Stochastic Particle Flow for Nonlinear High-Dimensional Filtering Problems

Understand

A series of novel filters for probabilistic inference that propose an alternative way of performing Bayesian updates, called particle flow filters, have been attracting recent interest.

• These filters provide approximate solutions to nonlinear filtering problems.
• They do so by defining a continuum of densities between the prior probability density and the posterior, i.e.
• the filtering density.