markov Random Fields for Structured Compressive Sensing.

Degree: 2018, University of Adelaide

 Compressive sensing (CS) has underpinned recent developments in data compression and signal acquisition systems. The goal of CS is to recover a high dimensional sparse… (more)

Subjects/Keywords: Compressive sensing; Markov random fields; structured sparsity model

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APA (6th Edition):

Suwanwimolkul, S. (2018). Adaptive Markov Random Fields for Structured Compressive Sensing. (Thesis). University of Adelaide. Retrieved from http://hdl.handle.net/2440/117806

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):

Suwanwimolkul, Suwichaya. “Adaptive Markov Random Fields for Structured Compressive Sensing.” 2018. Thesis, University of Adelaide. Accessed November 30, 2020. http://hdl.handle.net/2440/117806.

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

MLA Handbook (7th Edition):

Suwanwimolkul, Suwichaya. “Adaptive Markov Random Fields for Structured Compressive Sensing.” 2018. Web. 30 Nov 2020.

Vancouver:

Suwanwimolkul S. Adaptive Markov Random Fields for Structured Compressive Sensing. [Internet] [Thesis]. University of Adelaide; 2018. [cited 2020 Nov 30]. Available from: http://hdl.handle.net/2440/117806.

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

Council of Science Editors:

Suwanwimolkul S. Adaptive Markov Random Fields for Structured Compressive Sensing. [Thesis]. University of Adelaide; 2018. Available from: http://hdl.handle.net/2440/117806

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


Brigham Young University

7. Olsen, Jessica Lyn. An Applied Investigation of Gaussian Markov Random Fields.

Degree: MS, 2012, Brigham Young University

 Recently, Bayesian methods have become the essence of modern statistics, specifically, the ability to incorporate hierarchical models. In particular, correlated data, such as the data… (more)

Subjects/Keywords: Gaussian Markov Random Fields; Spatial; Correlated Data; Statistics and Probability

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APA (6th Edition):

Olsen, J. L. (2012). An Applied Investigation of Gaussian Markov Random Fields. (Masters Thesis). Brigham Young University. Retrieved from https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=4272&context=etd

Chicago Manual of Style (16th Edition):

Olsen, Jessica Lyn. “An Applied Investigation of Gaussian Markov Random Fields.” 2012. Masters Thesis, Brigham Young University. Accessed November 30, 2020. https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=4272&context=etd.

MLA Handbook (7th Edition):

Olsen, Jessica Lyn. “An Applied Investigation of Gaussian Markov Random Fields.” 2012. Web. 30 Nov 2020.

Vancouver:

Olsen JL. An Applied Investigation of Gaussian Markov Random Fields. [Internet] [Masters thesis]. Brigham Young University; 2012. [cited 2020 Nov 30]. Available from: https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=4272&context=etd.

Council of Science Editors:

Olsen JL. An Applied Investigation of Gaussian Markov Random Fields. [Masters Thesis]. Brigham Young University; 2012. Available from: https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=4272&context=etd


Michigan State University

8. Nadabar, Sateesha Gopalakrishna. Markov random field contextual models in computer vision.

Degree: PhD, Department of Computer Science, 1992, Michigan State University

Subjects/Keywords: Computer vision; Markov random fields

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

APA (6th Edition):

Nadabar, S. G. (1992). Markov random field contextual models in computer vision. (Doctoral Dissertation). Michigan State University. Retrieved from http://etd.lib.msu.edu/islandora/object/etd:21893

Chicago Manual of Style (16th Edition):

Nadabar, Sateesha Gopalakrishna. “Markov random field contextual models in computer vision.” 1992. Doctoral Dissertation, Michigan State University. Accessed November 30, 2020. http://etd.lib.msu.edu/islandora/object/etd:21893.

MLA Handbook (7th Edition):

Nadabar, Sateesha Gopalakrishna. “Markov random field contextual models in computer vision.” 1992. Web. 30 Nov 2020.

Vancouver:

Nadabar SG. Markov random field contextual models in computer vision. [Internet] [Doctoral dissertation]. Michigan State University; 1992. [cited 2020 Nov 30]. Available from: http://etd.lib.msu.edu/islandora/object/etd:21893.

Council of Science Editors:

Nadabar SG. Markov random field contextual models in computer vision. [Doctoral Dissertation]. Michigan State University; 1992. Available from: http://etd.lib.msu.edu/islandora/object/etd:21893

9. Kutarnia, Jason Francis. A Markov Random Field Based Approach to 3D Mosaicing and Registration Applied to Ultrasound Simulation.

Degree: PhD, 2014, Worcester Polytechnic Institute

 " A novel Markov Random Field (MRF) based method for the mosaicing of 3D ultrasound volumes is presented in this dissertation. The motivation for this… (more)

Subjects/Keywords: Markov random fields; registration; ultrasound mosaicing; discrete graph based technique

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APA (6th Edition):

Kutarnia, J. F. (2014). A Markov Random Field Based Approach to 3D Mosaicing and Registration Applied to Ultrasound Simulation. (Doctoral Dissertation). Worcester Polytechnic Institute. Retrieved from etd-082714-192730 ; https://digitalcommons.wpi.edu/etd-dissertations/369

Chicago Manual of Style (16th Edition):

Kutarnia, Jason Francis. “A Markov Random Field Based Approach to 3D Mosaicing and Registration Applied to Ultrasound Simulation.” 2014. Doctoral Dissertation, Worcester Polytechnic Institute. Accessed November 30, 2020. etd-082714-192730 ; https://digitalcommons.wpi.edu/etd-dissertations/369.

