Learning the structure of dynamic Bayesian networks from time series and steady state measurements

DOI10.1007/s10994-008-5053-yzbMath1470.68127OpenAlexW2027334863MaRDI QIDQ1009260

Ilya Shmulevich, Harri Lähdesmäki

Publication date: 31 March 2009

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

Full work available at URL: https://doi.org/10.1007/s10994-008-5053-y

zbMATH Keywords

Markov chain Monte CarloBayesian inferencedynamic Bayesian networkssteady-state analysistrans-dimensional Markov chain Monte Carlo

Mathematics Subject Classification ID

Bayesian inference (62F15) Monte Carlo methods (65C05) Learning and adaptive systems in artificial intelligence (68T05) Probabilistic graphical models (62H22)

Related Items (2)

Inferring dynamic gene regulatory networks with low-order conditional independencies -- an evaluation of the method ⋮ Bayesian network models for incomplete and dynamic data

Uses Software

• ARPACK
• BNT
• HdBCS

Cites Work

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