Inference for discretely observed stochastic kinetic networks with applications to epidemic modeling.
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ABSTRACT:
SUBMITTER: Choi B
PROVIDER: S-EPMC3276272 | biostudies-literature | 2012 Jan
REPOSITORIES: biostudies-literature
Publications
Inference for discretely observed stochastic kinetic networks with applications to epidemic modeling.
Choi Boseung B Rempala Grzegorz A GA
Biostatistics (Oxford, England) 20110810 1
We present a new method for Bayesian Markov Chain Monte Carlo-based inference in certain types of stochastic models, suitable for modeling noisy epidemic data. We apply the so-called uniformization representation of a Markov process, in order to efficiently generate appropriate conditional distributions in the Gibbs sampler algorithm. The approach is shown to work well in various data-poor settings, that is, when only partial information about the epidemic process is available, as illustrated on ...[more]
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