Sequential Monte Carlo Methods

Course Description: The objective is to provide a complete overview of the SMC field. We will cover the basics of Monte Carlo methods, importance sampling, sequential importance sampling, auxiliary methods and resampling techniques. We will also discuss the most recent adaptive methods. We will illustrate SMC methods on a variety of application areas including optimal estimation for non-linear non-Gaussian state-space models, sequential and batch Bayesian inference, computation of p-values, inference in contingency tables, rare event probabilities, optimization, counting the number of objects with a certain property for combinatorial structures, computation of eigenvalues and eigenmeasures of positive operators, PDE's admitting a Feynman-Kac representation and so on. We will also provide an introduction to the theory of SMC.

Prerequisite: A reasonable background in statistics and probability.
Instructors: Arnaud Doucet (University of British Columbia) and guest lecturers.