Statistical Comparison of the Running Times of Global Posteriori Inference Algorithms in Algebraic Bayesian Networks. / Вяткин, Артём Андреевич; Тулупьев, Александр Львович.
Proceedings of 2023 XXVI International Conference on Soft Computing and Measurements (SCM). 2023. p. 24-28.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
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TY - GEN
T1 - Statistical Comparison of the Running Times of Global Posteriori Inference Algorithms in Algebraic Bayesian Networks
AU - Вяткин, Артём Андреевич
AU - Тулупьев, Александр Львович
PY - 2023/5/24
Y1 - 2023/5/24
N2 - 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.
AB - 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.
KW - knowledge pattern
KW - probabilistic-logic inference
KW - tertiary structure
KW - probabilistic graphical models
KW - machine learning
KW - statistical study of algorithm complexities
UR - https://www.mendeley.com/catalogue/0fa1f66b-d855-3d4f-b5e2-f83d4532feb2/
U2 - 10.1109/scm58628.2023.10159044
DO - 10.1109/scm58628.2023.10159044
M3 - Conference contribution
SN - 9798350322484
SP - 24
EP - 28
BT - Proceedings of 2023 XXVI International Conference on Soft Computing and Measurements (SCM)
T2 - 2023 XXVI International Conference on Soft Computing and Measurements (SCM)
Y2 - 24 May 2023 through 26 May 2023
ER -
ID: 108621828