DOI

Global posteriori inference is an important problem in the theory of algebraic Bayesian networks, which are a subclass of probabilistic graphical models. So far, two algorithms have been presented that allow global posteriori inference. These algorithms use different global algebraic Bayesian network structures – secondary and tertiary. Thus, the task of comparing these algorithms is defined, and, in particular, it is necessary to perform a statistical analysis of their running times, which is the purpose of this paper. In addition, the stochastic generation algorithm of acyclic algebraic Bayesian networks was defined in the framework of the article, which allowed to generate data for the experiment. As a result, it was found that the algorithm using a tertiary structure works 1.15–2 times faster on algebraic Bayesian networks, where there are 5–150 knowledge patterns. At the same time, the difference in running time is noticeable the more atoms in the knowledge pattern models and their intersections.
Переведенное названиеСтатистическое сравнение времени работы алгоритмов глобального апостериорного вывода в алгебраических байесовских сетях
Язык оригиналаанглийский
Название основной публикацииProceedings of 2023 XXVI International Conference on Soft Computing and Measurements (SCM)
Страницы24-28
Число страниц5
ISBN (электронное издание)979-8-3503-2248-4
DOI
СостояниеОпубликовано - 24 мая 2023
Опубликовано для внешнего пользованияДа
Событие2023 XXVI International Conference on Soft Computing and Measurements (SCM) - Saint Petersburg, Russian Federation
Продолжительность: 24 мая 202326 мая 2023

конференция

конференция2023 XXVI International Conference on Soft Computing and Measurements (SCM)
Период24/05/2326/05/23

    Области исследований

  • knowledge pattern, probabilistic-logic inference, tertiary structure, probabilistic graphical models, machine learning, statistical study of algorithm complexities

ID: 108621828