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- 2006 – 2010 (25)

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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

URL: http://etd.lib.msu.edu/islandora/object/etd:22785

Subjects/Keywords: Markov random fields; Markov processes

Record Details Similar Records

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

APA (6^{th} 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 (16^{th} Edition):

Kinateder, Kimberly Kay Johannes. “Strong Markov properties for Markov random fields.” 1990. Doctoral Dissertation, Michigan State University. Accessed December 01, 2020. http://etd.lib.msu.edu/islandora/object/etd:22785.

MLA Handbook (7^{th} Edition):

Kinateder, Kimberly Kay Johannes. “Strong Markov properties for Markov random fields.” 1990. Web. 01 Dec 2020.

Vancouver:

Kinateder KKJ. Strong Markov properties for Markov random fields. [Internet] [Doctoral dissertation]. Michigan State University; 1990. [cited 2020 Dec 01]. 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

URL: http://www.theses.fr/2010ECAP0024

►

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

APA (6^{th} 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 (16^{th} 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 December 01, 2020. http://www.theses.fr/2010ECAP0024.

MLA Handbook (7^{th} 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. 01 Dec 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 Dec 01]. 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

URL: http://www.theses.fr/2013ECAP0064

►

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 (6^{th} 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 (16^{th} 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 December 01, 2020. http://www.theses.fr/2013ECAP0064.

MLA Handbook (7^{th} 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. 01 Dec 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 Dec 01]. 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

URL: http://www.escholarship.org/uc/item/4367r57k

► 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

Record Details Similar Records

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APA (6^{th} 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 (16^{th} Edition):

Xie, Jianwen. “Learning Inhomogeneous FRAME Models for Object Patterns.” 2014. Thesis, UCLA. Accessed December 01, 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 (7^{th} Edition):

Xie, Jianwen. “Learning Inhomogeneous FRAME Models for Object Patterns.” 2014. Web. 01 Dec 2020.

Vancouver:

Xie J. Learning Inhomogeneous FRAME Models for Object Patterns. [Internet] [Thesis]. UCLA; 2014. [cited 2020 Dec 01]. 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

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

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

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

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

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APA (6^{th} Edition):

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

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

Fix, Alexander Jobe. “GRAPH CUTS, SUM-OF-SUBMODULAR FLOW, AND LINEAR PROGRAMMING: EFFECTIVE INFERENCE IN HIGHER-ORDER MARKOV RANDOM FIELDS.” 2017. Doctoral Dissertation, Cornell University. Accessed December 01, 2020. http://hdl.handle.net/1813/51592.

MLA Handbook (7^{th} Edition):

Fix, Alexander Jobe. “GRAPH CUTS, SUM-OF-SUBMODULAR FLOW, AND LINEAR PROGRAMMING: EFFECTIVE INFERENCE IN HIGHER-ORDER MARKOV RANDOM FIELDS.” 2017. Web. 01 Dec 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 Dec 01]. 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

URL: http://hdl.handle.net/2440/117806

► 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

Record Details Similar Records

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APA (6^{th} Edition):

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

Not specified: Masters Thesis or Doctoral Dissertation

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

Suwanwimolkul, Suwichaya. “Adaptive Markov Random Fields for Structured Compressive Sensing.” 2018. Thesis, University of Adelaide. Accessed December 01, 2020. http://hdl.handle.net/2440/117806.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Suwanwimolkul, Suwichaya. “Adaptive Markov Random Fields for Structured Compressive Sensing.” 2018. Web. 01 Dec 2020.

Vancouver:

Suwanwimolkul S. Adaptive Markov Random Fields for Structured Compressive Sensing. [Internet] [Thesis]. University of Adelaide; 2018. [cited 2020 Dec 01]. Available from: http://hdl.handle.net/2440/117806.

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

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

URL: https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=4272&context=etd

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

APA (6^{th} 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 (16^{th} Edition):

Olsen, Jessica Lyn. “An Applied Investigation of Gaussian Markov Random Fields.” 2012. Masters Thesis, Brigham Young University. Accessed December 01, 2020. https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=4272&context=etd.

MLA Handbook (7^{th} Edition):

Olsen, Jessica Lyn. “An Applied Investigation of Gaussian Markov Random Fields.” 2012. Web. 01 Dec 2020.

Vancouver:

Olsen JL. An Applied Investigation of Gaussian Markov Random Fields. [Internet] [Masters thesis]. Brigham Young University; 2012. [cited 2020 Dec 01]. 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

URL: http://etd.lib.msu.edu/islandora/object/etd:21893

Subjects/Keywords: Computer vision; Markov random fields

Record Details Similar Records

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

APA (6^{th} 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 (16^{th} Edition):

Nadabar, Sateesha Gopalakrishna. “Markov random field contextual models in computer vision.” 1992. Doctoral Dissertation, Michigan State University. Accessed December 01, 2020. http://etd.lib.msu.edu/islandora/object/etd:21893.

