Sumeetpal Singh (Cambridge)

Computing the filter derivative using Sequential Monte Carlo

Sequential Monte Carlo (SMC) methods are a widely used set of computational tools for inference in non-linear non-Gaussian state-space models. We propose a SMC algorithm to compute the derivative of the optimal filter in a Hidden Markov Model (HMM) and study its stability both theoretically and with numerical examples. Applications include calibrating the HMM from observed data in an online manner.

(Joint work with P. Del Moral and A. Doucet)