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Marginal likelihood
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#	Item
71	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.princeton.edu Language: English - Date: 2014-05-19 05:49:39 Bayesian statistics Statistical theory Kullback–Leibler divergence Thermodynamics Identifiability Marginal likelihood Gamma distribution Functional magnetic resonance imaging Markov chain Statistics Logic Estimation theory
72	82 References Bock, R. D., & Aitkin, M[removed]Marginal maximum likelihood estimation of item parameters: An application of an EM algorithm. Psychometrika, 46, 443–459. Burket, G. R[removed]PARDUX [Computer program] Add to Reading List Source URL: marylandpublicschools.org Language: English - Date: 2013-08-22 16:22:42 Polytomous Rasch model Expectation–maximization algorithm Maximum likelihood Anton Formann Nambury S. Raju Statistics Psychometrics Estimation theory
73	Last Modified[removed]Basics of Probability and Statistics Key Concepts During a risk analysis, various numbers called probabilities are estimated and used to describe the likelihood of events and characterize the Add to Reading List Source URL: www.usbr.gov Language: English - Date: 2013-08-09 15:05:19 Probability Random variable Probability interpretations Probability distribution Marginal distribution Independence Conditional probability Cumulative distribution function Odds Probability theory Statistics Probability and statistics
74	A Composite Likelihood View for Multi-Label Classification Yi Zhang Jeff Schneider School of Computer Science, Carnegie Mellon University Add to Reading List Source URL: www.cs.cmu.edu Language: English - Date: 2013-02-04 21:37:18 Bayesian statistics Statistical theory Likelihood function Marginal likelihood Maximum likelihood Classifier chains Multi-label classification Kullback–Leibler divergence Statistical classification Statistics Estimation theory Machine learning
75	Proposal/Contract no.: [removed]Project start: September 1, 2006 Project end: August 31, 2009 INTAMAP Interoperability and Automated Mapping Add to Reading List Source URL: www.intamap.org Language: English - Date: 2009-11-09 05:05:54 Estimation theory Robust statistics Statistical theory Kriging Variogram Maximum likelihood Marginal likelihood Normal distribution Gaussian process Statistics Geostatistics Bayesian statistics
76	Package ‘diversitree’ July 2, 2014 Title diversitree: comparative phylogenetic analyses of diversification Version 0.9-7 Date 2014-02-20 Author Richard G. FitzJohn , with GeoSSE and Add to Reading List Source URL: cran.r-project.org Language: English - Date: 2014-07-02 11:39:25 Marginal likelihood PHY Ancestral reconstruction Bayesian statistics Estimation theory Likelihood function
77	Proposal/Contract no.: [removed]Project start: September 1, 2006 Project end: August 31, 2009 INTAMAP Interoperability and Automated Mapping Add to Reading List Source URL: www.intamap.org Language: English - Date: 2009-11-09 05:05:53 M-estimators Geostatistics Maximum likelihood Statistical theory Marginal likelihood Likelihood function Kriging Covariance function Multivariate normal distribution Statistics Estimation theory Bayesian statistics
78	J. R. Statist. Soc. B[removed], Part 2, pp. 235–251 Ascent-based Monte Carlo expectation– maximization Brian S. Caffo, Add to Reading List Source URL: www.math.mtu.edu Language: English - Date: 2005-09-05 18:13:36 Estimation theory Monte Carlo methods Statistical mechanics Markov chain Normal distribution Expectation–maximization algorithm Marginal likelihood Statistics Probability and statistics Markov models
79	Proposal/Contract no.: [removed]Project start: September 1, 2006 Project end: August 31, 2009 INTAMAP Interoperability and Automated Mapping Add to Reading List Source URL: www.intamap.org Language: English - Date: 2009-11-09 05:05:53 Bayesian statistics Robot control Computational statistics Variational Bayesian methods Particle filter Normal distribution Marginal likelihood Gaussian process Importance sampling Statistics Estimation theory Monte Carlo methods
80	Generalized Linear Mixed Models Introduction Generalized linear models (GLMs) represent a class of fixed effects regression models for several types of dependent variables (i.e., continuous, dichotomous, Add to Reading List Source URL: www.uic.edu Language: English - Date: 2007-05-22 10:53:02 Statistical models Categorical data Analysis of variance Generalized linear model Linear regression Logistic regression Marginal model Poisson regression Quasi-likelihood Statistics Regression analysis Econometrics

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