POSTPONED: High-performance importance sampling schemes for Bayesian inference
Victor Elvira (University of Edinburgh)
Monday 30th March, 2020 14:00-15:00 Maths 311B
Importance sampling (IS) is an elegant, theoretically sound, flexible, and simple-to-understand methodology for approximation of moments of distributions in Bayesian inference (and beyond). The only requirement is the point-wise evaluation of the targeted distribution. The basic mechanism of IS consists of (a) drawing samples from simple proposal densities, (b) weighting the samples by accounting for the mismatch between the targeted and the proposal densities, and (c) approximating the moments of interest with the weighted samples. The performance of IS methods directly depends on the choice of the proposal functions. For that reason, the proposals have to be updated and improved with iterations so that samples are generated in regions of interest. In this talk, we will first introduce the basics of IS and multiple IS (MIS), motivating the need of using several proposal densities. Then, the focus will be on motivating the use of adaptive IS (AIS) algorithms, describing an encompassing framework of recent methods in the current literature. Finally, we will briefly present some numerical examples where we will study the performance of various IS-based algorithms.