Sampling and noise
Alexandre Chorin, UC Berkeley

Abstract: 
I will review some sampling algorithms with applications to Bayesian estimation and particle filters.Particle filters can collapse, and I will present some theoretical results on the circumstances under which they do. 
There is a widely shared belief that particle filters suffer from a ``curse of dimensionality" so that they must collapse if the number of variablesto be estimated is large enough; I will show that this is not so.

Particle filters, and data assimilation in general, usually require a priori knowledge of the statistics of the noise. I will show that they can
also be used to determine these statistics.

(joint work with Ole Hald, Matthias Morzfeld, and Xuemin Tu).