Second-Order Particle MCMC for Bayesian Parameter Inference
2014 (English)In: Proceedings of the 19th IFAC World Congress, 2014, 8656-8661 p.Conference paper (Refereed)
We propose an improved proposal distribution in the Particle Metropolis-Hastings (PMH) algorithm for Bayesian parameter inference in nonlinear state space models. This proposal incorporates second-order information about the parameter posterior distribution, which can be extracted from the particle filter already used within the PMH algorithm. The added information makes the proposal scale-invariant, simpler to tune and can possibly also shorten the burn-in phase. The proposed algorithm has a computational cost which is proportional to the number of particles, i.e. the same as the original marginal PMH algorithm. Finally, we provide two numerical examples that illustrates some of the possible benefits of adding the second-order information.
Place, publisher, year, edition, pages
2014. 8656-8661 p.
Particle filtering/Monte Carlo methods; Nonlinear system identification; Bayesian methods
Control Engineering Probability Theory and Statistics
IdentifiersURN: urn:nbn:se:liu:diva-113997DOI: 10.3182/20140824-6-ZA-1003.00277OAI: oai:DiVA.org:liu-113997DiVA: diva2:786291
19th IFAC World Congress, Cape Town, South Africa, August 24-29, 2014
FunderSwedish Research Council, 621-2013-5524