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Mixture theory
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131	Statistical Models David M. Blei Columbia University October 14, 2014 We have discussed graphical models. Graphical models are a formalism for representing families of probability distributions. They are connected to ef Add to Reading List Source URL: www.cs.columbia.edu Language: English - Date: 2015-03-12 00:16:19 Statistical theory Bayesian inference Prior probability Maximum likelihood Normal distribution Maximum a posteriori estimation Expectation–maximization algorithm Mixture model Statistics Bayesian statistics Estimation theory
132	Convex Relaxations of Latent Variable Training Yuhong Guo and Dale Schuurmans Department of Computing Science University of Alberta {yuhong,dale}@cs.ualberta.ca Add to Reading List Source URL: webdocs.cs.ualberta.ca Language: English - Date: 2008-01-19 16:30:45 Estimation theory Operations research Linear programming Econometrics Expectation–maximization algorithm Convex optimization Maximum likelihood Mixture model Convex function Statistics Mathematical optimization Mathematical sciences
133	Online Estimation of B-Spline Mixture Models From TOF-PET List-Mode Data Colas Schretter, Jianyong Sun and Leif Kobbelt Abstract—In emission tomography, images are usually represented by regular grids of voxels or over Add to Reading List Source URL: homepages.ulb.ac.be Language: English - Date: 2011-02-23 10:40:01 Cluster analysis Splines Machine learning Mixture model Expectation–maximization algorithm Gaussian function Normal distribution Multivariate normal distribution Iterative reconstruction Statistics Probability and statistics Estimation theory
134	BAYESIAN STATISTICS 8 Proceedings of the Eighth Valencia International Meeting June 2–6, 2006 Edited by Add to Reading List Source URL: www.uv.es Language: English - Date: 2007-10-03 04:30:10 Bayesian inference Econometrics Philosophy of science Bayesian probability Bayes factor Bayesian linear regression Dirichlet process Mixture model Regression analysis Statistics Bayesian statistics Statistical theory
135	HYPERPARAMETERS: OPTIMIZE, OR INTEGRATE OUT? David J.C. MacKay Cavendish Laboratory, Cambridge, CB3 0HE. United Kingdom. Add to Reading List Source URL: bayes.wustl.edu Language: English - Date: 2010-12-21 17:21:30 Estimation theory Statistical inference Normal distribution Mixture model Hyperparameter Maximum likelihood Variational Bayesian methods Least squares support vector machine Statistics Bayesian statistics Statistical theory
136	Markov Chain Monte Carlo (and Bayesian Mixture Models) David M. Blei Columbia University October 14, 2014 Add to Reading List Source URL: www.cs.columbia.edu Language: English - Date: 2015-03-12 00:16:19 Statistical models Monte Carlo methods Markov models Expectation–maximization algorithm Mixture model Gibbs sampling Conjugate prior Markov chain Monte Carlo Variational Bayesian methods Statistics Bayesian statistics Estimation theory
137	A Generative Model for 3D Urban Scene Understanding from Movable Platforms Andreas Geiger and Martin Lauer Department of Measurement and Control Karlsruhe Institute of Technology Raquel Urtasun Add to Reading List Source URL: www.cvlibs.net Language: English - Date: 2012-03-05 09:32:49 Estimation theory Bayesian statistics Markov models Maximum likelihood Parametric model Mixture model Dimensional analysis Conjugate prior 3D modeling Statistics Statistical theory Statistical models
138	A Course in Machine Learning Hal Daumé III 14 \| E XPECTATION M AXIMIZATION Add to Reading List Source URL: ciml.info Language: English - Date: 2014-09-24 16:08:33 Machine learning Cluster analysis Expectation–maximization algorithm Missing data Maximum likelihood Mixture model K-means clustering Likelihood-ratio test Dimensional analysis Statistics Estimation theory Statistical theory
139	LATENT CLASS ANALYSIS WITH SAMPLING WEIGHTS: A MAXIMUM LIKELIHOOD APPROACH Jeroen K. Vermunt Department of Methodology and Statistics, Tilburg University Jay Magidson Statistical Innovations Inc. Add to Reading List Source URL: statisticalinnovations.com Language: English - Date: 2006-09-05 12:15:22 Mathematical analysis Expectation–maximization algorithm Missing data Mixture model Latent class model Latent variable model Weight function Maximum likelihood Statistics Estimation theory Statistical models
140	J. R. Statist. Soc. B, Part 2, pp. 319–392 Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations Add to Reading List Source URL: www.math.ntnu.no Language: English - Date: 2011-12-21 15:18:36 Estimation theory Markov models Probability theory Variational Bayesian methods Mixture model Normal distribution Marginal likelihood Deviance information criterion Gaussian process Statistics Probability and statistics Bayesian statistics

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