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title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Development of a framework for the effective inference in bayesian networks
Creators: name: Ð Ð°Ð¿Ð¾Ð¿Ð¾ÑÑ, ÐÐµÐ¾Ð½Ð¸Ð´
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2020
Subjects: Ð±Ð°Ð¹ÐµÑÐ¾Ð²ÑÐºÐ¸Ðµ ÑÐµÑÐ¸
Ð²ÑÐ²Ð¾Ð´ Ð² Ð±Ð°Ð¹ÐµÑÐ¾Ð²ÑÐºÐ¸Ñ ÑÐµÂ­ÑÑÑ
ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° ÑÑÐµÐ¹Ð¼Ð²Ð¾ÑÐºÐ°
bayesian networks
inference in bayesian networks
framework development

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description: Bayesian networks are one of the types of probabilistic graph models widely used to encode uncertanty in expert systems. The given work reviews a problem of the efficient inference in bayesian networks and following set of goals was achieved during the course of the study: 1. Review of existing algorithms for inference in bayesian networks. 2. Review of existing software implementing this type of algorithms. 3. Development of a framework for inference in bayesian networks. 4. Comparative efficiency analysis of the algorithms that were implemented. At the phase of efficiency research of the implemented algorithms, using the models and test-cases that were available to the author, it was discovered that the implemented version of the juntion tree algorithm shows better results for the models with specific characteristics. As a result a perfomance increase was achieved for the module to which the framework was introduced.