Auxiliary Particle filtering within adaptive Metropolis-Hastings Sampling

TL;DR

This paper introduces an efficient adaptive particle filtering method integrated within Metropolis-Hastings sampling for Bayesian inference in state space models, improving computational efficiency and enabling unbiased likelihood estimation.

Contribution

It develops an adaptive auxiliary particle filter and an independent proposal MCMC method that together enhance efficiency and unbiasedness in Bayesian inference for state space models.

Findings

Adaptive particle filters outperform standard ones at high signal-to-noise ratios.

The proposed extit{aimh} sampler converges to the posterior distribution.

Marginal likelihood can be efficiently and unbiasedly estimated using the particle filter.

Abstract

Our article deals with Bayesian inference for a general state space model with the simulated likelihood computed by the particle filter. We show empirically that the partially or fully adapted particle filters can be much more efficient than the standard particle, especially when the signal to noise ratio is high. This is especially important because using the particle filter within MCMC sampling is O(T^2), where T is the sample size. We also show that an adaptive independent proposal for the unknown parameters based on a mixture of normals can be much more efficient than the usual optimal random walk methods because the simulated likelihood is not continuous in the parameters and the cost of constructing a good adaptive proposal is negligible compared to the cost of evaluating the simulated likelihood. Independent \MH proposals are also attractive because they are easy to run in parallel on multiple processors. The article also shows that the proposed \aimh sampler converges to the posterior distribution. We also show that the marginal likelihood of any state space model can be obtained in an efficient and unbiased manner by using the \pf making model comparison straightforward. Obtaining the marginal likelihood is often difficult using other methods. Finally, we prove that the simulated likelihood obtained by the auxiliary particle filter is unbiased. This result is fundamental to using the particle for MCMC sampling and is first obtained in a more abstract and difficult setting by Del Moral (2004). However, our proof is direct and will make the result accessible to readers.