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Expectation–maximization algorithm
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331	Assessing Degeneracy in Statistical Models of Social Networks 1 Mark S. Handcock University of Washington, Seattle Working Paper no. 39 Add to Reading List Source URL: www.csss.washington.edu Language: English - Date: 2009-01-07 17:16:05 Statistical theory M-estimators Maximum likelihood Exponential family Likelihood function Entailment Expectation–maximization algorithm Estimation theory Statistics Logic
332	44 PROBIT LATENT CLASS ANALYSIS Add to Reading List Source URL: john-uebersax.com Language: English - Date: 2009-07-16 17:15:20 Latent class model Latent variable model Item response theory Latent variable Rasch model Expectation–maximization algorithm Polychoric correlation Probit Scale analysis Statistics Statistical models Psychometrics
333	Collaborative Filtering and the Missing at Random Assumption Benjamin M. Marlin Richard S. Zemel Yahoo! Research and Department of Add to Reading List Source URL: www.cs.toronto.edu Language: English - Date: 2009-06-29 08:36:44 Cluster analysis Collaborative filtering Social information processing Mixture model Expectation–maximization algorithm Recommender system Conjugate prior Statistical model Maximum likelihood Statistics Estimation theory Statistical theory
334	1 Chapter 4 Efficient Likelihood Estimation and Related Tests 1. Maximum likelihood and efficient likelihood estimation Add to Reading List Source URL: www.stat.washington.edu Language: English - Date: 2015-02-09 12:24:35 Maximum likelihood Expectation–maximization algorithm Likelihood function Likelihood-ratio test Score Hájek–Le Cam convolution theorem Fisher consistency Statistics Estimation theory Statistical theory
335	Chap.8 Learning and generalization [Book, Chap. 6] Avoid using a model with too little flexibility to model the underlying nonlinear relation adequately (underfitting ), and using a model with too much flexibility, which Add to Reading List Source URL: www.ocgy.ubc.ca Language: English - Date: 2013-11-04 00:57:35 Maximum likelihood Mean squared error Likelihood function Standard deviation Kullback–Leibler divergence Expectation–maximization algorithm Statistics Estimation theory Normal distribution
336	Statistics 582, Problem Set 1 Wellner; [removed]Reading: • Chapter 6, Sections 4-6 and 10; Lehmann and Casella, TPE, pages[removed]and[removed]. • Course Notes, sections 4.5 and 4.6. Add to Reading List Source URL: www.stat.washington.edu Language: English - Date: 2015-01-07 12:21:00 Identifiability Expectation–maximization algorithm Likelihood function Errors-in-variables models Differences between codices Sinaiticus and Vaticanus Estimation theory Statistics Maximum likelihood
337	The Multidimensional Wisdom of Crowds Peter Welinder1 Steve Branson2 Serge Belongie2 Pietro Perona1 1 California Institute of Technology, 2 University of California, San Diego {welinder,perona}@caltech.edu Add to Reading List Source URL: machinelearning.wustl.edu Language: English - Date: 2013-03-25 14:51:25 Punctuation Computational statistics Annotation Natural language processing Expectation–maximization algorithm Pattern recognition Science Statistics Computational linguistics Chess notation
338	STAT 538 Lecture 8 Maximum Entropy Models c Marina Meil˘a Add to Reading List Source URL: www.stat.washington.edu Language: English - Date: 2015-02-17 20:52:05 Bayesian statistics Kullback–Leibler divergence Thermodynamics Normal distribution Principle of maximum entropy Convex optimization Expectation–maximization algorithm Entropy Maximum entropy probability distribution Statistics Probability and statistics Statistical theory
339	Chapter 1 Expectation-Maximization methods for solving (PO)MDPs and optimal control problems Marc Toussaint1 , Amos Storkey2 and Stefan Harmeling3 As this book demonstrates, the development of efficient probabilistic in Add to Reading List Source URL: homepages.inf.ed.ac.uk Language: English - Date: 2012-04-27 07:18:55 Missing data Markov decision process Symbol Statistics Estimation theory Expectation–maximization algorithm
340	1 Deep Neural Networks for Acoustic Modeling in Speech Recognition Geoffrey Hinton, Li Deng, Dong Yu, George Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara Sainath, an Add to Reading List Source URL: static.googleusercontent.com Language: English - Date: 2012-10-26 02:52:04 Hidden Markov model Artificial neural network Boltzmann machine Generative model Feedforward neural network Speech recognition Backpropagation Expectation–maximization algorithm Latent variable Statistics Neural networks Machine learning

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