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Importance Sampling with Particle Flows

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If you have a question about this talk, please contact Fredrik Lindsten.

The Bayesian approach to inference is based upon calculating posterior probability distributions. When these are not analytically tractable, we can instead estimate lots of useful properties by sampling. However, drawing samples from a posterior distribution is also a challenging task. This talk will focus on two methods for achieving this, importance sampling and particle flow sampling.

Importance sampling is a well-established, well-used, well-studied algorithm, in which samples are drawn from an importance distribution, and then weighted so as to represent the posterior. Particle flow sampling instead uses a differential equation to move samples through the state space to positions that represent the posterior.

We discuss the relative advantages and disadvantages of these two methods, and show how they may be usefully combined to achieve better inference with a class of nonlinear Gaussian models.

This talk is part of the Signal Processing and Communications Lab Seminars series.

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