Quasi-Newton particle Metropolis-Hastings
2015 (English)In: Proceedings of the 17th IFAC Symposium on System Identification., Elsevier, 2015, Vol. 48 Issue 28, 981-986 p.Conference paper (Refereed)
Particle Metropolis-Hastings enables Bayesian parameter inference in general nonlinear state space models (SSMs). However, in many implementations a random walk proposal is used and this can result in poor mixing if not tuned correctly using tedious pilot runs. Therefore, we consider a new proposal inspired by quasi-Newton algorithms that may achieve similar (or better) mixing with less tuning. An advantage compared to other Hessian based proposals, is that it only requires estimates of the gradient of the log-posterior. A possible application is parameter inference in the challenging class of SSMs with intractable likelihoods.We exemplify this application and the benefits of the new proposal by modelling log-returns offuture contracts on coffee by a stochastic volatility model with alpha-stable observations.
Place, publisher, year, edition, pages
Elsevier, 2015. Vol. 48 Issue 28, 981-986 p.
Bayesian parameter inference; state space models; approximate Bayesian computations; particle Markov chain Monte Carlo; α-stable distributions
Control Engineering Probability Theory and Statistics
IdentifiersURN: urn:nbn:se:liu:diva-123666DOI: 10.1016/j.ifacol.2015.12.258OAI: oai:DiVA.org:liu-123666DiVA: diva2:891385
Proceedings of the 17th IFAC Symposium on System Identification, Beijing, China, October 19-21, 2015.
FunderSwedish Research Council, 637-2014-466Swedish Research Council, 621-2013-5524