### Implicit sampling in the small-noise limit

- Series
- Applied and Computational Mathematics Seminar
- Time
- Monday, November 6, 2017 - 13:55 for 1 hour (actually 50 minutes)
- Location
- Skiles 005
- Speaker
- Prof. Kevin Lin – University of Arizona – klin@math.arizona.edu

Weighted direct samplers, sometimes also called importance
samplers, are Monte Carlo algorithms for generating
independent, weighted samples from a given target
probability distribution. They are used in, e.g., data
assimilation, state estimation for dynamical systems, and
computational statistical mechanics. One challenge in
designing weighted samplers is to ensure the variance of the
weights, and that of the resulting estimator, are
well-behaved. Recently, Chorin, Tu, Morzfeld, and coworkers
have introduced a class of novel weighted samplers called
implicit samplers, which possess a number of nice empirical
properties. In this talk, I will summarize an asymptotic
analysis of implicit samplers in the small-noise limit and
describe a simple method to obtain a higher-order accuracy.
I will also discuss extensions to stochastic differential
equatons. This is joint work with Jonathan Goodman, Andrew
Leach, and Matthias Morzfeld.