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Marginal likelihood
Results: 108

#	Item
41	Decomposing Spatiotemporal Brain Patterns into Topographic Latent Sources Samuel J. Gershman∗ Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA Add to Reading List Source URL: www.cs.columbia.edu Language: English - Date: 2015-03-12 00:16:21 Bayesian statistics Statistical theory Kullback–Leibler divergence Thermodynamics Identifiability Marginal likelihood Gamma distribution Functional magnetic resonance imaging Markov chain Statistics Logic Estimation theory
42	20 STATISTICAL LEARNING METHODS In which we view learning as a form of uncertain reasoning from observations. Add to Reading List Source URL: aima.cs.berkeley.edu Language: English - Date: 2002-11-14 11:01:47 Estimation theory Maximum likelihood Likelihood-ratio test Naive Bayes classifier Bayesian inference Conjugate prior Minimum description length Bayesian network Marginal likelihood Statistics Bayesian statistics Statistical theory
43	6.867 Machine learning, lecture 10 (Jaakkola) 1 Lecture topics: model selection criteria • Structural risk minimization, example derivation Add to Reading List Source URL: ocw.mit.edu Language: English - Date: 2015-03-15 16:36:56 Bayesian statistics Econometrics Bayesian inference Maximum likelihood Loss function Likelihood function Marginal likelihood Normal distribution Bayesian information criterion Statistics Estimation theory Statistical theory
44	Variational Bayesian Inference with Stochastic Search John Paisley1 David M. Blei3 Michael I. Jordan1,2 1 Add to Reading List Source URL: www.cs.columbia.edu Language: English - Date: 2015-03-12 00:16:22 Bayesian statistics Probability theory Science Variational Bayesian methods Variational message passing Marginal likelihood Dirichlet process Variational method Delta method Statistics Estimation theory Logic
45	Journal of Machine Learning Research[removed]1031 Submitted 9/12; Revised 1/13; Published 4/13 Variational Inference in Nonconjugate Models Chong Wang Add to Reading List Source URL: www.cs.columbia.edu Language: English - Date: 2015-03-12 00:16:23 Estimation theory Gamma distribution Conjugate prior Exponential family Marginal likelihood Delta method Sufficient statistic Kullback–Leibler divergence Expectation–maximization algorithm Statistics Statistical theory Bayesian statistics
46	Nonparametric Variational Inference Samuel J. Gershman [removed] Department of Psychology, Princeton University, Green Hall, Princeton, NJ[removed]USA Matthew D. Hoffman Add to Reading List Source URL: www.cs.columbia.edu Language: English - Date: 2015-03-12 00:16:21 Estimation theory Statistical models Statistical theory Variational Bayesian methods Probability theory Conjugate prior Mixture model Marginal likelihood Kullback–Leibler divergence Statistics Bayesian statistics Probability and statistics
47	egu_logo_without_circle_grey Add to Reading List Source URL: www.atmos-meas-tech.net Language: English - Date: 2014-12-03 12:07:22 Optics AERONET Bayesian inference Bidirectional reflectance distribution function Uncertainty quantification Marginal likelihood Bayes factor Statistics Mathematical sciences Bayesian statistics
48	Journal of Machine Learning Research[removed]3015 Submitted 8/10; Revised 9/10; Published[removed]Gaussian Processes for Machine Learning (GPML) Toolbox Carl Edward Rasmussen∗ Add to Reading List Source URL: www.jmlr.org Language: English - Date: 2010-11-19 19:10:52 Gaussian process Likelihood function Variational Bayesian methods Marginal likelihood Kriging Covariance Regression analysis Conjugate prior Maximum likelihood Statistics Bayesian statistics Estimation theory
49	Microsoft Word - Characterizing Species at Risk I - BBN modeling[removed]appendix.rtf Add to Reading List Source URL: www.plexusowls.com Language: English - Date: 2006-11-05 11:11:50 Bayesian statistics Conditional probability Conditionals Probability Maximum likelihood Marginal likelihood Expectation–maximization algorithm GEC Conditional probability distribution Statistics Probability theory Estimation theory
50	Learning Community-based Preferences via Dirichlet Process Mixtures of Gaussian Processes Ehsan Abbasnejad Scott Sanner ANU & NICTA NICTA & ANU Add to Reading List Source URL: users.cecs.anu.edu.au Language: English - Date: 2013-04-25 08:14:01 Markov chain Normal distribution Dirichlet process Bayesian inference Marginal likelihood Conjugate prior Statistics Bayesian statistics Mixture model

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