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Expectation–maximization algorithm
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131	Expectation Maximization and Complex Duration Distributions for Continuous Time Bayesian Networks Uri Nodelman Stanford University Add to Reading List Source URL: robotics.stanford.edu Language: English - Date: 2005-06-05 21:42:42 Exponentials Statistical theory Bayesian statistics Expectation–maximization algorithm Hidden Markov model Bayesian network Exponential family Maximum likelihood Sufficient statistic Statistics Markov models Estimation theory
132	Latent Coincidence Analysis: A Hidden Variable Model for Distance Metric Learning Matthew Der and Lawrence K. Saul Department of Computer Science and Engineering University of California, San Diego Add to Reading List Source URL: cseweb.ucsd.edu Language: English - Date: 2012-12-11 16:44:36 Statistical classification Data analysis Principal component analysis Singular value decomposition Linear classifier K-nearest neighbor algorithm Dimension reduction Linear discriminant analysis Expectation–maximization algorithm Statistics Multivariate statistics Machine learning
133	Trust-Based Fusion of Untrustworthy Information in Crowdsourcing Applications Matteo Venanzi, Alex Rogers, Nicholas R. Jennings University of Southampton Southampton, UK Add to Reading List Source URL: eprints.soton.ac.uk Language: English - Date: 2013-10-30 11:22:56 Estimation theory Social psychology Statistical theory Expectation–maximization algorithm Missing data Maximum likelihood Crowdsourcing Trust Confidence interval Statistics Technology Science
134	LNCSAutomatic Rank Determination in Projective Nonnegative Matrix Factorization Add to Reading List Source URL: homepages.inf.ed.ac.uk Language: English - Date: 2013-09-03 10:14:17 Matrix theory Multivariate statistics Singular value decomposition Non-negative matrix factorization Principal component analysis Eigenvalues and eigenvectors Rank Expectation–maximization algorithm Matrix decomposition Algebra Mathematics Linear algebra
135	LATENT COINCIDENCE ANALYSIS: A HIDDEN VARIABLE MODEL FOR DISTANCE METRIC LEARNING Matthew Der and Lawrence K. Saul, University of California, San Diego 1 INTRODUCTION Add to Reading List Source URL: cseweb.ucsd.edu Language: English - Date: 2012-12-11 16:44:37 Statistical models K-nearest neighbor algorithm Mixture model Estimation theory Expectation–maximization algorithm Statistics Machine learning Artificial intelligence
136	Tell Me What You See and I will Show You Where It Is Jia Xu1 Alexander G. Schwing2 Raquel Urtasun2,3 1 University of Wisconsin-Madison 2 University of Toronto 3 TTI Chicago Add to Reading List Source URL: www.cs.toronto.edu Language: English - Date: 2014-04-21 14:33:16 Theoretical computer science Graphical models Categorical data Artificial intelligence Structured prediction Conditional random field Expectation–maximization algorithm Market segmentation Probabilistic latent semantic analysis Statistics Mathematics Machine learning
137	Tensor Analyzers Yichuan Tang Ruslan Salakhutdinov Add to Reading List Source URL: www.cs.toronto.edu Language: English - Date: 2013-05-13 22:23:10 Binary operations Tensor product Tensor Z3 Expectation–maximization algorithm One-shot learning Vehicle Identification Number Computing Statistics Mathematics Tensors
138	Leveraging User Libraries to Bootstrap Collaborative Filtering Laurent Charlin ∗ Add to Reading List Source URL: www.cs.toronto.edu Language: English - Date: 2014-07-17 22:09:04 Econometrics Information retrieval Estimation theory Information systems Collaborative filtering Expectation–maximization algorithm Cold start Regression analysis Recommender system Statistics Collective intelligence Information science
139	CHAPTER 6. MODEL AND MODEL SUMMARY OUTPUT CHAPTER 6. MODEL AND MODEL SUMMARY OUTPUT Following estimation of a model, various output file listings and graphical displays are produced. The specific output produced is con Add to Reading List Source URL: www.statisticalinnovations.com Language: English - Date: 2009-05-18 08:48:12 Estimation theory Akaike information criterion Psychometrics Expectation–maximization algorithm Bootstrapping Structural equation modeling Bayesian information criterion Statistics Statistical inference Model selection
140	Expectation Propagation for Continuous Time Bayesian Networks Uri Nodelman Stanford University Add to Reading List Source URL: robotics.stanford.edu Language: English - Date: 2005-06-05 21:42:48 Graphical models Markov processes Probability theory Exponentials Belief propagation Coding theory Continuous-time Markov process Normal distribution Expectation–maximization algorithm Statistics Mathematics Probability and statistics

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