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Markov random field
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91	Cutset sampling for Bayesian networks Cutset sampling for Bayesian networks Bozhena Bidyuk Rina Dechter Add to Reading List Source URL: www.ics.uci.edu Language: English - Date: 2006-10-03 14:22:14 Graphical models Monte Carlo methods Bayesian statistics Probability theory Networks Decomposition method Bayesian network Markov random field Gibbs sampling Statistics Graph theory Probability and statistics
92	REGULARIZATION, ADAPTATION, AND NON-INDEPENDENT FEATURES IMPROVE HIDDEN CONDITIONAL RANDOM FIELDS FOR PHONE CLASSIFICATION Yun-Hsuan Sung,1 Constantinos Boulis,2 Christopher Manning,3 Dan Jurafsky4 Electrical Engineering Add to Reading List Source URL: nlp.stanford.edu Language: English - Date: 2007-10-14 22:19:15 Learning Pattern recognition Regularization Conditional random field Hidden Markov model Speech recognition Overfitting Limited-memory BFGS Early stopping Machine learning Statistics Artificial intelligence
93	Modelling Immunisation Coverage in Nigeria: a Bayesian Structured Additive Regression Approach Samson Babatunde Adebayo Research and Evaluation Division, Society for Family Health, PMB 5116 Wuse, Abuja, Nigeria, sadebayo Add to Reading List Source URL: www.statistics.gov.hk Language: English - Date: 2013-08-22 04:39:21 Health Biotechnology Preventive medicine Vaccine Measles Markov random field Prevention Biology Vaccination
94	Conditional Random Fields: An Introduction∗ Hanna M. Wallach February 24, 2004 1 Add to Reading List Source URL: www.inference.phy.cam.ac.uk Language: English - Date: 2004-09-21 19:00:07 Probability theory Statistical theory Graphical models Bayesian statistics Machine learning Conditional random field Hidden Markov model Markov random field Information theory Statistics Probability and statistics Probability
95	Generating Markov Equivalent Maximal Ancestral Graphs by Single Edge Replacement Jin Tian Department of Computer Science Iowa State University Add to Reading List Source URL: www.cs.iastate.edu Language: English - Date: 2005-06-01 16:14:08 Bayesian statistics Statistical models Networks Ancestral graph Bayesian network M-separation Graph Directed graph Markov random field Graph theory Graphical models Statistics
96	Webpage Understanding: an Integrated Approach Jun Zhu ∗ Dept. of Comp. Sci. & Tech. Add to Reading List Source URL: research.microsoft.com Language: English - Date: 2007-06-15 03:38:48 Mathematics Statistics Machine learning Artificial intelligence Probability theory Conditional random field Hidden Markov model Pattern recognition Markov random field Theoretical computer science Graphical models Applied mathematics
97	498 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 47, NO. 2, FEBRUARY 2001 Factor Graphs and the Sum-Product Algorithm Frank R. Kschischang, Senior Member, IEEE, Brendan J. Frey, Member, IEEE, and Add to Reading List Source URL: www.psi.toronto.edu Language: English - Date: 2005-01-17 09:27:09 Factor graph Belief propagation Tree decomposition Directed acyclic graph Bayesian network Markov random field Graph coloring Path decomposition Graph theory Mathematics Graphical models
98	Hidden Conditional Random Fields for Phone Recognition Yun-Hsuan Sung 1 and Dan Jurafsky 2 1 Electrical Engineering, Stanford University Add to Reading List Source URL: nlp.stanford.edu Language: English - Date: 2010-01-07 17:30:55 Probability and statistics Probability Hidden Markov model Conditional random field Speech recognition Mutual information Sequence labeling Generative model Viterbi algorithm Statistics Machine learning Markov models
99	FROM FLAT DIRECT MODELS TO SEGMENTAL CRF MODELS Geoffrey Zweig and Patrick Nguyen Microsoft Corporation One Microsoft Way, Redmond, WAABSTRACT This paper summarizes recent work at Microsoft on the development of n Add to Reading List Source URL: research.microsoft.com Language: English - Date: 2010-01-06 19:40:40 Computational linguistics Conditional random field Maximum-entropy Markov model Speech recognition N-gram Language model Dynamic programming Part-of-speech tagging Information extraction Markov models Science Probability
100	MAXIMUM CONDITIONAL LIKELIHOOD LINEAR REGRESSION AND MAXIMUM A POSTERIORI FOR HIDDEN CONDITIONAL RANDOM FIELDS SPEAKER ADAPTATION Yun-Hsuan Sung,1 Constantinos Boulis,2 Dan Jurafsky3 Electrical Engineering,1 Linguistics2 Add to Reading List Source URL: nlp.stanford.edu Language: English - Date: 2008-04-28 14:26:34 Econometrics Machine learning Markov models Hidden Markov model Maximum likelihood Linear regression Pattern recognition Normal distribution Conditional random field Statistics Estimation theory Regression analysis

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