Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
Algorithm of global posteriori inference in algebraic Bayesian networks is considered in the paper. The results obtained earlier for local a posteriori inference are briefly presented. Main steps of global propagation algorithm are described in details. A transition matrix from the vector of knowledge pattern elements to the virtual evidence, propagated to the next knowledge pattern, is proposed. The stated theorem describes the matrix-vector representation of the stochastic evidence propagation algorithm within a network with scalar estimates of the knowledge patterns elements probabilities of truth. The obtained results form the basis for development of the global posteriori inference machine matrix-vector representation in algebraic Bayesian networks and simplify its further software implementation.
Original language | English |
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Title of host publication | Proceedings of 2017 20th IEEE International Conference on Soft Computing and Measurements, SCM 2017 |
Editors | S. Shaposhnikov |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 22-24 |
Number of pages | 3 |
ISBN (Electronic) | 9781538618103 |
DOIs | |
State | Published - 6 Jul 2017 |
Event | 20th IEEE International Conference on Soft Computing and Measurements, SCM 2017 - St. Petersburg, Russian Federation Duration: 24 May 2017 → 26 May 2017 |
Conference | 20th IEEE International Conference on Soft Computing and Measurements, SCM 2017 |
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Country/Territory | Russian Federation |
City | St. Petersburg |
Period | 24/05/17 → 26/05/17 |
ID: 36985044