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Bayesian selection probability estimation for probabilistic Boolean networks
Author(s) -
Toyoda Mitsuru
Publication year - 2019
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.2166
Subject(s) - markov chain , computer science , probabilistic logic , bayesian network , standard boolean model , bayesian probability , posterior probability , algorithm , boolean network , selection (genetic algorithm) , boolean function , mathematical optimization , mathematics , and inverter graph , artificial intelligence , machine learning , boolean circuit
A Bayesian approach to estimate selection probabilities of probabilistic Boolean networks is developed in this study. The concepts of inverse Boolean function and updatable set are introduced to specify states which can be used to update a Bayesian posterior distribution. The analysis on convergence of the posteriors is carried out by exploiting the combination of semi‐tensor product technique and state decomposition algorithm for Markov chain. Finally, some numerical examples demonstrate the proposed estimation algorithm.