A non-homogeneous dynamic Bayesian network with a hidden Markov model dependency structure among the temporal data points

DOI10.1007/s10994-015-5503-2zbMath1360.92046OpenAlexW1760772398WikidataQ59480189 ScholiaQ59480189MaRDI QIDQ298349

Marco Grzegorczyk

Publication date: 20 June 2016

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-015-5503-2

zbMATH Keywords

hidden Markov modelMarkov chain Monte Carlomixture modelallocation samplermultiple changepoint processnon-homogeneous dynamic Bayesian network

Mathematics Subject Classification ID

Bayesian inference (62F15) Bayesian problems; characterization of Bayes procedures (62C10) Markov processes: estimation; hidden Markov models (62M05) Monte Carlo methods (65C05) Systems biology, networks (92C42)

Related Items (2)

Comparative evaluation of various frequentist and Bayesian non-homogeneous Poisson counting models ⋮ Reproducing kernels and choices of associated feature spaces, in the form of \(L^2\)-spaces

Uses Software

• BioBayes
• Diurnal

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