MLA Handbook (7th Edition):

Kutarnia, Jason Francis. “A Markov Random Field Based Approach to 3D Mosaicing and Registration Applied to Ultrasound Simulation.” 2014. Web. 30 Nov 2020.

Vancouver:

Kutarnia JF. A Markov Random Field Based Approach to 3D Mosaicing and Registration Applied to Ultrasound Simulation. [Internet] [Doctoral dissertation]. Worcester Polytechnic Institute; 2014. [cited 2020 Nov 30]. Available from: etd-082714-192730 ; https://digitalcommons.wpi.edu/etd-dissertations/369.

Council of Science Editors:

Kutarnia JF. A Markov Random Field Based Approach to 3D Mosaicing and Registration Applied to Ultrasound Simulation. [Doctoral Dissertation]. Worcester Polytechnic Institute; 2014. Available from: etd-082714-192730 ; https://digitalcommons.wpi.edu/etd-dissertations/369


Virginia Tech

10. Chaabene, Walid. Scalable Structure Learning of Graphical Models.

Degree: MS, Computer Science and Applications, 2017, Virginia Tech

 Hypothesis-free learning is increasingly popular given the large amounts of data becoming available. Structure learning, a hypothesis-free approach, of graphical models is a field of… (more)

Subjects/Keywords: L1-based Structure Learning; Linear Dynamical Systems; Markov Random Fields

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APA (6th Edition):

Chaabene, W. (2017). Scalable Structure Learning of Graphical Models. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/86263

Chicago Manual of Style (16th Edition):

Chaabene, Walid. “Scalable Structure Learning of Graphical Models.” 2017. Masters Thesis, Virginia Tech. Accessed November 30, 2020. http://hdl.handle.net/10919/86263.

MLA Handbook (7th Edition):

Chaabene, Walid. “Scalable Structure Learning of Graphical Models.” 2017. Web. 30 Nov 2020.

Vancouver:

Chaabene W. Scalable Structure Learning of Graphical Models. [Internet] [Masters thesis]. Virginia Tech; 2017. [cited 2020 Nov 30]. Available from: http://hdl.handle.net/10919/86263.

Council of Science Editors:

Chaabene W. Scalable Structure Learning of Graphical Models. [Masters Thesis]. Virginia Tech; 2017. Available from: http://hdl.handle.net/10919/86263


University of Texas – Austin

11. Yang, Eunho. High-dimensional statistics : model specification and elementary estimators.

Degree: PhD, Computer Science, 2014, University of Texas – Austin

 Modern statistics typically deals with complex data, in particular where the ambient dimension of the problem p may be of the same order as, or… (more)

Subjects/Keywords: High-dimensional statistics; Markov random fields; Graphical models; Closed-form estimators

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APA (6th Edition):

Yang, E. (2014). High-dimensional statistics : model specification and elementary estimators. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/28058

Chicago Manual of Style (16th Edition):

Yang, Eunho. “High-dimensional statistics : model specification and elementary estimators.” 2014. Doctoral Dissertation, University of Texas – Austin. Accessed November 30, 2020. http://hdl.handle.net/2152/28058.

MLA Handbook (7th Edition):

Yang, Eunho. “High-dimensional statistics : model specification and elementary estimators.” 2014. Web. 30 Nov 2020.

Vancouver:

Yang E. High-dimensional statistics : model specification and elementary estimators. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2014. [cited 2020 Nov 30]. Available from: http://hdl.handle.net/2152/28058.

Council of Science Editors:

Yang E. High-dimensional statistics : model specification and elementary estimators. [Doctoral Dissertation]. University of Texas – Austin; 2014. Available from: http://hdl.handle.net/2152/28058


University of Lund

12. Bolin, David. Models and Methods for Random Fields in Spatial Statistics with Computational Efficiency from Markov Properties.

Degree: 2012, University of Lund

 The focus of this work is on the development of new random field models and methods suitable for the analysis of large environmental data sets.… (more)

Subjects/Keywords: Probability Theory and Statistics; random fields; Gaussian Markov random fields; Matérn covariances; stochastic partial differential equations; Computational efficiency

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APA (6th Edition):

Bolin, D. (2012). Models and Methods for Random Fields in Spatial Statistics with Computational Efficiency from Markov Properties. (Doctoral Dissertation). University of Lund. Retrieved from https://lup.lub.lu.se/record/2539400 ; https://portal.research.lu.se/ws/files/4123914/2539412.pdf

Chicago Manual of Style (16th Edition):

Bolin, David. “Models and Methods for Random Fields in Spatial Statistics with Computational Efficiency from Markov Properties.” 2012. Doctoral Dissertation, University of Lund. Accessed November 30, 2020. https://lup.lub.lu.se/record/2539400 ; https://portal.research.lu.se/ws/files/4123914/2539412.pdf.

MLA Handbook (7th Edition):

Bolin, David. “Models and Methods for Random Fields in Spatial Statistics with Computational Efficiency from Markov Properties.” 2012. Web. 30 Nov 2020.

Vancouver:

Bolin D. Models and Methods for Random Fields in Spatial Statistics with Computational Efficiency from Markov Properties. [Internet] [Doctoral dissertation]. University of Lund; 2012. [cited 2020 Nov 30]. Available from: https://lup.lub.lu.se/record/2539400 ; https://portal.research.lu.se/ws/files/4123914/2539412.pdf.

