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Bayesian inference as iterated random functions with applications to sequential inference in graphical models
Arash Amini · XuanLong Nguyen

Fri Dec 06 05:56 PM -- 06:00 PM (PST) @ Harvey's Convention Center Floor, CC

We propose a general formalism of iterated random functions with semigroup property, under which exact and approximate Bayesian posterior updates can be viewed as specific instances. A convergence theory for iterated random functions is presented. As an application of the general theory we analyze convergence behaviors of exact and approximate message-passing algorithms that arise in a sequential change point detection problem formulated via a latent variable directed graphical model. The sequential inference algorithm and its supporting theory are illustrated by simulated examples.

Author Information

Arash Amini (UCLA)
Long Nguyen (University of Michigan)

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