A Bayesian autoregressive three-state hidden Markov model for identifying switching monotonic regimes in microarray time course data

DOI10.1515/1544-6115.1778zbMath1296.92029OpenAlexW2061672940WikidataQ34320768 ScholiaQ34320768MaRDI QIDQ461635

Serena Arima, Alessio Farcomeni

Publication date: 13 October 2014

Published in: Statistical Applications in Genetics and Molecular Biology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1515/1544-6115.1778

zbMATH Keywords

HMMBayesian statisticsMCMCmicroarray time course data

Mathematics Subject Classification ID

General biostatistics (92B15) Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) (60J20)

Related Items (3)

Bayesian analysis of latent Markov models with non-ignorable missing data ⋮ Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates ⋮ S-estimation of hidden Markov models

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