MLA Handbook (7^{th} Edition):

Nadabar, Sateesha Gopalakrishna. “Markov random field contextual models in computer vision.” 1992. Web. 01 Dec 2020.

Vancouver:

Nadabar SG. Markov random field contextual models in computer vision. [Internet] [Doctoral dissertation]. Michigan State University; 1992. [cited 2020 Dec 01]. 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

URL: etd-082714-192730 ; https://digitalcommons.wpi.edu/etd-dissertations/369

► " 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

Record Details Similar Records

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

APA (6^{th} 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 (16^{th} 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 December 01, 2020. etd-082714-192730 ; https://digitalcommons.wpi.edu/etd-dissertations/369.

MLA Handbook (7^{th} Edition):

Kutarnia, Jason Francis. “A Markov Random Field Based Approach to 3D Mosaicing and Registration Applied to Ultrasound Simulation.” 2014. Web. 01 Dec 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 Dec 01]. 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

URL: http://hdl.handle.net/10919/86263

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

APA (6^{th} 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 (16^{th} Edition):

Chaabene, Walid. “Scalable Structure Learning of Graphical Models.” 2017. Masters Thesis, Virginia Tech. Accessed December 01, 2020. http://hdl.handle.net/10919/86263.

MLA Handbook (7^{th} Edition):

Chaabene, Walid. “Scalable Structure Learning of Graphical Models.” 2017. Web. 01 Dec 2020.

Vancouver:

Chaabene W. Scalable Structure Learning of Graphical Models. [Internet] [Masters thesis]. Virginia Tech; 2017. [cited 2020 Dec 01]. 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

URL: http://hdl.handle.net/2152/28058

► 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

Record Details Similar Records

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

APA (6^{th} 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 (16^{th} Edition):

Yang, Eunho. “High-dimensional statistics : model specification and elementary estimators.” 2014. Doctoral Dissertation, University of Texas – Austin. Accessed December 01, 2020. http://hdl.handle.net/2152/28058.

MLA Handbook (7^{th} Edition):

Yang, Eunho. “High-dimensional statistics : model specification and elementary estimators.” 2014. Web. 01 Dec 2020.

Vancouver:

Yang E. High-dimensional statistics : model specification and elementary estimators. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2014. [cited 2020 Dec 01]. 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

URL: https://lup.lub.lu.se/record/2539400 ; https://portal.research.lu.se/ws/files/4123914/2539412.pdf

► 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

Record Details Similar Records

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

APA (6^{th} 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 (16^{th} 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 December 01, 2020. https://lup.lub.lu.se/record/2539400 ; https://portal.research.lu.se/ws/files/4123914/2539412.pdf.

MLA Handbook (7^{th} Edition):

Bolin, David. “Models and Methods for Random Fields in Spatial Statistics with Computational Efficiency from Markov Properties.” 2012. Web. 01 Dec 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 Dec 01]. 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

URL: http://hdl.handle.net/2142/18543

► 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

Record Details Similar Records

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

APA (6^{th} 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 (16^{th} 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 December 01, 2020. http://hdl.handle.net/2142/18543.

MLA Handbook (7^{th} Edition):

Le, Tung. “Probabilistic inference via sum-product algorithms on binary pairwise Gibbs random fields with applications to multiple fault diagnosis.” 2011. Web. 01 Dec 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 Dec 01]. 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

URL: http://www.teses.usp.br/teses/disponiveis/55/55134/tde-16032015-113613/ ;

►

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

Record Details Similar Records

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

APA (6^{th} 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 (16^{th} 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 December 01, 2020. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-16032015-113613/ ;.

MLA Handbook (7^{th} Edition):

Botelho, Glenda Michele. “Segmentação de imagens baseada em redes complexas e superpixels: uma aplicação ao censo de aves.” 2014. Web. 01 Dec 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 Dec 01]. 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

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210650

►

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

Record Details Similar Records

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

APA (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

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

Gao, Xiaoxu. “Exploring declarative rule-based probabilistic frameworks for link prediction in Knowledge Graphs.” 2017. Thesis, KTH. Accessed December 01, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210650.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Gao, Xiaoxu. “Exploring declarative rule-based probabilistic frameworks for link prediction in Knowledge Graphs.” 2017. Web. 01 Dec 2020.

Vancouver:

Gao X. Exploring declarative rule-based probabilistic frameworks for link prediction in Knowledge Graphs. [Internet] [Thesis]. KTH; 2017. [cited 2020 Dec 01]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210650.

