2026-10-14T09:27:37Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33802642 2026-09-15T15:23:35Z portal_1197 item_type_3 month_year_09_2026

Data for Scalable Bayesian inference for coupled hidden Markov and semi-Markov models Panayiota Touloupou (4745205) Bärbel Finkenstädt (224917) Simon E. F. Spencer (4526623) Hidden Markov models Monte Carlo method Markov processes Bayesian statistical decision theory Migrated from ePrints 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.<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 2019-08-20T00:00:00Z Dataset Dataset 10.82444/warw.33802642.v1 https://figshare.com/articles/dataset/Data_for_Scalable_Bayesian_inference_for_coupled_hidden_Markov_and_semi-Markov_models/33802642 CC BY 4.0