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Comparative study of noisy-max nodes and general nodes in Bayesian network models

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13440%2F20%3A43895560" target="_blank" >RIV/44555601:13440/20:43895560 - isvavai.cz</a>

  • Result on the web

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Comparative study of noisy-max nodes and general nodes in Bayesian network models

  • Original language description

    This article is devoted to the use of Bayesian networks for analyzing the growth of gross domestic product (GDP) of Ukraine and offers a comparative description of the use of various structural learning algorithms. A comparative study of the behavior of the Noisy-MAX nodes and the General nodes in the design of the Bayesian network was carried out. It has been shown that Noisy-max nodes in comparison with General nodes provide a relatively high initial accuracy. General nodes require retesting. However, Noisy-MAX nodes entail an increase in time and computational cost. (C) 2020 for this paper by its authors.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2020

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    CEUR Workshop Proceedings

  • ISBN

  • ISSN

    1613-0073

  • e-ISSN

  • Number of pages

    11

  • Pages from-to

    56-66

  • Publisher name

    CEUR-WS

  • Place of publication

    Německo

  • Event location

    Lviv

  • Event date

    Apr 23, 2020

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article