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

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

URL: http://hdl.handle.net/1802/28364

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

APA (6^{th} 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 (16^{th} Edition):

Papai, Tivadar (1984 - ). “Exploiting constraints, sequential structure, and knowledge in Markov logic networks.” 2014. Doctoral Dissertation, University of Rochester. Accessed December 01, 2020. http://hdl.handle.net/1802/28364.

MLA Handbook (7^{th} Edition):

Papai, Tivadar (1984 - ). “Exploiting constraints, sequential structure, and knowledge in Markov logic networks.” 2014. Web. 01 Dec 2020.

Vancouver:

Papai T(-). Exploiting constraints, sequential structure, and knowledge in Markov logic networks. [Internet] [Doctoral dissertation]. University of Rochester; 2014. [cited 2020 Dec 01]. 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

URL: https://scholars.unh.edu/dissertation/1378

► 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

Record Details Similar Records

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

APA (6^{th} 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 (16^{th} 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 December 01, 2020. https://scholars.unh.edu/dissertation/1378.

MLA Handbook (7^{th} 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. 01 Dec 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 Dec 01]. 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

URL: https://eprints.qut.edu.au/71760/

► 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

Record Details Similar Records

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

APA (6^{th} 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/

Not specified: Masters Thesis or Doctoral Dissertation

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

He, Hu. “Joint 2D and 3D cues for image segmentation towards robotic applications.” 2014. Thesis, Queensland University of Technology. Accessed December 01, 2020. https://eprints.qut.edu.au/71760/.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

He, Hu. “Joint 2D and 3D cues for image segmentation towards robotic applications.” 2014. Web. 01 Dec 2020.

Vancouver:

He H. Joint 2D and 3D cues for image segmentation towards robotic applications. [Internet] [Thesis]. Queensland University of Technology; 2014. [cited 2020 Dec 01]. Available from: https://eprints.qut.edu.au/71760/.

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/

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

URL: http://urn.fi/urn:isbn:9789526207544

► 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

Record Details Similar Records

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

APA (6^{th} 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 (16^{th} Edition):

Roininen, L (Lassi). “Discretisation-invariant and computationally efficient correlation priors for Bayesian inversion.” 2015. Doctoral Dissertation, University of Oulu. Accessed December 01, 2020. http://urn.fi/urn:isbn:9789526207544.

MLA Handbook (7^{th} Edition):

Roininen, L (Lassi). “Discretisation-invariant and computationally efficient correlation priors for Bayesian inversion.” 2015. Web. 01 Dec 2020.

Vancouver:

Roininen L(. Discretisation-invariant and computationally efficient correlation priors for Bayesian inversion. [Internet] [Doctoral dissertation]. University of Oulu; 2015. [cited 2020 Dec 01]. 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

URL: https://lib.dr.iastate.edu/etd/16333

► 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

Record Details Similar Records

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

APA (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} 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 December 01, 2020. https://lib.dr.iastate.edu/etd/16333.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Clark, Nicholas John. “Self-exciting spatio-temporal statistical models for count data with applications to modeling the spread of violence.” 2018. Web. 01 Dec 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 Dec 01]. Available from: https://lib.dr.iastate.edu/etd/16333.

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

Not specified: Masters Thesis or Doctoral Dissertation

Michigan State University

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

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

URL: http://etd.lib.msu.edu/islandora/object/etd:22749

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

Record Details Similar Records

❌

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

APA (6^{th} 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 (16^{th} Edition):

Zhang, Sixiang. “Markov properties of measure-indexed Gaussian random fields.” 1990. Doctoral Dissertation, Michigan State University. Accessed December 01, 2020. http://etd.lib.msu.edu/islandora/object/etd:22749.

MLA Handbook (7^{th} Edition):

Zhang, Sixiang. “Markov properties of measure-indexed Gaussian random fields.” 1990. Web. 01 Dec 2020.

Vancouver:

Zhang S. Markov properties of measure-indexed Gaussian random fields. [Internet] [Doctoral dissertation]. Michigan State University; 1990. [cited 2020 Dec 01]. 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

URL: http://hdl.handle.net/1911/105527

► ``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

Record Details Similar Records

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

APA (6^{th} 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 (16^{th} Edition):

Baker, Yulia. “Methods and Applications for Mixed Graphical Models.” 2017. Doctoral Dissertation, Rice University. Accessed December 01, 2020. http://hdl.handle.net/1911/105527.

MLA Handbook (7^{th} Edition):

Baker, Yulia. “Methods and Applications for Mixed Graphical Models.” 2017. Web. 01 Dec 2020.

Vancouver:

Baker Y. Methods and Applications for Mixed Graphical Models. [Internet] [Doctoral dissertation]. Rice University; 2017. [cited 2020 Dec 01]. 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

URL: http://hdl.handle.net/10453/128023

► 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.

