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Convergence controls for MCMC algorithms, with applications to hidden markov chains
- Source :
- Scopus-Elsevier
- Publication Year :
- 1999
- Publisher :
- Informa UK Limited, 1999.
-
Abstract
- 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 online controls, which rely on classical non-parametric tests, to evaluate independence from the start-up distribution, stability of the Markov chain, and asymptotic normality. These tests lead to graphical control spreadsheets which arepresentedin the set-up of normalmixture hidden Markov chains to compare the full Gibbs sampler with an aggregated Gibbs sampler based on the forward – backward formulas.
- Subjects :
- Statistics and Probability
Markov chain mixing time
Markov chain
Applied Mathematics
Variable-order Markov model
Markov model
Statistics::Computation
ComputingMethodologies_PATTERNRECOGNITION
Modeling and Simulation
Calculus
Markov property
Examples of Markov chains
Forward algorithm
Hidden semi-Markov model
Statistics, Probability and Uncertainty
Algorithm
Mathematics
Subjects
Details
- ISSN :
- 15635163 and 00949655
- Volume :
- 64
- Database :
- OpenAIRE
- Journal :
- Journal of Statistical Computation and Simulation
- Accession number :
- edsair.doi.dedup.....32687654ab99bd97296298eb9729d162