Subsampling Sequential Monte Carlo for Static Bayesian Models

Understand

We show how to speed up Sequential Monte Carlo (SMC) for Bayesian inference in large data problems by data subsampling.

• SMC sequentially updates a cloud of particles through a sequence of distributions, beginning with a distribution that is easy to sample from such as the prior and ending with the posterior distribution.
• Each update of the particle cloud consists of three steps: reweighting, resampling, and moving.
• In the move step, each particle is moved using a Markov kernel; this is typically the most computationally expensive part, particularly when the dataset is large.