Council of Science Editors:

Bolin D. Models and Methods for Random Fields in Spatial Statistics with Computational Efficiency from Markov Properties. [Doctoral Dissertation]. University of Lund; 2012. Available from: https://lup.lub.lu.se/record/2539400 ; https://portal.research.lu.se/ws/files/4123914/2539412.pdf


University of Illinois – Urbana-Champaign

13. Le, Tung. Probabilistic inference via sum-product algorithms on binary pairwise Gibbs random fields with applications to multiple fault diagnosis.

Degree: PhD, 1200, 2011, University of Illinois – Urbana-Champaign

 In this dissertation, we consider probabilistic inference problems on binary pairwise Gibbs random fields (BPW-GRFs), which belong to a class of Markov random fields with… (more)

Subjects/Keywords: Probabilistic inference; marginal problems; marginal bounds; graphical models; Markov random fields; Gibbs random fields; binary pairwise Gibbs random fields; belief propagation; sum-product algorithms; fault diagnosis

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APA (6th Edition):

Le, T. (2011). Probabilistic inference via sum-product algorithms on binary pairwise Gibbs random fields with applications to multiple fault diagnosis. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/18543

Chicago Manual of Style (16th Edition):

Le, Tung. “Probabilistic inference via sum-product algorithms on binary pairwise Gibbs random fields with applications to multiple fault diagnosis.” 2011. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed November 30, 2020. http://hdl.handle.net/2142/18543.

MLA Handbook (7th Edition):

Le, Tung. “Probabilistic inference via sum-product algorithms on binary pairwise Gibbs random fields with applications to multiple fault diagnosis.” 2011. Web. 30 Nov 2020.

Vancouver:

Le T. Probabilistic inference via sum-product algorithms on binary pairwise Gibbs random fields with applications to multiple fault diagnosis. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2011. [cited 2020 Nov 30]. Available from: http://hdl.handle.net/2142/18543.

Council of Science Editors:

Le T. Probabilistic inference via sum-product algorithms on binary pairwise Gibbs random fields with applications to multiple fault diagnosis. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2011. Available from: http://hdl.handle.net/2142/18543

14. Botelho, Glenda Michele. Segmentação de imagens baseada em redes complexas e superpixels: uma aplicação ao censo de aves.

Degree: PhD, Ciências de Computação e Matemática Computacional, 2014, University of São Paulo

Uma das etapas mais importantes da análise de imagens e, que conta com uma enorme quantidade de aplicações, é a segmentação. No entanto, uma boa… (more)

Subjects/Keywords: Birds census; Censo demográfico de aves; Community detection; Complex networks; Detecção de comunidades; Image segmentation; Markov Random fields; Markov Random fields; Redes complexas; Segmentação de imagens; Superpixels; Superpixels; Textura; Texture

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APA (6th Edition):

Botelho, G. M. (2014). Segmentação de imagens baseada em redes complexas e superpixels: uma aplicação ao censo de aves. (Doctoral Dissertation). University of São Paulo. Retrieved from http://www.teses.usp.br/teses/disponiveis/55/55134/tde-16032015-113613/ ;

Chicago Manual of Style (16th Edition):

Botelho, Glenda Michele. “Segmentação de imagens baseada em redes complexas e superpixels: uma aplicação ao censo de aves.” 2014. Doctoral Dissertation, University of São Paulo. Accessed November 30, 2020. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-16032015-113613/ ;.

MLA Handbook (7th Edition):

Botelho, Glenda Michele. “Segmentação de imagens baseada em redes complexas e superpixels: uma aplicação ao censo de aves.” 2014. Web. 30 Nov 2020.

Vancouver:

Botelho GM. Segmentação de imagens baseada em redes complexas e superpixels: uma aplicação ao censo de aves. [Internet] [Doctoral dissertation]. University of São Paulo; 2014. [cited 2020 Nov 30]. Available from: http://www.teses.usp.br/teses/disponiveis/55/55134/tde-16032015-113613/ ;.

Council of Science Editors:

Botelho GM. Segmentação de imagens baseada em redes complexas e superpixels: uma aplicação ao censo de aves. [Doctoral Dissertation]. University of São Paulo; 2014. Available from: http://www.teses.usp.br/teses/disponiveis/55/55134/tde-16032015-113613/ ;


KTH

15. Gao, Xiaoxu. Exploring declarative rule-based probabilistic frameworks for link prediction in Knowledge Graphs.

Degree: Information and Communication Technology (ICT), 2017, KTH

En kunskapsgraf lagrar information från webben i form av relationer mellan olika entiteter. En kunskapsgrafs kvalité bestäms av hur komplett den är och dess… (more)

Subjects/Keywords: Knowledge Graph; Link Prediction; Probabilistic Soft Logic; Hinge-loss Markov Random Fields; Kunskapsgraf; Länkförutsägelser; Probabilistic Soft Logic; Hinge-loss Markov Random Fields; Computer and Information Sciences; Data- och informationsvetenskap

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

APA (6th Edition):

Gao, X. (2017). Exploring declarative rule-based probabilistic frameworks for link prediction in Knowledge Graphs. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210650

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

Chicago Manual of Style (16th Edition):

Gao, Xiaoxu. “Exploring declarative rule-based probabilistic frameworks for link prediction in Knowledge Graphs.” 2017. Thesis, KTH. Accessed November 30, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210650.

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

MLA Handbook (7th Edition):

Gao, Xiaoxu. “Exploring declarative rule-based probabilistic frameworks for link prediction in Knowledge Graphs.” 2017. Web. 30 Nov 2020.

