Abstract
In many problems in the area of artificial intelligence, it is necessary to deal with uncertainty. Using probabilistic models can also improve efficiency of standard AI-based techniques.
Commonly used methods for dealing with uncertainty include Bayesian models, which can be used to describe and work with probabilistic systems effectively. This article reviews several various models based on the Bayesian approach and typical algorithms used to work with them, along with some examples of their application.