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Computational Gains Via a Discretization of the Parameter Space in Individual Level Models of Infectious Disease

The Bayesian Markov Chain Monte Carlo(MCMC) approach to inference is commonly used to estimate the parameters in spatial infectious disease models. However, such MCMC analyses can pose a hefty computational burden. Here we present new method to reduce the computing time cost in such MCMC analyses and study its usefulness. This method is based a round the discretization of the spatial parameters in the infectious disease model. A normal approximation of the posterior density of the output from the original model will be compared to that of the modified model, using the Kullback-Leibler(KL) divergence measure.

Identiferoai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OGU.10214/3276
Date13 January 2012
CreatorsFANG, XUAN
ContributorsDeardon, Rob
Source SetsLibrary and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada
LanguageEnglish
Detected LanguageEnglish
TypeThesis

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