Sequential Monte Carlo Methods - an introduction
Fanny Empacher (University of St Andrews, UK)
Sequential Monte Carlo (SMC) methods are a helpful tool for inference in State Space Models, in particular in cases where
standard methods such as MCMC or the Kalman filter fail. SMC methods can handle non-linear and non-Gaussian models, and can
be extended to suit many requirements. This talk and its continuation with Part II on February 26 will introduce
these methods, discuss some theoretical properties and give example applications. I will also discuss typical problems
and possible solutions, including some recent developments. Part I will focus on state inference methods which form
the basis for parameter inference methods that will be discussed in Part II.