Record Details Similar Records

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

APA (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

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

Thiyagarajan, Karthick. “Robust sensor technologies combined with smart predictive analytics for hostile sewer infrastructures.” 2018. Thesis, University of Technology, Sydney. Accessed December 01, 2020. http://hdl.handle.net/10453/128023.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Thiyagarajan, Karthick. “Robust sensor technologies combined with smart predictive analytics for hostile sewer infrastructures.” 2018. Web. 01 Dec 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 Dec 01]. Available from: http://hdl.handle.net/10453/128023.

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

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

URL: https://curate.nd.edu/show/k930bv75n9g

► 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

Record Details Similar Records

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

APA (6^{th} 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

Not specified: Masters Thesis or Doctoral Dissertation

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

Wang, Jiao. “System and Image Modeling in Statistical Iterative Reconstruction for Multi-Slice CT</h1>.” 2012. Thesis, University of Notre Dame. Accessed December 01, 2020. https://curate.nd.edu/show/k930bv75n9g.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Wang, Jiao. “System and Image Modeling in Statistical Iterative Reconstruction for Multi-Slice CT</h1>.” 2012. Web. 01 Dec 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 Dec 01]. Available from: https://curate.nd.edu/show/k930bv75n9g.

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

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

URL: http://resolver.tudelft.nl/uuid:80755ee1-95c9-4b7d-b828-fff818ceadd4

► 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

Record Details Similar Records

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

APA (6^{th} 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 (16^{th} Edition):

Mandersloot, Jeroen (author). “Model-based rare category detection for temporal data.” 2018. Masters Thesis, Delft University of Technology. Accessed December 01, 2020. http://resolver.tudelft.nl/uuid:80755ee1-95c9-4b7d-b828-fff818ceadd4.

MLA Handbook (7^{th} Edition):

Mandersloot, Jeroen (author). “Model-based rare category detection for temporal data.” 2018. Web. 01 Dec 2020.

Vancouver:

Mandersloot J(. Model-based rare category detection for temporal data. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2020 Dec 01]. 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

URL: http://hdl.handle.net/2429/28863

► *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

Record Details Similar Records

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

APA (6^{th} 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 (16^{th} Edition):

Siksik, Ola. “Markov random fields in visual reconstruction : a transputer-based multicomputer implementation.” 1990. Masters Thesis, University of British Columbia. Accessed December 01, 2020. http://hdl.handle.net/2429/28863.

MLA Handbook (7^{th} Edition):

Siksik, Ola. “Markov random fields in visual reconstruction : a transputer-based multicomputer implementation.” 1990. Web. 01 Dec 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 Dec 01]. 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

URL: http://hdl.handle.net/2152/ETD-UT-2011-08-4331

► 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

Record Details Similar Records

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

APA (6^{th} 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 (16^{th} Edition):

Johnson, Christopher Carroll. “Greedy structure learning of Markov Random Fields.” 2011. Masters Thesis, University of Texas – Austin. Accessed December 01, 2020. http://hdl.handle.net/2152/ETD-UT-2011-08-4331.

MLA Handbook (7^{th} Edition):

Johnson, Christopher Carroll. “Greedy structure learning of Markov Random Fields.” 2011. Web. 01 Dec 2020.

Vancouver:

Johnson CC. Greedy structure learning of Markov Random Fields. [Internet] [Masters thesis]. University of Texas – Austin; 2011. [cited 2020 Dec 01]. 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

URL: http://www.theses.fr/2011ECAP0037

►

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

Record Details Similar Records

❌

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

APA (6^{th} 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 (16^{th} 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 December 01, 2020. http://www.theses.fr/2011ECAP0037.

MLA Handbook (7^{th} 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. 01 Dec 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 Dec 01]. 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

URL: http://www.theses.fr/2012ESAE0020

►

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

Record Details Similar Records

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

APA (6^{th} 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 (16^{th} 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 December 01, 2020. http://www.theses.fr/2012ESAE0020.

MLA Handbook (7^{th} 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. 01 Dec 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 Dec 01]. 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

URL: http://hdl.handle.net/10063/6426

► 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

Record Details Similar Records

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

APA (6^{th} 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 (16^{th} Edition):

Morris, Lindsay. “Spatial and Temporal Modelling of Hoki Distribution using Gaussian Markov Random Fields.” 2017. Masters Thesis, Victoria University of Wellington. Accessed December 01, 2020. http://hdl.handle.net/10063/6426.

MLA Handbook (7^{th} Edition):

Morris, Lindsay. “Spatial and Temporal Modelling of Hoki Distribution using Gaussian Markov Random Fields.” 2017. Web. 01 Dec 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 Dec 01]. 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