Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo and exact approximation
Duration: 36 mins 36 secs
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About this item
| Description: |
Vihola, M
Thursday 6th July 2017 - 13:30 to 14:15 |
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| Created: | 2017-07-24 11:34 |
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| Collection: | Scalable inference; statistical, algorithmic, computational aspects |
| Publisher: | Isaac Newton Institute |
| Copyright: | Vihola, M |
| Language: | eng (English) |
| Abstract: | We consider an importance sampling (IS) type estimator based on Markov chain Monte Carlo (MCMC) which targets an approximate marginal distribution. The IS approach, based on unbiased estimators, is consistent, and provides a natural alternative to delayed acceptance (DA) pseudo-marginal MCMC. The IS approach enjoys many benefits against DA, including a straightforward parallelisation. We focus on a Bayesian latent variable model setting, where the MCMC operates on the hyperparameters, and the latent variable distributions are approximated. |
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