Interval estimation of conditional probabilities in Bayesian Belief Network

An interval estimation of conditional probabilities in Bayesian belief network is considered, taking into account the uncertainty of the initial data. To account for errors in the values of functions and arguments, it is proposed to use confluent analysis methods. It is assumed that the conditional probability densities correspond to the normal distribution law. Formulas for interval estimation of conditional probability densities are given, taking into account the errors of their parameters. The results of mathematical modeling of obtaining point and interval estimates of conditional and a posteriori probabilities of the Bayesian belief network are shown.