Development of decision support programs based on Bayesian probabilistic models

The paper presents programs focused on using decision-making support tools and implementing original approaches to a rating estimation by an expert group and to fuzzy inference. The programs use the probabilistic models based on Bayes' formula previously proposed and pub-lished in the works of the authors. The models interpret the input estimated data as evidence in favor to one or another hypothesis from a set of possible ones determined by the model specifics: hypotheses on the object's place in the rating (in the group expert rating estimation model) and hypotheses on the possible value of the output linguistic variable (in the fuzzy inference model). The obtained evidence is transformed in a model-specific way into a set of Bayesian conditional probabilities computed under the assumption that the corresponding hypothesis is true, and then posterior probability distributions on the set of these hypotheses are calculated. These posterior distributions are used to obtain the final result: a rating of objects (in the rating estimation model), defuzzified value of linguistic variable (in the fuzzy inference model). The paper discusses the features of the software implementation of models on Java platform, notes the advantages of models confirmed or identified in the process of software implementation. The developed programs are registered in the Register of Computer Programs of the Russian Federal Service for Intellectual Property (Rospatent) and are used in the educational process at St. Petersburg State Transport University.