Convergence Controls for MCMC Algorithms with Applications to Hidden Markov Chains

In complex models like hidden Markov chains, the convergence of the MCMC algorithms used to approximate the posterior distribution and the Bayes estimates of the parameters of interest must be controlled in a robust manner. We propose in this paper a series of on-line controls, which rely on classical non-parametric tests, to evaluate independence from the start-up distribution, stability of the Markov chain, and asymptotic normality.

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Year of publication:	1998
Authors:	Robert, C.P. ; Ryden, T. ; Titterington, D.M.
Subject:	TESTS \| ECONOMETRICS

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Type of publication:	Book / Working Paper
Notes:	22 pages
Classification:	C12 - Hypothesis Testing ; C14 - Semiparametric and Nonparametric Methods
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10005780807