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Expectation–maximization algorithm
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#	Item
621	5. Mixtures and Products of Experts University of Cambridge Engineering Part IIB Module 4F10: Statistical Pattern Processing Add to Reading List Source URL: mi.eng.cam.ac.uk Language: English - Date: 2013-11-13 04:10:48 Maximum likelihood Likelihood function Multivariate normal distribution Mixture model Normal distribution Statistics Estimation theory Expectation–maximization algorithm
622	Learning Stochastic Feedforward Neural Networks Yichuan Tang Department of Computer Science University of Toronto Toronto, Ontario, Canada. Add to Reading List Source URL: www.cs.toronto.edu Language: English - Date: 2013-11-19 21:06:13 Machine learning Estimation theory Statistical theory Expectation–maximization algorithm Bayesian network Gibbs sampling Perceptron Kullback–Leibler divergence Mixture model Statistics Statistical models Neural networks
623	Journal of Machine Learning Research[removed]1031 Submitted 9/12; Revised 1/13; Published 4/13 Variational Inference in Nonconjugate Models Chong Wang Add to Reading List Source URL: www.cs.princeton.edu Language: English - Date: 2013-06-14 05:31:44 Estimation theory Gamma distribution Conjugate prior Exponential family Marginal likelihood Delta method Sufficient statistic Kullback–Leibler divergence Expectation–maximization algorithm Statistics Statistical theory Bayesian statistics
624	A Statistician Plays Darts Ryan J. Tibshirani∗ Andrew Price† Jonathan Taylor‡ Add to Reading List Source URL: www.stat.cmu.edu Language: English - Date: 2012-01-20 13:00:19 Statistical theory Regression analysis Normal distribution Expectation–maximization algorithm Multivariate normal distribution Standard deviation Errors and residuals in statistics Maximum likelihood Fourier transform Statistics Estimation theory Summary statistics
625	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL., NO. 1 Human Action Recognition by Semi-Latent Topic Models Add to Reading List Source URL: www.cs.sfu.ca Language: English - Date: 2009-02-05 14:48:35 Latent Dirichlet allocation Expectation–maximization algorithm Topic model Probabilistic latent semantic analysis Maximum likelihood Statistics Statistical natural language processing Estimation theory
626	Matrix Updates for Perceptron Training of Continuous Density Hidden Markov Models Chih-Chieh Cheng Department of Computer Science and Engineering, University of California, San Diego Fei Sha Add to Reading List Source URL: www-bcf.usc.edu Language: English - Date: 2011-08-01 12:49:00 Artificial intelligence Neural networks Perceptron Expectation–maximization algorithm Hidden Markov model Speech recognition Cholesky decomposition Part-of-speech tagging Statistical classification Statistics Machine learning Markov models
627	This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and shar Add to Reading List Source URL: www.soest.hawaii.edu Language: English - Date: 2013-10-29 16:26:31 Bayesian inference Bayesian probability Bayesian network Bayesian linear regression Robust Bayes analysis Conjugate prior Bayesian information criterion Expectation–maximization algorithm Hierarchical Bayes model Statistics Bayesian statistics Bayes factor
628	Markov Topic Models Chong Wang ∗ Computer Science Dept. Princeton University Princeton, NJ 08540 Add to Reading List Source URL: www.cs.princeton.edu Language: English - Date: 2010-08-18 22:20:56 Mathematics Dynamic topic model Latent Dirichlet allocation Topic model Perplexity Markov random field Expectation–maximization algorithm Information retrieval Statistical natural language processing Statistics Machine learning
629	An Adaptive Learning Rate for Stochastic Variational Inference Rajesh Ranganath Princeton University, 35 Olden St., Princeton, NJ[removed]RAJESHR @ CS . PRINCETON . EDU Add to Reading List Source URL: www.cs.princeton.edu Language: English - Date: 2013-03-19 08:30:44 Stochastic optimization Probability theory Stochastic approximation Mathematical optimization Bayesian inference Latent Dirichlet allocation Expectation–maximization algorithm Variational Bayesian methods Statistics Bayesian statistics Estimation theory
630	MODELING INTERACTIONS FROM EMAIL COMMUNICATION Dong Zhang1 , Daniel Gatica-Perez1 , Deb Roy2 and Samy Bengio1 1 IDIAP Research Institute, Swiss Federal Institute of Technology, Lausanne, Switzerland 2 Add to Reading List Source URL: www.media.mit.edu Language: English - Date: 2006-03-27 17:15:08 Statistical natural language processing Categorical data Probabilistic latent semantic analysis Estimation theory Markov chain Hidden Markov model Expectation–maximization algorithm Matrix Statistics Email Markov models

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