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Data augmentation for models based on rejection sampling.

dc.contributor.author Dunson, David B
dc.contributor.author Lin, L
dc.contributor.author Rao, V
dc.coverage.spatial England
dc.date.accessioned 2017-10-01T21:17:23Z
dc.date.available 2017-10-01T21:17:23Z
dc.date.issued 2016-06
dc.identifier https://www.ncbi.nlm.nih.gov/pubmed/27279660
dc.identifier asw005
dc.identifier.issn 0006-3444
dc.identifier.uri http://hdl.handle.net/10161/15598
dc.description.abstract We present a data augmentation scheme to perform Markov chain Monte Carlo inference for models where data generation involves a rejection sampling algorithm. Our idea is a simple scheme to instantiate the rejected proposals preceding each data point. The resulting joint probability over observed and rejected variables can be much simpler than the marginal distribution over the observed variables, which often involves intractable integrals. We consider three problems: modelling flow-cytometry measurements subject to truncation; the Bayesian analysis of the matrix Langevin distribution on the Stiefel manifold; and Bayesian inference for a nonparametric Gaussian process density model. The latter two are instances of doubly-intractable Markov chain Monte Carlo problems, where evaluating the likelihood is intractable. Our experiments demonstrate superior performance over state-of-the-art sampling algorithms for such problems.
dc.language eng
dc.relation.ispartof Biometrika
dc.relation.isversionof 10.1093/biomet/asw005
dc.subject Bayesian inference
dc.subject Density estimation
dc.subject Gaussian process
dc.subject Intractable likelihood
dc.subject Markov chain Monte Carlo
dc.subject Matrix Langevin distribution
dc.subject Rejection sampling
dc.subject Truncation
dc.title Data augmentation for models based on rejection sampling.
dc.type Journal article
pubs.author-url https://www.ncbi.nlm.nih.gov/pubmed/27279660
pubs.begin-page 319
pubs.end-page 335
pubs.issue 2
pubs.organisational-group Duke
pubs.organisational-group Duke Institute for Brain Sciences
pubs.organisational-group Electrical and Computer Engineering
pubs.organisational-group Institutes and Provost's Academic Units
pubs.organisational-group Pratt School of Engineering
pubs.organisational-group Statistical Science
pubs.organisational-group Trinity College of Arts & Sciences
pubs.organisational-group University Institutes and Centers
pubs.publication-status Published
pubs.volume 103


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