### Backward SDE method for nonlinear filtering problems

- Series
- Applied and Computational Mathematics Seminar
- Time
- Monday, October 17, 2016 - 14:05 for 1 hour (actually 50 minutes)
- Location
- Skiles 005
- Speaker
- Prof. Yanzhao Cao – Auburn University Mathematics

A nonlinear filtering problem can be classified as a stochastic Bayesian
optimization problem of identifying the state of a stochastic dynamical
system based on noisy observations of the system. Well known numerical
simulation methods include unscented Kalman filters and particle
filters. In this talk, we consider a class of efficient numerical
methods based on forward backward stochastic differential equations.
The backward SDEs for nonlinear filtering problems are similar to the
Fokker-Planck equations for SDEs. We will describe the process of
deriving such backward SDEs as well as high order numerical algorithms
to solve them, which in turn solve nonlinear filtering problems.