Event Date:
Wednesday, November 25, 2020 - 3:30pm to 4:30pm
Event Location:
- Zoom Meeting
Title: Quasi-stationary Monte Carlo methods via stochastic approximation
Abstract:
A new class of Monte Carlo algorithms, quasi-stationary Monte Carlo (QSMC), designed for exact Bayesian inference on large datasets, was recently presented in [Pollock et al (2020), JRSSB]. In my talk I will describe this class of methods, and the associated probabilistic concept of quasi-stationarity, which concerns the distribution of an extant stochastic population. I will present an alternative approach to QSMC based on stochastic approximation, rather than interacting particle systems, which is conceptually more straightforward and can be significantly more straightforward to implement, while retaining the desirable properties of exactness and scalability.
Joint work with Murray Pollock, Gareth Roberts, David Steinsaltz.
Bio:
Andi Wang is is a senior research associate (post doc) at the School of Mathematic, University of Bristol, UK. He completed his DPhil at the University of Oxford in 2019, under the joint supervision of Gareth Roberts (Warwick) and David Steinsaltz (Oxford). His current research is on the methodological and theoretical properties of Monte Carlo methods, working with Christophe Andrieu, Anthony Lee and Sam Power at Bristol.
November 24, 2020 - 8:52am