Bayesian non-parametric inference for stochastic epidemic models using Gaussian Processes.
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ABSTRACT:
SUBMITTER: Xu X
PROVIDER: S-EPMC5031942 | biostudies-literature | 2016 Oct
REPOSITORIES: biostudies-literature
Publications
Bayesian non-parametric inference for stochastic epidemic models using Gaussian Processes.
Xu Xiaoguang X Kypraios Theodore T O'Neill Philip D PD
Biostatistics (Oxford, England) 20160318 4
This paper considers novel Bayesian non-parametric methods for stochastic epidemic models. Many standard modeling and data analysis methods use underlying assumptions (e.g. concerning the rate at which new cases of disease will occur) which are rarely challenged or tested in practice. To relax these assumptions, we develop a Bayesian non-parametric approach using Gaussian Processes, specifically to estimate the infection process. The methods are illustrated with both simulated and real data sets ...[more]
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