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Activity Number: 146 - Scaling up Bayesian Inference for Massive Data Sets
Type: Invited
Date/Time: Monday, July 29, 2019 : 10:30 AM to 12:20 PM
Sponsor: IMS
Abstract #300161 Presentation
Title: Continuous-Time Monte Carlo and Scalable Bayesian Inference
Author(s): Paul Fearnhead*
Companies: Lancaster University
Keywords: Markov chain Monte Carlo; piecewise deterministic Markov process; Zig-Zag Process; Bouncy Particle Sampler; Sub-sampling

Traditional Bayesian computational methods, such as MCMC, are based on discrete-time Markov chains. In recent years there has been interest in using continuous-time Markov processes within an MCMC-like procedure. This involves constructing and simulating a so-called piecewise deterministic Markov process that has the posterior distribution as its stationary distribution. Exact simulation is possible as these processes are finite-dimensional (to simulate their trajectories over any finite time interval requires simulating and storing only a finite-number of events). This talk will introduce these methods and their underlying theory and explain why they hold promise for scalable Bayesian inference: a piecewise deterministic Markov process with the correct stationary distribution can be simulated whilst only requiring access to a small sub-sample of data points at each iteration.

This is joint work with Joris Bierkens and Gareth Roberts, see and

Authors who are presenting talks have a * after their name.

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