Perfect samplers for mixtures of distributions
Casella, George; Mengersen, Kerrie; Robert, Christian P.; Titterington, Mike (2002), Perfect samplers for mixtures of distributions, Journal of the Royal Statistical Society. Series B, Statistical Methodology, 64, 4, p. 777-790. http://dx.doi.org/10.1111/1467-9868.00360
TypeArticle accepté pour publication ou publié
Journal nameJournal of the Royal Statistical Society. Series B, Statistical Methodology
MetadataShow full item record
Abstract (EN)We consider the construction of perfect samplers for posterior distributions associated with mixtures of exponential families and conjugate priors, starting with a perfect slice sampler in the spirit of Mira and co-workers. The methods rely on a marginalization akin to Rao–Blackwellization and illustrate the duality principle of Diebolt and Robert. A first approximation embeds the finite support distribution on the latent variables within a continuous support distribution that is easier to simulate by slice sampling, but we later demonstrate that the approximation can be very poor. We conclude by showing that an alternative perfect sampler based on a single backward chain can be constructed. This alternative can handle much larger sample sizes than the slice sampler first proposed.
Subjects / KeywordsCoupling; Gibbs sampling; Marginalization; Monotonicity; Slice sampling
Showing items related by title and author.
Alston, C.L.; Mengersen, Kerrie; Robert, Christian P.; Thompson, J.M.; Littlefield, P.J.; Perry, D.; Ball, A.J. (2007) Article accepté pour publication ou publié