Particle-Iterated Smoothing

• Activity Number: 133
• Type: Contributed
• Date/Time: Monday, August 10, 2015 : 8:30 AM to 10:20 AM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Dao Nguyen*
• Keywords: iterated filtering ; PMCMC ; fixed lag smoothing ; second order ; hessian ; stochastic approximation

Abstract

Second-order particle Markov chain Monte Carlo is an attractive class of parameter inference for state-space models due to the fact that exploiting the score and the observed information matrix estimated from particle filter, it can improve the estimations in term of i) shortening the burn-in period, (ii) accellerating the mixing of the Markov chain at the stationary period, and (iii) simplifying tuning. Unfortunately, current approaches rely on the ability to sample from the derivative and Hessian of transition and observation densities, which are rather unrealistic. We, therefore, propose a simpler approach, namely particle iterated smoothing. Not only derive theoretical analysis of the asymptotic properties of our approach, we also show better emprical results compared to standard methods.