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

The Atrium, University of Guelph Institutional Repository

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

Show full item record

Title: Computational Gains Via a Discretization of the Parameter Space in Individual Level Models of Infectious Disease
Author: FANG, XUAN
Department: Department of Mathematics and Statistics
Program: Mathematics and Statistics
Advisor: Deardon, Rob
Abstract: 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.
URI: http://hdl.handle.net/10214/3276
Date: 2012-01-13


Files in this item

Files Size Format View
Computational G ... of Infectious Disease.pdf 1.152Mb PDF View/Open

This item appears in the following Collection(s)

Show full item record

Search the Atrium


Advanced Search

Browse

My Account