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Data for Scalable Bayesian inference for coupled hidden Markov and semi-Markov models

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

Research output not available from this repository, contact author.
Official URL: https://doi.org/10.6084/m9.figshare.9693164.v2

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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:
DateEvent
20 August 2019Published
18 September 2019Updated
Status: Not Peer Reviewed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 IDRIOXX Funder NameFunder ID
OPP1152057Bill and Melinda Gates Foundationhttp://dx.doi.org/10.13039/100000865
OPP1053230Bill and Melinda Gates Foundationhttp://dx.doi.org/10.13039/100000865
OPP1156227Bill and Melinda Gates Foundationhttp://dx.doi.org/10.13039/100000865
MR/P026400/1 [MRC] Medical Research Councilhttp://dx.doi.org/10.13039/501100000265
EP/R018561/1[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
PhD scholarshipUniversity of Warwickhttp://dx.doi.org/10.13039/501100000741
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