On the influence of the proposal distributions on a reversible jump MCMC algorithm applied to the detection of multiple change-points

DOI10.1016/S0167-9473(02)00055-5zbMath0993.62027MaRDI QIDQ1614845

R. Smith

Publication date: 9 September 2002

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

zbMATH Keywords

Poisson processreversible jump Markov chain Monte Carlohierarchical Bayesian modelacceptance ratelevels of seismicityrandom proposal

Mathematics Subject Classification ID

Bayesian inference (62F15) Seismology (including tsunami modeling), earthquakes (86A15) Numerical analysis or methods applied to Markov chains (65C40)

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Uses Software

• CODA
• boa
• Gibbsit

Cites Work

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• Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
• Bayesian computation and stochastic systems. With comments and reply.
• Rates of convergence of the Hastings and Metropolis algorithms
• Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review
• Simulation Run Length Control in the Presence of an Initial Transient
• A Bayesian Analysis for Change Point Problems
• Adaptive Rejection Sampling for Gibbs Sampling
• Hierarchical Bayesian Analysis of Changepoint Problems
• Bayesian Retrospective Multiple-Changepoint Identification