Collapsing of Non-centred Parameterized MCMC Algorithms with Applications to Epidemic Models

Collapsing of Non-centred Parameterized MCMC Algorithms with Applications to Epidemic Models

0.00 Avg rating0 Votes
Article ID: iaor201781
Volume: 44
Issue: 1
Start Page Number: 81
End Page Number: 96
Publication Date: Mar 2017
Journal: Scandinavian Journal of Statistics
Authors: ,
Keywords: markov processes, statistics: distributions
Abstract:

Data augmentation is required for the implementation of many Markov chain Monte Carlo (MCMC) algorithms. The inclusion of augmented data can often lead to conditional distributions from well‐known probability distributions for some of the parameters in the model. In such cases, collapsing (integrating out parameters) has been shown to improve the performance of MCMC algorithms. We show how integrating out the infection rate parameter in epidemic models leads to efficient MCMC algorithms for two very different epidemic scenarios, final outcome data from a multitype SIR epidemic and longitudinal data from a spatial SI epidemic. The resulting MCMC algorithms give fresh insight into real‐life epidemic data sets.

Reviews

Required fields are marked *. Your email address will not be published.