Vancouver:

Gao X. Exploring declarative rule-based probabilistic frameworks for link prediction in Knowledge Graphs. [Internet] [Thesis]. KTH; 2017. [cited 2020 Nov 30]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210650.

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

Council of Science Editors:

Gao X. Exploring declarative rule-based probabilistic frameworks for link prediction in Knowledge Graphs. [Thesis]. KTH; 2017. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210650

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


University of Rochester

16. Papai, Tivadar (1984 - ). Exploiting constraints, sequential structure, and knowledge in Markov logic networks.

Degree: PhD, 2014, University of Rochester

 In this dissertation we propose extensions to Markov logic networks that can improve inference and learning by exploiting deterministic constraints, expert knowledge or se- quential/temporal… (more)

Subjects/Keywords: Constraint programming; Exponential families; Markov logic; Modal logic; Random fields; Sequential domains

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APA (6th Edition):

Papai, T. (. -. ). (2014). Exploiting constraints, sequential structure, and knowledge in Markov logic networks. (Doctoral Dissertation). University of Rochester. Retrieved from http://hdl.handle.net/1802/28364

Chicago Manual of Style (16th Edition):

Papai, Tivadar (1984 - ). “Exploiting constraints, sequential structure, and knowledge in Markov logic networks.” 2014. Doctoral Dissertation, University of Rochester. Accessed November 30, 2020. http://hdl.handle.net/1802/28364.

MLA Handbook (7th Edition):

Papai, Tivadar (1984 - ). “Exploiting constraints, sequential structure, and knowledge in Markov logic networks.” 2014. Web. 30 Nov 2020.

Vancouver:

Papai T(-). Exploiting constraints, sequential structure, and knowledge in Markov logic networks. [Internet] [Doctoral dissertation]. University of Rochester; 2014. [cited 2020 Nov 30]. Available from: http://hdl.handle.net/1802/28364.

Council of Science Editors:

Papai T(-). Exploiting constraints, sequential structure, and knowledge in Markov logic networks. [Doctoral Dissertation]. University of Rochester; 2014. Available from: http://hdl.handle.net/1802/28364

17. Ziniti, Beth Louise. Computationally Efficient Specifications of Spatial Point Process Models and Spatio-Temporal Gaussian Models: Combining Remote Sensing Drivers with Geospatial Disease Case Data to Enhance Geographic Epidemiology.

Degree: PhD, 2016, University of New Hampshire

  In this dissertation, the flexibility of Bayesian hierarchical models specified using a latent Gaussian Markov Random Field (GMRF) are evaluated for use in analyzing… (more)

Subjects/Keywords: Gaussian Markov Random Fields; spatial epidemiology; spatial point processes; water quality; Statistics

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APA (6th Edition):

Ziniti, B. L. (2016). Computationally Efficient Specifications of Spatial Point Process Models and Spatio-Temporal Gaussian Models: Combining Remote Sensing Drivers with Geospatial Disease Case Data to Enhance Geographic Epidemiology. (Doctoral Dissertation). University of New Hampshire. Retrieved from https://scholars.unh.edu/dissertation/1378

Chicago Manual of Style (16th Edition):

Ziniti, Beth Louise. “Computationally Efficient Specifications of Spatial Point Process Models and Spatio-Temporal Gaussian Models: Combining Remote Sensing Drivers with Geospatial Disease Case Data to Enhance Geographic Epidemiology.” 2016. Doctoral Dissertation, University of New Hampshire. Accessed November 30, 2020. https://scholars.unh.edu/dissertation/1378.

MLA Handbook (7th Edition):

Ziniti, Beth Louise. “Computationally Efficient Specifications of Spatial Point Process Models and Spatio-Temporal Gaussian Models: Combining Remote Sensing Drivers with Geospatial Disease Case Data to Enhance Geographic Epidemiology.” 2016. Web. 30 Nov 2020.

Vancouver:

Ziniti BL. Computationally Efficient Specifications of Spatial Point Process Models and Spatio-Temporal Gaussian Models: Combining Remote Sensing Drivers with Geospatial Disease Case Data to Enhance Geographic Epidemiology. [Internet] [Doctoral dissertation]. University of New Hampshire; 2016. [cited 2020 Nov 30]. Available from: https://scholars.unh.edu/dissertation/1378.

Council of Science Editors:

Ziniti BL. Computationally Efficient Specifications of Spatial Point Process Models and Spatio-Temporal Gaussian Models: Combining Remote Sensing Drivers with Geospatial Disease Case Data to Enhance Geographic Epidemiology. [Doctoral Dissertation]. University of New Hampshire; 2016. Available from: https://scholars.unh.edu/dissertation/1378


Queensland University of Technology

18. He, Hu. Joint 2D and 3D cues for image segmentation towards robotic applications.

Degree: 2014, Queensland University of Technology

 This thesis investigates the fusion of 3D visual information with 2D image cues to provide 3D semantic maps of large-scale environments in which a robot… (more)

Subjects/Keywords: Image Segmentation; Computer Vision; Robotics; Structure from Motion; Markov Random Fields; Graph Cut; Energy Minimisation

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APA (6th Edition):

He, H. (2014). Joint 2D and 3D cues for image segmentation towards robotic applications. (Thesis). Queensland University of Technology. Retrieved from https://eprints.qut.edu.au/71760/

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):

He, Hu. “Joint 2D and 3D cues for image segmentation towards robotic applications.” 2014. Thesis, Queensland University of Technology. Accessed November 30, 2020. https://eprints.qut.edu.au/71760/.

