Journal
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
Volume 18, Issue 2, Pages 349-367Publisher
AMER STATISTICAL ASSOC
DOI: 10.1198/jcgs.2009.06134
Keywords
Adaption; Convergence; Hierarchical models; Markov chain Monte Carlo; Metropolis algorithm; Metropolis-within-Gibbs; Nonconjugate priors; Non-Markovian
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Funding
- NSERC of Canada
- Engineering and Physical Sciences Research Council [EP/D002060/1] Funding Source: researchfish
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We investigate the use of adaptive MCMC algorithms to automatically tune the Markov chain parameters during a run. Examples include the Adaptive Metropolis (AM) multivariate algorithm of Haario, Saksman, and Tamminen (2001), Metropolis-within-Gibbs algorithms for nonconjugate hierarchical models, regionally adjusted Metropolis algorithms, and logarithmic scalings. Computer simulations indicate that the algorithms perform very well compared to nonadaptive algorithms, even in high dimension.
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