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SUMMARY:Exact simulation-based Bayesian inference for epidemic models - TJ
  McKinley\, Department of Veterinary Medicine
DTSTART:20111107T120000Z
DTEND:20111107T130000Z
UID:TALK33679@talks.cam.ac.uk
CONTACT:Prof. Julia Gog
DESCRIPTION:Inference in epidemic models poses many challenges\, not least
  because of missing or unobserved data. A powerful method for tackling som
 e of these issues is to use a Bayesian framework and data-augmented Markov
  chain Monte Carlo fitting algorithms. However\, these techniques can beco
 me computationally intensive for large-scale systems. An alternative is to
  use pseudo-marginal algorithms (O'Neill et al.\, 2000\; Beaumont\, 2003\;
  Andrieu and Roberts\, 2009)\, which provide methods for estimating both e
 xact and approximate posterior distributions for the parameters-of-interes
 t based on importance sample estimates generated from model simulations. W
 hen the observation process is deterministic\, then this requires that the
  model simulations match the observed data exactly\, which can be problema
 tic in highly stochastic systems without the availability of large amounts
  of computing power. We present some methods for reducing stochasticity an
 d improving computational efficiency for simulations of epidemic models\, 
 by conditioning the simulations on the model and data. We illustrate these
  techniques on real data for a variety of model/data combinations.
LOCATION:DD47\, Cripps Court\, Queens' College
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