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

MLA Handbook (7th Edition):

He, Hu. “Joint 2D and 3D cues for image segmentation towards robotic applications.” 2014. Web. 30 Nov 2020.

Vancouver:

He H. Joint 2D and 3D cues for image segmentation towards robotic applications. [Internet] [Thesis]. Queensland University of Technology; 2014. [cited 2020 Nov 30]. Available from: https://eprints.qut.edu.au/71760/.

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

Council of Science Editors:

He H. Joint 2D and 3D cues for image segmentation towards robotic applications. [Thesis]. Queensland University of Technology; 2014. Available from: https://eprints.qut.edu.au/71760/

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


University of Oulu

19. Roininen, L. (Lassi). Discretisation-invariant and computationally efficient correlation priors for Bayesian inversion.

Degree: 2015, University of Oulu

 Abstract We are interested in studying Gaussian Markov random fields as correlation priors for Bayesian inversion. We construct the correlation priors to be discretisation-invariant, which… (more)

Subjects/Keywords: Bayesian statistical inverse problems; Gaussian Markov random fields; convergence; discretisation; stochastic partial differential equations

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APA (6th Edition):

Roininen, L. (. (2015). Discretisation-invariant and computationally efficient correlation priors for Bayesian inversion. (Doctoral Dissertation). University of Oulu. Retrieved from http://urn.fi/urn:isbn:9789526207544

Chicago Manual of Style (16th Edition):

Roininen, L (Lassi). “Discretisation-invariant and computationally efficient correlation priors for Bayesian inversion.” 2015. Doctoral Dissertation, University of Oulu. Accessed November 30, 2020. http://urn.fi/urn:isbn:9789526207544.

MLA Handbook (7th Edition):

Roininen, L (Lassi). “Discretisation-invariant and computationally efficient correlation priors for Bayesian inversion.” 2015. Web. 30 Nov 2020.

Vancouver:

Roininen L(. Discretisation-invariant and computationally efficient correlation priors for Bayesian inversion. [Internet] [Doctoral dissertation]. University of Oulu; 2015. [cited 2020 Nov 30]. Available from: http://urn.fi/urn:isbn:9789526207544.

Council of Science Editors:

Roininen L(. Discretisation-invariant and computationally efficient correlation priors for Bayesian inversion. [Doctoral Dissertation]. University of Oulu; 2015. Available from: http://urn.fi/urn:isbn:9789526207544


Iowa State University

20. Clark, Nicholas John. Self-exciting spatio-temporal statistical models for count data with applications to modeling the spread of violence.

Degree: 2018, Iowa State University

 In this dissertation we provide statistical models and inferential techniques for analyzing the number of violent or criminal events as they evolve over space and… (more)

Subjects/Keywords: Dependent Counts; Hawkes Processes; Laplace Approximations; Markov Random Fields; Statistics and Probability

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APA (6th Edition):

Clark, N. J. (2018). Self-exciting spatio-temporal statistical models for count data with applications to modeling the spread of violence. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/16333

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):

Clark, Nicholas John. “Self-exciting spatio-temporal statistical models for count data with applications to modeling the spread of violence.” 2018. Thesis, Iowa State University. Accessed November 30, 2020. https://lib.dr.iastate.edu/etd/16333.

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

MLA Handbook (7th Edition):

Clark, Nicholas John. “Self-exciting spatio-temporal statistical models for count data with applications to modeling the spread of violence.” 2018. Web. 30 Nov 2020.

Vancouver:

Clark NJ. Self-exciting spatio-temporal statistical models for count data with applications to modeling the spread of violence. [Internet] [Thesis]. Iowa State University; 2018. [cited 2020 Nov 30]. Available from: https://lib.dr.iastate.edu/etd/16333.

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

Council of Science Editors:

Clark NJ. Self-exciting spatio-temporal statistical models for count data with applications to modeling the spread of violence. [Thesis]. Iowa State University; 2018. Available from: https://lib.dr.iastate.edu/etd/16333

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


Michigan State University

21. Zhang, Sixiang. Markov properties of measure-indexed Gaussian random fields.

Degree: PhD, Department of Statistics and Probability, 1990, Michigan State University

Subjects/Keywords: Markov processes; Gaussian processes; Random fields

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APA (6th Edition):

Zhang, S. (1990). Markov properties of measure-indexed Gaussian random fields. (Doctoral Dissertation). Michigan State University. Retrieved from http://etd.lib.msu.edu/islandora/object/etd:22749

Chicago Manual of Style (16th Edition):

Zhang, Sixiang. “Markov properties of measure-indexed Gaussian random fields.” 1990. Doctoral Dissertation, Michigan State University. Accessed November 30, 2020. http://etd.lib.msu.edu/islandora/object/etd:22749.

MLA Handbook (7th Edition):

Zhang, Sixiang. “Markov properties of measure-indexed Gaussian random fields.” 1990. Web. 30 Nov 2020.

Vancouver:

Zhang S. Markov properties of measure-indexed Gaussian random fields. [Internet] [Doctoral dissertation]. Michigan State University; 1990. [cited 2020 Nov 30]. Available from: http://etd.lib.msu.edu/islandora/object/etd:22749.

