Particle Metropolis Hastings using Langevin Dynamics
2013 (Engelska)Ingår i: Proceedings of the 38th International Conference on Acoustics, Speech, and Signal Processing, IEEE conference proceedings, 2013, s. 6308-6312Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]
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.
Ort, förlag, år, upplaga, sidor
IEEE conference proceedings, 2013. s. 6308-6312
Nyckelord [en]
Bayesian inference, Sequential Monte Carlo, Particle Markov Chain Monte Carlo, Langevin Monte Carlo
Nationell ämneskategori
Reglerteknik Signalbehandling Sannolikhetsteori och statistik
Identifikatorer
URN: urn:nbn:se:liu:diva-93699DOI: 10.1109/ICASSP.2013.6638879ISI: 000329611506094OAI: oai:DiVA.org:liu-93699DiVA, id: diva2:626579
Konferens
38th International Conference on Acoustics, Speech, and Signal Processing, Vancouver, Canada, 28-31 May, 2013
Projekt
CADICS, CNDS
Forskningsfinansiär
Vetenskapsrådet2013-06-102013-06-102016-05-04Bibliografiskt granskad