DOI

Algebraic Bayesian networks belong to the class of machine-learning probabilistic graphical models. One of the main tasks during researching machine learning models is the optimization of their time of work. This paper presents approaches to parallelizing algorithms for maintaining local consistency in algebraic Bayesian networks as one of the ways to optimize their time of work. An experiment provided to compare the time of parallel and nonparallel realizations of algorithms for maintaining local consistency.

Original languageEnglish
Title of host publicationProceedings of the 4th International Scientific Conference on Intelligent Information Technologies for Industry, IITI 2019
EditorsSergey Kovalev, Andrey Sukhanov, Valery Tarassov, Vaclav Snasel
PublisherSpringer Nature
Pages214-222
Number of pages9
ISBN (Print)9783030500962
DOIs
StatePublished - 2020
Event4th International Scientific Conference on Intelligent Information Technologies for Industry, IITI 2019 - Ostrava-Prague, Czech Republic
Duration: 2 Dec 2019 → 7 Dec 2019

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1156 AISC
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

Conference4th International Scientific Conference on Intelligent Information Technologies for Industry, IITI 2019
Country/TerritoryCzech Republic
CityOstrava-Prague
Period2/12/19 → 7/12/19

    Research areas

  • Algebraic Bayesian networks, Bayesian networks, Consistency, Knowledge pattern, Machine learning, Parallel computing, Probabilistic graphic models, Probabilistic-logical inference

    Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)

ID: 88231115