Speeding-up Pseudo-marginal MCMC using a surrogate model

Add to your list(s) Download to your calendar using vCal

• Thiery, AH (National University of Singapore)
• Tuesday 22 April 2014, 11:40-12:15
• Seminar Room 1, Newton Institute.

If you have a question about this talk, please contact Mustapha Amrani.

Advanced Monte Carlo Methods for Complex Inference Problems

The pseudo-marginal MCMC algorithm is a powerful tool for exploring the posterior distribution when only unbiased stochastic estimates of the target density are available. In many situations, although there is no closed-form expression for this density, computationally cheap deterministic estimates are also available; one can then use a delayed-acceptance strategy for exploiting these cheap approximations.

As powerful as they are, the use of such algorithms are difficult in practice: it involves tuning the MCMC proposals and choosing the computational budget that one is willing to invest in the creation of the unbiased estimates while taking into account the quality of the cheap deterministic approximations. In this talk we discuss how high-dimensional asymptotic results can help in the tuning of these delayed-acceptance pseudo-marginal MCMC algorithms.

This is joint work with Chris Sherlock and Andrew Golightly.

This talk is part of the Isaac Newton Institute Seminar Series series.

This talk is included in these lists:

• All CMS events
• Featured lists
• INI info aggregator
• Isaac Newton Institute Seminar Series
• School of Physical Sciences
• Seminar Room 1, Newton Institute
• bld31

Note that ex-directory lists are not shown.