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dc.contributor.author Niemi, J
dc.contributor.author West, M
dc.coverage.spatial United States
dc.date.accessioned 2011-06-21T17:30:31Z
dc.date.issued 2010-06-01
dc.identifier http://www.ncbi.nlm.nih.gov/pubmed/20563281
dc.identifier.citation J Comput Graph Stat, 2010, 19 (2), pp. 260 - 280
dc.identifier.issn 1061-8600
dc.identifier.uri http://hdl.handle.net/10161/4403
dc.description.abstract We describe a strategy for Markov chain Monte Carlo analysis of non-linear, non-Gaussian state-space models involving batch analysis for inference on dynamic, latent state variables and fixed model parameters. The key innovation is a Metropolis-Hastings method for the time series of state variables based on sequential approximation of filtering and smoothing densities using normal mixtures. These mixtures are propagated through the non-linearities using an accurate, local mixture approximation method, and we use a regenerating procedure to deal with potential degeneracy of mixture components. This provides accurate, direct approximations to sequential filtering and retrospective smoothing distributions, and hence a useful construction of global Metropolis proposal distributions for simulation of posteriors for the set of states. This analysis is embedded within a Gibbs sampler to include uncertain fixed parameters. We give an example motivated by an application in systems biology. Supplemental materials provide an example based on a stochastic volatility model as well as MATLAB code.
dc.format.extent 260 - 280
dc.language eng
dc.language.iso en_US en_US
dc.relation.ispartof J Comput Graph Stat
dc.title Adaptive Mixture Modelling Metropolis Methods for Bayesian Analysis of Non-linear State-Space Models.
dc.title.alternative en_US
dc.type Journal Article
dc.description.version Version of Record en_US
duke.date.pubdate 2010-6-0 en_US
duke.description.endpage 280 en_US
duke.description.issue 2 en_US
duke.description.startpage 260 en_US
duke.description.volume 19 en_US
dc.relation.journal Journal of Computational and Graphical Statistics en_US
pubs.author-url http://www.ncbi.nlm.nih.gov/pubmed/20563281
pubs.issue 2
pubs.organisational-group /Duke
pubs.organisational-group /Duke/School of Medicine
pubs.organisational-group /Duke/School of Medicine/Institutes and Centers
pubs.organisational-group /Duke/School of Medicine/Institutes and Centers/Duke Cancer Institute
pubs.organisational-group /Duke/Trinity College of Arts & Sciences
pubs.organisational-group /Duke/Trinity College of Arts & Sciences/Statistical Science
pubs.publication-status Published
pubs.volume 19

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