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

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.<br><br>Data record consists of 9 data files. 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