Particle Metropolis-Hastings using gradient and Hessian information
This work improves computational efficiency for researchers using PMH in statistical inference, though it is incremental as it builds on existing PMH methods.
The authors tackled the problem of inefficient parameter exploration in Particle Metropolis-Hastings (PMH) for Bayesian inference in nonlinear state space models by proposing versions that incorporate gradient and Hessian information into the proposal, resulting in decreased burn-in length, increased mixing, and scale-invariant proposals.
Particle Metropolis-Hastings (PMH) allows for Bayesian parameter inference in nonlinear state space models by combining Markov chain Monte Carlo (MCMC) and particle filtering. The latter is used to estimate the intractable likelihood. In its original formulation, PMH makes use of a marginal MCMC proposal for the parameters, typically a Gaussian random walk. However, this can lead to a poor exploration of the parameter space and an inefficient use of the generated particles. We propose a number of alternative versions of PMH that incorporate gradient and Hessian information about the posterior into the proposal. This information is more or less obtained as a byproduct of the likelihood estimation. Indeed, we show how to estimate the required information using a fixed-lag particle smoother, with a computational cost growing linearly in the number of particles. We conclude that the proposed methods can: (i) decrease the length of the burn-in phase, (ii) increase the mixing of the Markov chain at the stationary phase, and (iii) make the proposal distribution scale invariant which simplifies tuning.