Convergence controls for MCMC algorithms, with applications to hidden markov chains

DOI10.1080/00949659908811984zbMath0968.62049OpenAlexW2082285675MaRDI QIDQ4949763

Tobias Rydén, Christian P. Robert Robert, Michael D. Titterington

Publication date: 17 September 2001

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: http://crest.science/RePEc/wpstorage/1998-05.pdf

zbMATH Keywords

stationarymixture modelallocationsubsamplingnormality testKolmogorov-Smirnov testsbackward formulaSpearman independence test

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Numerical analysis or methods applied to Markov chains (65C40)

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Cites Work

• Markov chains and stochastic stability
• Shift-coupling
• Bayesian estimation of hidden Markov chains: A stochastic implementation
• Reparameterisation issues in mixture modelling and their bearing on MCMC algorithms.
• Convergence control methods for Markov chain Monte Carlo algorithms
• Inference from iterative simulation using multiple sequences
• A maximal inequality and dependent Marcinkiewicz-Zygmund strong laws
• Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review
• Practical Markov Chain Monte Carlo
• Sampling-Based Approaches to Calculating Marginal Densities
• Rao-Blackwellisation of sampling schemes
• Gibbs Sampling for Bayesian Non-Conjugate and Hierarchical Models by Using Auxiliary Variables
• A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains
• The law of the iterated logarithm for stationary processes satisfying mixing conditions
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