Parameter learning of stochastic Boolean networks

Abstract

AbstractIn this article, the parameter learning problem is studied for stochastic Boolean networks (SBNs). Both the measure noise and the system noise are assumed to be white and modeled by sequences of Bernoulli distributed stochastic variables which are mutually independent. An algebraic representation of the SBNs is obtained by taking advantage of vector expression of logic variable and applying the semi‐tensor product technique. Consequently, the parameter learning problem is reformulated as an optimization problem that makes it possible to identify the system matrices of SBNs in an efficient computation way. Subsequently, properties of forward and backward probabilities are investigated, and the EM algorithm is utilized to learn the model parameters from time series data. Finally, a numerical experiment is presented to show the usefulness of the designed parameter learning algorithm.