State Distribution of Markovian Jump Boolean Networks and Its Applications
Min Meng, Gaoxi Xiao
Abstract
This article investigates the state distribution of Markovian jump Boolean networks subject to stochastic disturbances based on the measured outputs. The considered disturbances are modeled as independent and identically distributed processes with known probability distributions. An iterative algorithm is proposed to compute conditional probability distributions of the current state and one-step predicted state based on the knowledge of the output measurements. The obtained conditional probability distributions can be applied to study the optimal state estimation, reconstructibility, and fault detection of Markovian jump Boolean networks.
Topics & Concepts
Markov processIndependent and identically distributed random variablesConditional probabilityMathematicsState (computer science)Probability distributionConditional probability distributionJumpJoint probability distributionJump processBoolean networkStochastic processComputer scienceApplied mathematicsBoolean functionAlgorithmRandom variableStatisticsQuantum mechanicsPhysicsGene Regulatory Network AnalysisAdvanced Control Systems OptimizationFormal Methods in Verification