Bayesian Process Networks An Approach to Systemic Process Risk Analysis by Mapping Process Models onto Bayesian Networks

This paper presents an approach to mapping a process model onto a Bayesian network resulting in a Bayesian Process Network which will be applied to process risk analysis Exemplified by the model of Event driven Process Chains it is demonstrated how a process model can be mapped onto an isomorphic Bayesian network thus creating a Bayesian Process Network Process events functions objects and operators are mapped onto random variables and the causal mechanisms between these are represented by appropriate conditional probabilities Since process risks can be regarded as deviations of the process from its reference state all process risks can be mapped onto risk states of the random variables By example we show how process risks can be specified evaluated and analyzed by means of a Bayesian Process Network The results reveal that the approach presented herein is a simple technique for enabling systemic process risk analysis because the Bayesian Process Network can be designed solely based on an existing process model