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Bayesian networks

ОК.21

Обов’язкова дисципліна для ОП

Second

2021/2022

1 Semester

4

Know and understand the basic algorithms of logical-probabilistic derivation in Bayesian networks of trust and in algebraic Bayesian networks. Be able to apply the apparatus of Bayesian networks, using algorithms for eliminating variables and symbolic calculations, clustering and algorithms for propagating messages between network nodes.

Full-time form

Know: basics of mathematical analysis, algebra, probability theory, mathematical statistics. Be able to: formalize tasks and compile algorithms for the implementation of tasks. Have basic skills: working with stochastic objects.

Acquisition by students of basic knowledge about methods of modeling of processes of arbitrary nature, which take place in conditions of uncertainty, with the help of Bayesian networks, solving problems of forecasting, medical and technical diagnostics, making managerial decisions, automatic control.

1. Feller W. Introduction to probability theory and its applications. - M .: Mir, 1984. 2. Popov Yu. D. Linear and nonlinear programming. - К .: УМК ВО, 1988. 3. Tulupyev AL, Nikolenko SI, Sirotkin AV Bayesian networks: logical-probabilistic approach. - СПб: Наука, 2006. - 608 с. 4. Lebedev EA, Sharapov MM Course of lectures on probability theory. - Kyiv: Norita-Plus, 2007. 5. Volkovich VL, Voloshin AF, Zaslavsky VA, Ushakov IA Models and methods for optimizing the reliability of complex systems / Edited by Mikhalevich VS - К .: Наукова думка, 1993. - 312 с. 6. Neapolitan R. E. Learning Bayesian Networks. Pearson Prentice Hall, 2004. 7. Nguen H. T., Walker E. W. A First Course in Fuzzy Logic. - N.Y., London, Washington: Chapman & Hall / CRC, 2000. - 373 p. 8. Ershov Yu.A., Palyutin EA Mathematical logic. - М .: Наука, 1977. - 336 с. 9. Tulupyev AL Algebraic Bayesian networks: theoretical foundations and consistency. - SPb .: SPIIRAN, 1995. - 76 p.

Lecture, test work, independent work.

Test work, exam.

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