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Sequential Monte Carlo without likelihoods

delete2007-02-06
delete581
delete
OA
AI
S
Scott A. Sisson *
Y
Yanan Fan
M
Mark M. Tanaka
DOI：10.1073/pnas.0607208104delete
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Abstract

Abstract

En 中文
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.
Keywords:
approximate Bayesian computation
Bayesian inference
importance sampling
intractable likelihoods
tuberculosis
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Journal

P
Proceedings of the National Academy of Sciences of the United States of America
IF:
9.1
Papers:
10.8W
Citations:
73.5W

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