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Mixture theory
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#	Item
201	A Latent Variable Model for Geographic Lexical Variation Jacob Eisenstein Brendan O’Connor Noah A. Smith Eric P. Xing School of Computer Science Carnegie Mellon University Add to Reading List Source URL: www.cs.cmu.edu Language: English - Date: 2010-09-02 12:44:15 Estimation theory Dynamic topic model Statistical models Regression analysis Latent Dirichlet allocation Dirichlet process Mixture model Topic model Linear regression Statistics Statistical natural language processing Econometrics
202	Mixture models for describing some strange looking datasets Add to Reading List Source URL: www.rsc.org Language: English - Date: 2014-08-20 04:20:15 Statistical models Statistical theory Expectation–maximization algorithm Missing data Mixture model Maximum likelihood Least squares Normal distribution Mixture distribution Statistics Estimation theory Regression analysis
203	CS 294-2, Grouping and Recognition (Prof. Jitendra Malik) Sept 1st, 1999 Lecture #4 (Expectation Maximization Algorithm) DRAFT Notes by Rene Vidal Mixture models with incomplete data Add to Reading List Source URL: www.eecs.berkeley.edu Language: English - Date: 2003-07-25 14:54:43 Statistical theory Expectation–maximization algorithm Missing data Normal distribution Mixture model Maximum likelihood Multivariate normal distribution Statistics Estimation theory Cluster analysis
204	SECTION 2 ASPHALT EMULSION THEORY 2.1 Overview Asphalt emulsions, used in road construction and maintenance, may be defined as a homogeneous mixture of minute asphalt droplets suspended in a continuous water phase. These Add to Reading List Source URL: www.mdt.mt.gov Language: English - Date: 2004-02-06 23:50:14 Soft matter Condensed matter physics Dosage forms Building materials Emulsion Surfactant Asphalt Colloid Road surface Chemistry Colloidal chemistry Matter
205	Markov Chain Monte Carlo With Mixtures of Mutually Singular Distributions Raphael G OTTARDO and Adrian E. R AFTERY Markov chain Monte Carlo (MCMC) methods for Bayesian computation are mostly used when the dominating meas Add to Reading List Source URL: www.stat.washington.edu Language: English - Date: 2010-06-07 16:59:42 Markov models Markov chain Monte Carlo Probability distributions Measure theory Metropolis–Hastings algorithm Markov chain Gibbs sampling Mixture distribution Singular distribution Mathematical analysis Statistics Monte Carlo methods
206	Collaborative Information Processing in Wireless Sensor Networks for Diffusive Source Estimation by Add to Reading List Source URL: www.ece.unm.edu Language: English - Date: 2013-03-21 10:27:20 Wireless networking Wireless sensor network Technology Sensor node Estimation theory Mixture model Statistics Probability and statistics Signal processing
207	Overview Bayesian learning theory applied to human cognition Robert A. Jacobs1∗ and John K. Kruschke2 Probabilistic models based on Bayes’ rule are an increasingly popular approach to Add to Reading List Source URL: www.indiana.edu Language: English - Date: 2012-01-07 13:16:34 Bayesian inference Bayesian probability Graphical model Expectation–maximization algorithm Prior probability Hierarchical Bayes model Mixture model Latent variable Dirichlet process Statistics Bayesian statistics Bayesian network
208	Performance of Bayesian Model Selection Criteria for Gaussian Mixture Models 1 Russell J. Steele McGill University Adrian E. Raftery Add to Reading List Source URL: www.stat.washington.edu Language: English - Date: 2009-09-18 15:00:15 Statistical theory Model selection Bayesian inference Akaike information criterion Conjugate prior Variational Bayesian methods Mixture model Normal distribution Maximum likelihood Statistics Bayesian statistics Estimation theory
209	Scaling Nonparametric Bayesian Inference via Subsample-Annealing arXiv:1402.5473v1 [stat.ML] 22 Feb[removed]Fritz Obermeyer Add to Reading List Source URL: arxiv.org Language: English - Date: 2014-02-24 20:28:21 Computational statistics Markov models Estimation theory Statistical inference Gibbs sampling Hidden Markov model Simulated annealing Mixture model Bayesian inference Statistics Probability and statistics Monte Carlo methods
210	REVISITING SEMI-CONTINUOUS HIDDEN MARKOV MODELS K. Riedhammer1 , T. Bocklet1 , A. Ghoshal2,3 , D. Povey4 1 Pattern Recognition Lab, University of Erlangen-Nuremberg, G ERMANY 2 Add to Reading List Source URL: homepages.inf.ed.ac.uk Language: English - Date: 2012-06-12 09:45:08 Probability and statistics Estimation theory Markov models Bioinformatics Hidden Markov model Mixture model Speech recognition Expectation–maximization algorithm Pattern recognition Statistics Machine learning Computational statistics

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