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Expectation–maximization algorithm
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#	Item
341	Perturb-and-MAP Random Fields: Using Discrete Optimization to Learn and Sample from Energy Models George Papandreou1 and Alan L. Yuille1,2 Department of Statistics, University of California, Los Angeles 2 Department of B Add to Reading List Source URL: www.stat.ucla.edu Language: English - Date: 2011-11-01 22:20:35 Statistical inference Probability theory Bayesian statistics Markov random field Maximum likelihood Gumbel distribution Expectation–maximization algorithm Perturbation theory Kullback–Leibler divergence Statistics Estimation theory Statistical theory
342	Microsoft Word - GMM Estimation Mixture 4 June 2012.docx Add to Reading List Source URL: fbe.unimelb.edu.au Language: English - Date: 2013-08-05 02:20:06 Maximum likelihood Mixture model Bayes estimator Expectation–maximization algorithm Gamma distribution M-estimator Log-normal distribution Normal distribution Estimator Statistics Estimation theory Generalized method of moments
343	26th European Meeting of Statisticians, Toruń 2006 SCIENTIFIC PROGRAM Main Lectures Opening Lecture: Gareth ROBERTS, Exact simulation and inference for diffusions Forum Lectures: Add to Reading List Source URL: www.ems2006.umk.pl Language: English - Date: 2006-07-17 06:03:05 Estimation theory Statistical methods Statistical theory Nonparametric regression Robust regression Optimal design Seismic inversion Markov chain Expectation–maximization algorithm Statistics Regression analysis Statistical inference
344	A Practical Algorithm for Network Topology Inference Dimitri Marinakis, Gregory Dudek Centre for Intelligent Machines, McGill University 3480 University St, Montreal, Quebec, Canada H3A 2A7 {dmarinak,dudek}@cim.mcgill.ca Add to Reading List Source URL: www.cim.mcgill.ca Language: English - Date: 2006-02-27 13:10:52 Wireless sensor network Estimation theory Electronics Connectivity Wireless ad-hoc network Sensor node Topology Expectation–maximization algorithm Maximum likelihood Statistics Wireless networking Technology
345	Fast Algorithms for Large-State-Space HMMs with Applications to Web Usage Analysis Pedro F. Felzenszwalb1 , Daniel P. Huttenlocher2 , Jon M. Kleinberg2 1 Add to Reading List Source URL: www.cs.cornell.edu Language: English - Date: 2004-04-05 12:10:08 Probability and statistics Dynamic programming Bioinformatics Hidden Markov model Viterbi algorithm Computational linguistics Expectation–maximization algorithm Forward–backward algorithm Markov chain Markov models Statistics Error detection and correction
346	Journal of Machine Learning Research[removed]1140 Submitted 10/09; Revised 1/10; Published 3/10 Continuous Time Bayesian Network Reasoning and Learning Engine Christian R. Shelton Add to Reading List Source URL: www.jmlr.org Language: English - Date: 2010-03-09 15:22:27 Bayesian statistics Markov models Bayesian network Gibbs sampling Markov chain Expectation–maximization algorithm Machine learning Statistics Networks Graphical models
347	Title: Risk Measures for the PSE Index Using Hidden Markov Models Add to Reading List Source URL: www.bsp.gov.ph Language: English - Date: 2012-06-19 22:55:40 Markov processes Statistical inference Statistical dependence Hidden Markov model Markov chain Normal distribution Expectation–maximization algorithm Markov property Confidence interval Statistics Markov models Probability theory
348	Sharding Social Networks Quang Duong Sharad Goel University of Michigan Add to Reading List Source URL: 5harad.com Language: English - Date: 2014-09-24 20:04:52 Shard Graph theory Expectation–maximization algorithm Degree distribution Topology Data management Statistics Mathematics
349	Learning for Stereo Vision Using the Structured Support Vector Machine Yunpeng Li Daniel P. Huttenlocher Department of Computer Science, Cornell University Ithaca, NY 14853 {yuli,dph}@cs.cornell.edu Add to Reading List Source URL: www.cs.cornell.edu Language: English - Date: 2008-07-27 07:57:45 Statistical inference Statistical classification Statistical models Loss function Parametric model Maximum likelihood Support vector machine Expectation–maximization algorithm Supervised learning Statistics Statistical theory Estimation theory
350	Kernel Measures of Independence for non-iid Data∗ Le Song† School of Computer Science Carnegie Mellon University, Pittsburgh, USA [removed] Add to Reading List Source URL: users.cecs.anu.edu.au Language: English - Date: 2009-06-21 14:46:49 Multivariate statistics Statistical dependence Estimation theory Expectation–maximization algorithm Missing data Covariance Mutual information Convolution Principal component analysis Statistics Mathematical analysis Covariance and correlation

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