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Expectation–maximization algorithm
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#	Item
241	Latent Class Analysis Jeroen K. Vermunt & Jay Magidson The basic idea underlying latent class (LC) analysis is a very simple one: some of the parameters of a postulated statistical model differ across unobserved subgroup Add to Reading List Source URL: statisticalinnovations.com Language: English - Date: 2006-09-05 12:11:48 Econometrics Categorical data Multivariate statistics Latent class model Expectation–maximization algorithm Latent variable model Structural equation modeling Logistic regression Mixture model Statistics Regression analysis Statistical models
242	MACQUARIE UNIVERSITY STATISTICS DEPARTMENT SEMINAR Speaker: A/Prof Xinyuan Song, Department of Statistics, Chinese University of Hong Kong Date: Tuesday 28 April 2015, Time 2pm Venue: E4A523 Title: Statistics analysis of Add to Reading List Source URL: stat.mq.edu.au Language: English - Date: 2015-04-19 22:05:02 Bioinformatics Hidden Markov model Expectation–maximization algorithm Latent variable Estimation theory Book:Machine Learning - The Complete Guide Statistics Statistical models Markov models
243	An Introduction to Probability Models for Marketing Research Peter S. Fader University of Pennsylvania Bruce G. S. Hardie Add to Reading List Source URL: www.brucehardie.com Language: English - Date: 2013-07-16 16:45:35 M-estimators Bayesian statistics Maximum likelihood Likelihood function Statistical models Expectation–maximization algorithm Statistics Estimation theory Statistical theory
244	PDF Document Add to Reading List Source URL: hrl.informatik.uni-freiburg.de Language: English - Date: 2003-12-15 05:10:20 Robot control Linear filters Bioinformatics Hidden Markov model Expectation–maximization algorithm Markov chain Robotics Kalman filter 3D scanner Statistics Estimation theory Markov models
245	Learning Motion Patterns of People for Compliant Robot Motion Maren Bennewitz† Grzegorz Cielniak‡ † Add to Reading List Source URL: hrl.informatik.uni-freiburg.de Language: English - Date: 2004-03-30 10:22:04 Likelihood function 3D scanner Dimensional analysis Statistics Estimation theory Expectation–maximization algorithm
246	Multiple Imputation of Missing Values in Economic Surveys: Comparison of Competing Algorithms Masayoshi Takahashi1,2 and Takayuki Ito1 National Statistics Center, Tokyo, JAPAN 2 Corresponding author: Masayoshi Takahashi, Add to Reading List Source URL: www.statistics.gov.hk Language: English - Date: 2013-08-22 04:39:17 Statistical inference Monte Carlo methods Computational statistics Imputation SPSS Resampling Bootstrapping Expectation–maximization algorithm SAS Statistics Data analysis Missing data
247	GRAMMAR INDUCTION AND PARSING WITH DEPENDENCY-AND-BOUNDARY MODELS A DISSERTATION SUBMITTED TO THE DEPARTMENT OF COMPUTER SCIENCE AND THE COMMITTEE ON GRADUATE STUDIES Add to Reading List Source URL: nlp.stanford.edu Language: English - Date: 2013-12-13 22:01:08 Natural language processing Corpus linguistics Speech recognition Markov models Expectation–maximization algorithm Algorithm Lateen Part-of-speech tagging Mathematical optimization Linguistics Computational linguistics Science
248	A Continuum from Mixtures to Products: Aggregation under Bias Amos J. Storkey Zhanxing Zhu Jinli Hu School of Informatics,University of Edinburgh Add to Reading List Source URL: bigml.cs.tsinghua.edu.cn Language: English - Date: 2014-06-22 05:40:20 Probability and statistics Kullback–Leibler divergence Rényi entropy Expectation–maximization algorithm Entropy Perplexity Principle of maximum entropy Statistics Information theory Statistical theory
249	Chapter 11 Scaling the PIRLS 2006 Reading Assessment Data Pierre Foy, Joseph Galia, and Isaac Li 11.1 Add to Reading List Source URL: timssandpirls.bc.edu Language: English - Date: 2012-01-04 16:42:05 Statistical models Econometrics Statistical theory Item response theory Expectation–maximization algorithm Variance Linear regression Normal distribution Polytomous Rasch model Statistics Estimation theory Psychometrics
250	Exploring Compositional High Order Pattern Potentials for Structured Output Learning Yujia Li, Daniel Tarlow, Richard Zemel University of Toronto Toronto, ON, Canada, M5S 3G4 {yujiali, dtarlow, zemel}@cs.toronto.edu Add to Reading List Source URL: www.cs.toronto.edu Language: English - Date: 2013-04-19 18:32:04 Graphical models Statistical models Theoretical computer science Bayesian statistics Conditional random field Boltzmann machine Expectation–maximization algorithm Mixture model Pattern recognition Statistics Machine learning Probability and statistics

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