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Expectation–maximization algorithm
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#	Item
661	Iliadis et al. BMC Genetics 2010, 11:78 http://www.biomedcentral.com[removed]RESEARCH ARTICLE Open Access Add to Reading List Source URL: www.ncbi.nlm.nih.gov Language: English Population genetics Genetic genealogy Haplotype International HapMap Project Genetic association Linkage disequilibrium Expectation–maximization algorithm Genetics Biology Classical genetics
662	Choosing a Reliable (Extended William Add to Reading List Source URL: www.cs.berkeley.edu Language: English - Date: 2004-09-11 20:56:29 Finite fields Algorithm Mathematical logic PP XTR Probabilistic complexity theory Expectation–maximization algorithm Sipser–Lautemann theorem Theoretical computer science Applied mathematics Mathematics
663	Unsupervised Structure Prediction with Non-Parallel Multilingual Guidance Shay B. Cohen Dipanjan Das Noah A. Smith Language Technologies Institute School of Computer Science Carnegie Mellon University Add to Reading List Source URL: www.cs.cmu.edu Language: English - Date: 2011-06-21 13:09:56 Machine learning Estimation theory Corpus linguistics Markov models Bioinformatics Mixture model Expectation–maximization algorithm Part-of-speech tagging Stochastic context-free grammar Statistics Probability and statistics Natural language processing
664	TIMSS and PIRLS Achievement Scaling Methodology1 The TIMSS and PIRLS approach to scaling the achievement data, based on item response theory (IRT) scaling with marginal estimation, was developed originally by Educational Add to Reading List Source URL: timssandpirls.bc.edu Language: English - Date: 2012-12-12 11:58:59 Econometrics Educational research Regression analysis Item response theory Expectation–maximization algorithm Variance Normal distribution Linear regression Trends in International Mathematics and Science Study Statistics Estimation theory Statistical models
665	A Note on the Expectation-Maximization (EM) Algorithm ChengXiang Zhai Department of Computer Science University of Illinois at Urbana-Champaign November 2, 2004 Add to Reading List Source URL: www.cs.ust.hk Language: English - Date: 2006-03-11 03:50:39 Bayesian statistics Expectation–maximization algorithm Missing data Statistical models Maximum likelihood Likelihood function Mixture model Kullback–Leibler divergence Extremum estimator Statistics Estimation theory Statistical theory
666	Learning Nonlinear Dynamic Models from Non-sequenced Data Tzu-Kuo Huang Machine Learning Department Carnegie Mellon University Add to Reading List Source URL: www.cs.cmu.edu Language: English - Date: 2010-05-20 16:35:49 Computational statistics Dimension Nonlinear dimensionality reduction Expectation–maximization algorithm Dynamical system Normal distribution Semidefinite embedding Lorenz attractor Statistics Multivariate statistics Dimension reduction
667	Bayesian Unsupervised Topic Segmentation Jacob Eisenstein and Regina Barzilay Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology 77 Massachusetts Ave., Cambridge MA 02139 {jacob Add to Reading List Source URL: people.csail.mit.edu Language: English - Date: 2008-09-21 17:44:39 Image processing Natural language processing Speech recognition Segmentation Expectation–maximization algorithm Latent Dirichlet allocation Speech segmentation Market segmentation Text segmentation Statistics Computational linguistics Science
668	Package ‘flexmix’ September 24, 2014 Type Package Title Flexible Mixture Modeling Version[removed]Description FlexMix implements a general framework for finite Add to Reading List Source URL: cran.r-project.org Language: English - Date: 2014-09-24 11:27:12 Regression analysis Econometrics Statistical inference Bootstrapping Akaike information criterion Bayesian information criterion Binomial regression Mixture model Expectation–maximization algorithm Statistics Categorical data Model selection
669	Combinatorial Markov Random Fields Ron Bekkerman1 , Mehran Sahami2 , and Erik Learned-Miller1 1 Department of Computer Science, University of Massachusetts, Amherst MA 01002, {ronb\|elm}@cs.umass.edu, Add to Reading List Source URL: vis-www.cs.umass.edu Language: English - Date: 2008-01-02 15:36:23 Cluster analysis Expectation–maximization algorithm Mutual information Unsupervised learning Statistical classification Semi-supervised learning Gibbs sampling Consensus clustering Adjusted mutual information Statistics Machine learning Computational statistics
670	Connections between the Lines: Augmenting Social Networks with Text Jonathan Chang Jordan Boyd-Graber Add to Reading List Source URL: www.cs.princeton.edu Language: English - Date: 2010-08-18 22:20:55 Science Machine learning Entity-relationship model Topic model Latent Dirichlet allocation Expectation–maximization algorithm Maximum likelihood N-gram Statistics Statistical natural language processing Estimation theory

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