Focus on particle methods

old_uid9178
titleFocus on particle methods
start_date2010/10/21
schedule15h-17h
onlineno
summaryAn introduction to particle methods for filtering and smoothing Sylvain Le Corff (Telecom ParisTech): Error bounds in forward filtering backward smoothing (joint work with Cyrille Dubarry) [Abstract : Approximating joint smoothing distributions using particle-based methods is a well-known issue in statistical inference when operating on general state space hidden Markov models (HMM). In this paper, we focus on non-asymptotic bounds for the error generated by the computation of smoothed additive functionals. More precisely, this contribution provides new results on the forward filtering backward smoothing (FFBS) error?s Lq-norms under appropriate mixing conditions on the Markov kernel?s probability density function. The algorithm used has a computational complexity depending linearly on TN where T is the number of observations and N the number of particles. The main improvement ...
responsiblesBiau, Stoltz, Massart