Particle Metropolis Hastings using Langevin Dynamics
2013 (English)In: Proceedings of the 38th International Conference on Acoustics, Speech, and Signal Processing, IEEE conference proceedings, 2013, 6308-6312 p.Conference paper (Refereed)
Particle Markov Chain Monte Carlo (PMCMC) samplers allow for routine inference of parameters and states in challenging nonlinear problems. A common choice for the parameter proposal is a simple random walk sampler, which can scale poorly with the number of parameters.
In this paper, we propose to use log-likelihood gradients, i.e. the score, in the construction of the proposal, akin to the Langevin Monte Carlo method, but adapted to the PMCMC framework. This can be thought of as a way to guide a random walk proposal by using drift terms that are proportional to the score function. The method is successfully applied to a stochastic volatility model and the drift term exhibits intuitive behaviour.
Place, publisher, year, edition, pages
IEEE conference proceedings, 2013. 6308-6312 p.
Bayesian inference, Sequential Monte Carlo, Particle Markov Chain Monte Carlo, Langevin Monte Carlo
Control Engineering Signal Processing Probability Theory and Statistics
IdentifiersURN: urn:nbn:se:liu:diva-93699DOI: 10.1109/ICASSP.2013.6638879ISI: 000329611506094OAI: oai:DiVA.org:liu-93699DiVA: diva2:626579
38th International Conference on Acoustics, Speech, and Signal Processing, Vancouver, Canada, 28-31 May, 2013
FunderSwedish Research Council