An Introduction to Particle Filters

Particle filters are a class of estimation techniques that blend existing knowledge of the state (e.g., a model of the state that might not be perfect) and observations. These filters are built upon Bayes theorem, often simple to implement, and are successful in small enough sized systems. In this presentation, we will give a brief introduction to particle filters, touching on the advantages and disadvantages, and illustrate its implementation in a simple numerical experiment. 

Zoom link: https://www.math.arizona.edu/~klin/rtg-zoom