Bayesian analysis of the logit model and comparison of two Metropolis-Hastings strategies.

DOI10.1016/S0167-9473(01)00055-XzbMath1132.62311MaRDI QIDQ1605369

Anas Altaleb, Didier Chauveau

Publication date: 15 July 2002

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

zbMATH Keywords

Markov chain Monte CarloMetropolis-Hastings algorithmAdaptive algorithmStationarityConvergence assessmentBayesian statistic

Mathematics Subject Classification ID

Bayesian inference (62F15) Numerical analysis or methods applied to Markov chains (65C40)

Related Items (1)

SOLVING STOCHASTIC CHEMICAL KINETICS BY METROPOLIS-HASTINGS SAMPLING

Uses Software

• CODA
• Gibbsit

Cites Work

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• Geometric convergence of the Metropolis-Hastings simulation algorithm
• Rates of convergence of the Hastings and Metropolis algorithms
• Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• The Calculation of Posterior Distributions by Data Augmentation
• Un algorithme de Hastings-Metropolis avec apprentissage séquentiel
• Improving Convergence of the Hastings–Metropolis Algorithm with an Adaptive Proposal
• Monte Carlo sampling methods using Markov chains and their applications

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