Touloupou, Panayiota, Finkenstädt, Bärbel and Spencer, Simon E. F. (2019) Data for Scalable Bayesian inference for coupled hidden Markov and semi-Markov models. [Dataset]

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Abstract

Bayesian inference for coupled hidden Markov models frequently relies on data augmentation techniques for imputation of the hidden state processes. Considerable progress has been made on developing such techniques, mainly using Markov chain Monte Carlo (MCMC) methods. However, as the dimensionality and complexity of the hidden processes increase some of these methods become inefficient, either because they produce MCMC chains with high autocorrelation or because they become computationally intractable. Motivated by this fact we developed a novel MCMC algorithm, which is a modification of the forward filtering backward sampling algorithm, that achieves a good balance between computation and mixing properties, and thus can be used to analyse models with large numbers of hidden chains. Even though our approach is developed under the assumption of a Markovian model, we show how this assumption can be relaxed leading to minor modifications in the algorithm. Our approach is particularly well suited to epidemic models, where the hidden Markov chains represent the infection status of an individual through time. The performance of our method is assessed on simulated data on epidemic models for the spread of Escherichia coli O157:H7 in cattle.

Item Type:	Dataset
Subjects:	Q Science > QA Mathematics
Divisions:	Faculty of Science > Statistics
Type of Data:	Experimental data
Library of Congress Subject Headings (LCSH):	Hidden Markov models, Monte Carlo method, Markov processes, Bayesian statistical decision theory
Publisher:	Figshare
Official Date:	20 August 2019
Dates:	Date Event 20 August 2019 Published 18 September 2019 Updated
Status:	Not Peer Reviewed
Publication Status:	Published
Media of Output:	.r .so .cpp
Access rights to Published version:	Open Access
Copyright Holders:	University of Warwick
Description:	Data record consists of 9 data files, in .r .so and .cpp format, a supplementary PDF document and an accompanying readme file. Outline of all files for the epidemic example in Section 4.2 of the related paper: Simulated data set SimulatedData.r -- Section 4.2 for Markov epidemic model Subroutines: algorithms iFFBSalgorithm.r -- iFFBS algorithm MHiFFBSalgorithm.r -- MHiFFBS algorithm Subroutines Calling Cpp from R EcoliFFBScondmod.cpp -- Function for calculating the transition probabilities required by iFFBS method EcoliFFBScondmod.so -- Compiled file using the command: R CMD SHLIB EcoliFFBScondmod.cpp Implementation MCMC_using_iFFBS.r -- MCMC code to estimate parameters of Markov epidemic model using the proposed iFFBS algorithm for updating the hidden states MCMC_using_MHiFFBS.r -- MCMC code to estimate parameters of Markov epidemic model using the proposed MHiFFBS algorithm for updating the hidden states
RIOXX Funder/Project Grant:	Project/Grant ID RIOXX Funder Name Funder ID OPP1152057 Bill and Melinda Gates Foundation http://dx.doi.org/10.13039/100000865 OPP1053230 Bill and Melinda Gates Foundation http://dx.doi.org/10.13039/100000865 OPP1156227 Bill and Melinda Gates Foundation http://dx.doi.org/10.13039/100000865 MR/P026400/1 [MRC] Medical Research Council http://dx.doi.org/10.13039/501100000265 EP/R018561/1 [EPSRC] Engineering and Physical Sciences Research Council http://dx.doi.org/10.13039/501100000266 PhD scholarship University of Warwick http://dx.doi.org/10.13039/501100000741
Related URLs:	Related item in WRAP Other

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