Bayesian selection probability estimation for probabilistic Boolean networks

Abstract

AbstractA 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.