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SUMMARY:Importance sampling type estimators based on approximate marginal 
 Markov chain Monte Carlo and exact approximation - Matti               Vih
 ola        (University of Jyväskylä )
DTSTART:20170706T123000Z
DTEND:20170706T131500Z
UID:TALK73176@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:We consider an importance sampling (IS) type estimator based o
 n Markov chain Monte Carlo (MCMC) which targets an approximate marginal di
 stribution. The IS approach\, based on unbiased estimators\, is consistent
 \, and provides a natural alternative to delayed acceptance (DA) pseudo-ma
 rginal MCMC. The IS approach enjoys many benefits against DA\, including a
  straightforward parallelisation. We focus on a Bayesian latent variable m
 odel setting\, where the MCMC operates on the hyperparameters\, and the la
 tent variable distributions are approximated.&nbsp\;
LOCATION:Seminar Room 1\, Newton Institute
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