Open AccessInterval estimation of conditional probabilities in Bayesian Belief Network
Author(s) -
Yu. E. Gagarin,
U. V. Nikitenko,
М. А. Степович
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1902/1/012106
Subject(s) - conditional probability , law of total probability , conditional probability distribution , chain rule (probability) , mathematics , interval (graph theory) , point estimation , interval estimation , bayesian network , regular conditional probability , credible interval , prior probability , statistics , bayesian probability , posterior probability , confidence interval , combinatorics
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.