Council of Science Editors:

Zhang S. Markov properties of measure-indexed Gaussian random fields. [Doctoral Dissertation]. Michigan State University; 1990. Available from: http://etd.lib.msu.edu/islandora/object/etd:22749


Rice University

22. Baker, Yulia. Methods and Applications for Mixed Graphical Models.

Degree: PhD, Engineering, 2017, Rice University

 ``Multi-view Data'' is a term used to describe heterogeneous data measured on the same set of observations but collected from different sources and of potentially… (more)

Subjects/Keywords: Multi-view Data; mixed graphical models; Markov Random Fields; model selection; gene regulatory network

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APA (6th Edition):

Baker, Y. (2017). Methods and Applications for Mixed Graphical Models. (Doctoral Dissertation). Rice University. Retrieved from http://hdl.handle.net/1911/105527

Chicago Manual of Style (16th Edition):

Baker, Yulia. “Methods and Applications for Mixed Graphical Models.” 2017. Doctoral Dissertation, Rice University. Accessed November 30, 2020. http://hdl.handle.net/1911/105527.

MLA Handbook (7th Edition):

Baker, Yulia. “Methods and Applications for Mixed Graphical Models.” 2017. Web. 30 Nov 2020.

Vancouver:

Baker Y. Methods and Applications for Mixed Graphical Models. [Internet] [Doctoral dissertation]. Rice University; 2017. [cited 2020 Nov 30]. Available from: http://hdl.handle.net/1911/105527.

Council of Science Editors:

Baker Y. Methods and Applications for Mixed Graphical Models. [Doctoral Dissertation]. Rice University; 2017. Available from: http://hdl.handle.net/1911/105527


University of Technology, Sydney

23. Thiyagarajan, Karthick. Robust sensor technologies combined with smart predictive analytics for hostile sewer infrastructures.

Degree: 2018, University of Technology, Sydney

 Underground sewer systems are an important national infrastructure requirement of any country. In most cities, they are old and have been exposed to significant levels… (more)

Subjects/Keywords: Sensor technologies.; Sewer Infrastructures.; Gaussian Markov Random Fields.; Non-invasive Sensing.; Predictive analytics.

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APA (6th Edition):

Thiyagarajan, K. (2018). Robust sensor technologies combined with smart predictive analytics for hostile sewer infrastructures. (Thesis). University of Technology, Sydney. Retrieved from http://hdl.handle.net/10453/128023

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):

Thiyagarajan, Karthick. “Robust sensor technologies combined with smart predictive analytics for hostile sewer infrastructures.” 2018. Thesis, University of Technology, Sydney. Accessed November 30, 2020. http://hdl.handle.net/10453/128023.

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

MLA Handbook (7th Edition):

Thiyagarajan, Karthick. “Robust sensor technologies combined with smart predictive analytics for hostile sewer infrastructures.” 2018. Web. 30 Nov 2020.

Vancouver:

Thiyagarajan K. Robust sensor technologies combined with smart predictive analytics for hostile sewer infrastructures. [Internet] [Thesis]. University of Technology, Sydney; 2018. [cited 2020 Nov 30]. Available from: http://hdl.handle.net/10453/128023.

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

Council of Science Editors:

Thiyagarajan K. Robust sensor technologies combined with smart predictive analytics for hostile sewer infrastructures. [Thesis]. University of Technology, Sydney; 2018. Available from: http://hdl.handle.net/10453/128023

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


University of Notre Dame

24. Jiao Wang. System and Image Modeling in Statistical Iterative Reconstruction for Multi-Slice CT</h1>.

Degree: Electrical Engineering, 2012, University of Notre Dame

  Inverse problem involves estimating parameters or data from inadequate observations. Bayesian estimation introduces regularization to provide mild assumptions on the solution and prevent overfitting… (more)

Subjects/Keywords: X-ray CT; forward model; Markov random fields; a priori density; iterative reconstruction

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APA (6th Edition):

Wang, J. (2012). System and Image Modeling in Statistical Iterative Reconstruction for Multi-Slice CT</h1>. (Thesis). University of Notre Dame. Retrieved from https://curate.nd.edu/show/k930bv75n9g

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):

Wang, Jiao. “System and Image Modeling in Statistical Iterative Reconstruction for Multi-Slice CT</h1>.” 2012. Thesis, University of Notre Dame. Accessed November 30, 2020. https://curate.nd.edu/show/k930bv75n9g.

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

MLA Handbook (7th Edition):

Wang, Jiao. “System and Image Modeling in Statistical Iterative Reconstruction for Multi-Slice CT</h1>.” 2012. Web. 30 Nov 2020.

Vancouver:

Wang J. System and Image Modeling in Statistical Iterative Reconstruction for Multi-Slice CT</h1>. [Internet] [Thesis]. University of Notre Dame; 2012. [cited 2020 Nov 30]. Available from: https://curate.nd.edu/show/k930bv75n9g.

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

Council of Science Editors:

Wang J. System and Image Modeling in Statistical Iterative Reconstruction for Multi-Slice CT</h1>. [Thesis]. University of Notre Dame; 2012. Available from: https://curate.nd.edu/show/k930bv75n9g

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


Delft University of Technology

25. Mandersloot, Jeroen (author). Model-based rare category detection for temporal data.

Degree: 2018, Delft University of Technology

 Rare category detection is the task of discovering rare classes in unlabelled and imbalanced datasets. Existing algorithms focus almost exclusively on static data in which… (more)

Subjects/Keywords: rare category detection; temporal data; semi-supervised learning; mixture models; markov random fields

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APA (6th Edition):

Mandersloot, J. (. (2018). Model-based rare category detection for temporal data. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:80755ee1-95c9-4b7d-b828-fff818ceadd4

Chicago Manual of Style (16th Edition):

Mandersloot, Jeroen (author). “Model-based rare category detection for temporal data.” 2018. Masters Thesis, Delft University of Technology. Accessed November 30, 2020. http://resolver.tudelft.nl/uuid:80755ee1-95c9-4b7d-b828-fff818ceadd4.

