Estimation of a normal mixture model through Gibbs sampling and prior feedback

DOI10.1007/BF02562672zbMath0811.62037MaRDI QIDQ1345539

Caroline Soubiran, Christian P. Robert Robert

Publication date: 4 May 1995

Published in: Test (Search for Journal in Brave)

zbMATH Keywords

EM algorithmBayesian estimationmissing datamaximum likelihood estimatorsGibbs samplingmixture distributionconjugate priorsprior feedback

Mathematics Subject Classification ID

Point estimation (62F10) Bayesian inference (62F15) Statistical astronomy (85A35)

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Cites Work

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• Tools for statistical inference. Observed data and data augmentation methods
• Conjugate priors for exponential families
• Prior feedback: Bayesian tools for maximum likelihood estimation
• Practical Markov Chain Monte Carlo
• Sampling-Based Approaches to Calculating Marginal Densities
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• The Calculation of Posterior Distributions by Data Augmentation
• Statistical analysis of finite mixture distributions
• Estimation of parameters in hidden Markov models

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