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UID:20260913T220628EDT-8649crUxLA@132.216.98.100
DTSTAMP:20260914T020628Z
DESCRIPTION:Inferring the spatial dynamics of infectious diseases via Gauss
 ian process emulation.\n\nStatistical inference for spatial models of infe
 ctious disease spread is often very computationally expensive. Such models
  are generally fitted in a Bayesian Markov chain Monte Carlo (MCMC) framew
 ork\, which requires multiple calculation of what is often a computational
 ly cumbersome likelihood function. This problem is especially severe when 
 there are large numbers of latent variables to compute. Here\, we propose 
 a method of inference based on so-called emulation techniques. Once again\
 , the method is set in a Bayesian MCMC context\, but avoids calculation of
  the computationally expensive likelihood function by replacing it with a 
 Gaussian process approximation of the likelihood function built from simul
 ated data. We show that such a method can be used to infer the model param
 eters and underlying characteristics of spatial disease systems\, and that
  this can be done in much more computationally efficient manner than full 
 Bayesian MCMC allows.\n
DTSTART:20170131T203000Z
DTEND:20170131T213000Z
LOCATION:Purvis Hall\, CA\, QC\, Montreal\, H3A 1A2\, 1020 avenue des Pins 
 Ouest
SUMMARY:Robert Deardon\, PhD\, University of Calgary
URL:https://www.mcgill.ca/mathstat/channels/event/robert-deardon-phd-univer
 sity-calgary-265410
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