MLA Handbook (7th Edition):

Mandersloot, Jeroen (author). “Model-based rare category detection for temporal data.” 2018. Web. 30 Nov 2020.

Vancouver:

Mandersloot J(. Model-based rare category detection for temporal data. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2020 Nov 30]. Available from: http://resolver.tudelft.nl/uuid:80755ee1-95c9-4b7d-b828-fff818ceadd4.

Council of Science Editors:

Mandersloot J(. Model-based rare category detection for temporal data. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:80755ee1-95c9-4b7d-b828-fff818ceadd4


University of British Columbia

26. Siksik, Ola. Markov random fields in visual reconstruction : a transputer-based multicomputer implementation.

Degree: MS- MSc, Computer Science, 1990, University of British Columbia

Markov Random Fields (MRFs) are used in computer vision as an effective method for reconstructing a function starting from a set of noisy, or sparse… (more)

Subjects/Keywords: Markov processes; Random fields; Computer vision

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APA (6th Edition):

Siksik, O. (1990). Markov random fields in visual reconstruction : a transputer-based multicomputer implementation. (Masters Thesis). University of British Columbia. Retrieved from http://hdl.handle.net/2429/28863

Chicago Manual of Style (16th Edition):

Siksik, Ola. “Markov random fields in visual reconstruction : a transputer-based multicomputer implementation.” 1990. Masters Thesis, University of British Columbia. Accessed November 30, 2020. http://hdl.handle.net/2429/28863.

MLA Handbook (7th Edition):

Siksik, Ola. “Markov random fields in visual reconstruction : a transputer-based multicomputer implementation.” 1990. Web. 30 Nov 2020.

Vancouver:

Siksik O. Markov random fields in visual reconstruction : a transputer-based multicomputer implementation. [Internet] [Masters thesis]. University of British Columbia; 1990. [cited 2020 Nov 30]. Available from: http://hdl.handle.net/2429/28863.

Council of Science Editors:

Siksik O. Markov random fields in visual reconstruction : a transputer-based multicomputer implementation. [Masters Thesis]. University of British Columbia; 1990. Available from: http://hdl.handle.net/2429/28863


University of Texas – Austin

27. Johnson, Christopher Carroll. Greedy structure learning of Markov Random Fields.

Degree: MSin Computer Sciences, Computer Science, 2011, University of Texas – Austin

 Probabilistic graphical models are used in a variety of domains to capture and represent general dependencies in joint probability distributions. In this document we examine… (more)

Subjects/Keywords: Machine learning; Graphical models; Markov Random Fields; Structure learning; Probability; Uncertainty; Greedy algorithms

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APA (6th Edition):

Johnson, C. C. (2011). Greedy structure learning of Markov Random Fields. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2011-08-4331

Chicago Manual of Style (16th Edition):

Johnson, Christopher Carroll. “Greedy structure learning of Markov Random Fields.” 2011. Masters Thesis, University of Texas – Austin. Accessed November 30, 2020. http://hdl.handle.net/2152/ETD-UT-2011-08-4331.

MLA Handbook (7th Edition):

Johnson, Christopher Carroll. “Greedy structure learning of Markov Random Fields.” 2011. Web. 30 Nov 2020.

Vancouver:

Johnson CC. Greedy structure learning of Markov Random Fields. [Internet] [Masters thesis]. University of Texas – Austin; 2011. [cited 2020 Nov 30]. Available from: http://hdl.handle.net/2152/ETD-UT-2011-08-4331.

Council of Science Editors:

Johnson CC. Greedy structure learning of Markov Random Fields. [Masters Thesis]. University of Texas – Austin; 2011. Available from: http://hdl.handle.net/2152/ETD-UT-2011-08-4331

28. Wang, Chaohui. Distributed and higher-order graphical models : towards segmentation, tracking, matching and 3D model inference : Modèles graphiques distribués et d'ordre supérieur : pour la segmentation, le suivi d'objet, l'alignement et l'inférence de modèle 3D.

Degree: Docteur es, Mathématiques appliquées aux systèmes, 2011, Châtenay-Malabry, Ecole centrale de Paris

Cette thèse est dédiée au développement de méthodes à base de graphes, permettant de traiter les problèmes fondamentaux de la vision par ordinateur tels que… (more)

Subjects/Keywords: Champs de Markov Aléatoires; Suivi; Inférence de Modèles 3D; Markov Random Fields; Tracking; 3D Model Inference

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APA (6th Edition):

Wang, C. (2011). Distributed and higher-order graphical models : towards segmentation, tracking, matching and 3D model inference : Modèles graphiques distribués et d'ordre supérieur : pour la segmentation, le suivi d'objet, l'alignement et l'inférence de modèle 3D. (Doctoral Dissertation). Châtenay-Malabry, Ecole centrale de Paris. Retrieved from http://www.theses.fr/2011ECAP0037

Chicago Manual of Style (16th Edition):

Wang, Chaohui. “Distributed and higher-order graphical models : towards segmentation, tracking, matching and 3D model inference : Modèles graphiques distribués et d'ordre supérieur : pour la segmentation, le suivi d'objet, l'alignement et l'inférence de modèle 3D.” 2011. Doctoral Dissertation, Châtenay-Malabry, Ecole centrale de Paris. Accessed November 30, 2020. http://www.theses.fr/2011ECAP0037.

