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Expectation
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#	Item
201	Hybrid Variational/Gibbs Collapsed Inference in Topic Models Max Welling∗ Dept. Computer Science University of California Irvine Irvine, CA, USA Add to Reading List Source URL: www.ics.uci.edu Language: English - Date: 2008-05-14 13:35:04 Variational Bayesian methods Expectation–maximization algorithm Gibbs sampling Bayesian network Centimetre–gram–second system of units Belief propagation Bayesian inference Latent Dirichlet allocation Perplexity Statistics Bayesian statistics Graphical models
202	An Analysis of Single-Layer Networks in Unsupervised Feature Learning Adam Coates Stanford University Computer Science Dept. Add to Reading List Source URL: ai.stanford.edu Language: English - Date: 2014-06-02 17:06:00 K-means clustering Cluster analysis Pattern recognition Supervised learning Linear classifier Expectation–maximization algorithm Object recognition Mixture model Algorithm Statistics Machine learning Unsupervised learning
203	Pattern Recognition – 3021 Rapid and brief communication www.elsevier.com/locate/patcog Add to Reading List Source URL: www.tiberiocaetano.com Language: English - Date: 2008-05-10 06:34:39 Data mining Cluster analysis Mixture model Segmentation Statistical model Receiver operating characteristic Color model Expectation–maximization algorithm Statistics Image processing Machine learning
204	A DETERMINISTIC ANNEALING EM ALGORITHM FOR AUTOMATIC MUSIC TRANSCRIPTION Tian Cheng, Simon Dixon and Matthias Mauch Centre for Digital Music, Queen Mary University of London {tian.cheng, simon.dixon, matthias.mauch} @eec Add to Reading List Source URL: matthiasmauch.de Language: English - Date: 2014-01-28 06:16:13 Missing data Mixture model Statistics Estimation theory Expectation–maximization algorithm
205	Deterministic Latent Variable Models and their Pitfalls Max Welling∗ Chaitanya Chemudugunta Nathan Sutter Bren School of Information and Computer Science University of California, Irvine Add to Reading List Source URL: www.ics.uci.edu Language: English - Date: 2008-04-16 00:56:41 Information theory Statistical natural language processing Categorical data Probabilistic latent semantic analysis Expectation–maximization algorithm Perplexity Entropy Kullback–Leibler divergence Likelihood function Statistics Estimation theory Statistical theory
206	arXiv:1312.6114v9 [stat.ML] 10 AprAuto-Encoding Variational Bayes Max Welling Machine Learning Group Add to Reading List Source URL: www.ics.uci.edu Language: English - Date: 2014-04-19 07:05:02 Statistical models Statistical inference Expectation–maximization algorithm Missing data Variational Bayesian methods Maximum likelihood Normal distribution Bayesian network Marginal likelihood Statistics Estimation theory Bayesian statistics
207	Bayesian Sets Zoubin Ghahramani∗ and Katherine A. Heller Gatsby Computational Neuroscience Unit University College London London WC1N 3AR, U.K. Add to Reading List Source URL: www.gatsby.ucl.ac.uk Language: English - Date: 2006-01-13 14:27:22 Machine learning Statistical inference Data mining Bayesian inference Cluster analysis Prior probability Information retrieval Expectation–maximization algorithm One-shot learning Statistics Bayesian statistics Statistical theory
208	Note on “Obtaining the Maximum Likelihood Estimates in Incomplete R C Contingency Tables...” Michael Friendly, York University Abstract This note extends the construction of the design matrix used for estimating ce Add to Reading List Source URL: www.datavis.ca Language: English - Date: 2012-09-12 11:48:20 Design matrix Matrix Expectation–maximization algorithm SAS Statistics Regression analysis 1J
209	Maximum-likelihood estimation for the offset normal shape distributions using EM Alfred Kume Institute of Mathematics, Statistics and Actuarial Science, University of Kent Canterbury, CT2 7NF,UK Add to Reading List Source URL: www.ics.uci.edu Language: English - Date: 2010-09-23 15:44:42 Normal distribution Multivariate normal distribution Complex normal distribution Expectation–maximization algorithm Moment Exponential family Statistics Mathematical analysis Probability and statistics
210	Memory Bounded Inference in Topic Models Ryan Gomes GOMES @ VISION . CALTECH . EDU Dept. of Computation and Neural Systems, California Institute of Technology, Pasadena, CAUSA Max Welling Add to Reading List Source URL: www.ics.uci.edu Language: English - Date: 2008-09-12 18:11:33 Estimation theory Expectation–maximization algorithm Missing data Mixture model Latent Dirichlet allocation Dirichlet process Time complexity Calculus of variations Statistics Theoretical computer science Statistical natural language processing

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