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Discretization and MCMC Convergence Assessment (Paperback, Softcover reprint of the original 1st ed. 1998)
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Discretization and MCMC Convergence Assessment (Paperback, Softcover reprint of the original 1st ed. 1998)
Series: Lecture Notes in Statistics, 135
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This monograph proposes several approaches to convergence
monitoring for MCMC algorithms which are centered on the theme of
discrete Markov chains. After a short introduction to MCMC methods,
including recent developments like perfect simulation and Langevin
Metropolis-Hastings algorithms, and to the current convergence
diagnostics, the contributors present the theoretical basis for a
study of MCMC convergence using discrete Markov chains and their
specificities. The contributors stress in particular that this
study applies in a wide generality, starting with latent variable
models like mixtures, then extending the scope to chains with
renewal properties, and concluding with a general Markov chain.
They then relate the different connections with discrete or finite
Markov chains with practical convergence diagnostics which are
either graphical plots (allocation map, divergence graph, variance
stabilizing, normality plot), stopping rules (normality,
stationarity, stability tests), or confidence bounds (divergence,
asymptotic variance, normality). Most of the quantitative tools
take advantage of manageable versions of the CLT. The different
methods proposed here are first evaluated on a set of benchmark
examples and then studied on three full scale realistic
applications, along with the standard convergence diagnostics: A
hidden Markov modelling of DNA sequences, including a perfect
simulation implementation, a latent stage modelling of the dynamics
of HIV infection, and a modelling of hospitalization duration by
exponential mixtures. The monograph is the outcome of a monthly
research seminar held at CREST, Paris, since 1995. The seminar
involved the contributors to this monograph and wasled by Christian
P. Robert, Head of the Satistics Laboratory at CREST and Professor
of Statistics at the University of Rouen since 1992.
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