MLA Handbook (7th Edition):

Wang, Chaohui. “Distributed and higher-order graphical models : towards segmentation, tracking, matching and 3D model inference : Modèles graphiques distribués et d'ordre supérieur : pour la segmentation, le suivi d'objet, l'alignement et l'inférence de modèle 3D.” 2011. Web. 30 Nov 2020.

Vancouver:

Wang C. Distributed and higher-order graphical models : towards segmentation, tracking, matching and 3D model inference : Modèles graphiques distribués et d'ordre supérieur : pour la segmentation, le suivi d'objet, l'alignement et l'inférence de modèle 3D. [Internet] [Doctoral dissertation]. Châtenay-Malabry, Ecole centrale de Paris; 2011. [cited 2020 Nov 30]. Available from: http://www.theses.fr/2011ECAP0037.

Council of Science Editors:

Wang C. Distributed and higher-order graphical models : towards segmentation, tracking, matching and 3D model inference : Modèles graphiques distribués et d'ordre supérieur : pour la segmentation, le suivi d'objet, l'alignement et l'inférence de modèle 3D. [Doctoral Dissertation]. Châtenay-Malabry, Ecole centrale de Paris; 2011. Available from: http://www.theses.fr/2011ECAP0037

29. Roussel, Guillaume. Développement et évaluation de nouvelles méthodes de classification spatiale-spectrale d’images hyperspectrales : Development and evaluation of new spatial-spectral classification methods of hyperspectral images.

Degree: Docteur es, Photonique et systèmes optoélectroniques, 2012, Toulouse, ISAE

L'imagerie hyperspectrale, grâce à un nombre élevé de bandes spectrales très fines et contigües, est capable d'associer àchaque pixel d'une image une signature spectrale caractéristique… (more)

Subjects/Keywords: Classification; Hyperspectral; Information spatiale; Texture; Champs de Markov; Segmentation; Classification; Hyperspectral; Spatial information; Texture; Markov Random Fields; Segmentation; 621

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APA (6th Edition):

Roussel, G. (2012). Développement et évaluation de nouvelles méthodes de classification spatiale-spectrale d’images hyperspectrales : Development and evaluation of new spatial-spectral classification methods of hyperspectral images. (Doctoral Dissertation). Toulouse, ISAE. Retrieved from http://www.theses.fr/2012ESAE0020

Chicago Manual of Style (16th Edition):

Roussel, Guillaume. “Développement et évaluation de nouvelles méthodes de classification spatiale-spectrale d’images hyperspectrales : Development and evaluation of new spatial-spectral classification methods of hyperspectral images.” 2012. Doctoral Dissertation, Toulouse, ISAE. Accessed November 30, 2020. http://www.theses.fr/2012ESAE0020.

MLA Handbook (7th Edition):

Roussel, Guillaume. “Développement et évaluation de nouvelles méthodes de classification spatiale-spectrale d’images hyperspectrales : Development and evaluation of new spatial-spectral classification methods of hyperspectral images.” 2012. Web. 30 Nov 2020.

Vancouver:

Roussel G. Développement et évaluation de nouvelles méthodes de classification spatiale-spectrale d’images hyperspectrales : Development and evaluation of new spatial-spectral classification methods of hyperspectral images. [Internet] [Doctoral dissertation]. Toulouse, ISAE; 2012. [cited 2020 Nov 30]. Available from: http://www.theses.fr/2012ESAE0020.

Council of Science Editors:

Roussel G. Développement et évaluation de nouvelles méthodes de classification spatiale-spectrale d’images hyperspectrales : Development and evaluation of new spatial-spectral classification methods of hyperspectral images. [Doctoral Dissertation]. Toulouse, ISAE; 2012. Available from: http://www.theses.fr/2012ESAE0020


Victoria University of Wellington

30. Morris, Lindsay. Spatial and Temporal Modelling of Hoki Distribution using Gaussian Markov Random Fields.

Degree: 2017, Victoria University of Wellington

 In order to carry out assessment of marine stock levels, an accurate estimate of the current year's population abundance must be formulated. Standardized catch per… (more)

Subjects/Keywords: Spatial autocorrelation; Point referenced data; Gaussian Markov random fields; Bayesian hierarchical models; Gaussian; Markov; Spatial statistics

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APA (6th Edition):

Morris, L. (2017). Spatial and Temporal Modelling of Hoki Distribution using Gaussian Markov Random Fields. (Masters Thesis). Victoria University of Wellington. Retrieved from http://hdl.handle.net/10063/6426

Chicago Manual of Style (16th Edition):

Morris, Lindsay. “Spatial and Temporal Modelling of Hoki Distribution using Gaussian Markov Random Fields.” 2017. Masters Thesis, Victoria University of Wellington. Accessed November 30, 2020. http://hdl.handle.net/10063/6426.

MLA Handbook (7th Edition):

Morris, Lindsay. “Spatial and Temporal Modelling of Hoki Distribution using Gaussian Markov Random Fields.” 2017. Web. 30 Nov 2020.

Vancouver:

Morris L. Spatial and Temporal Modelling of Hoki Distribution using Gaussian Markov Random Fields. [Internet] [Masters thesis]. Victoria University of Wellington; 2017. [cited 2020 Nov 30]. Available from: http://hdl.handle.net/10063/6426.

Council of Science Editors:

Morris L. Spatial and Temporal Modelling of Hoki Distribution using Gaussian Markov Random Fields. [Masters Thesis]. Victoria University of Wellington; 2017. Available from: http://hdl.handle.net/10063/6426

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