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Proc Natl Acad Sci U S A. 2007 Feb 6;104(6):1760-5. Epub 2007 Jan 30.

Sequential Monte Carlo without likelihoods.

Author information

1
School of Mathematics and Statistics, University of New South Wales, Sydney, NSW 2052, Australia. scott.sisson@unsw.edu.au

Erratum in

  • Proc Natl Acad Sci U S A. 2009 Sep 29;106(39):16889.

Abstract

Recent new methods in Bayesian simulation have provided ways of evaluating posterior distributions in the presence of analytically or computationally intractable likelihood functions. Despite representing a substantial methodological advance, existing methods based on rejection sampling or Markov chain Monte Carlo can be highly inefficient and accordingly require far more iterations than may be practical to implement. Here we propose a sequential Monte Carlo sampler that convincingly overcomes these inefficiencies. We demonstrate its implementation through an epidemiological study of the transmission rate of tuberculosis.

PMID:
17264216
PMCID:
PMC1794282
DOI:
10.1073/pnas.0607208104
[Indexed for MEDLINE]
Free PMC Article

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