The PC-Algorithm of the Algebraic Bayesian Network Secondary Structure Training

被引：1
|
作者：
Kharitonov, Nikita [1 ]
Abramov, Maxim [2 ]
Tulupyev, Alexander [1 ,2 ]
机构：
[1] St Petersburg State Univ, St Petersburg, Russia
[2] Russian Acad Sci, St Petersburg Fed Res Ctr, St Petersburg, Russia
来源：
ARTIFICIAL INTELLIGENCE, RCAI 2021 | 2021年 / 12948卷
关键词：
Algebraic bayesian networks; Bayesian belief networks; Probabilistic graphical models; Structure training; PC-algorithm; LOGIC;
D O I：
10.1007/978-3-030-86855-0_18
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Algebraic Bayesian networks and Bayesian belief networks are one of the probabilistic graphical models. One of the main tasks which need to be solved during the networks' handling is the model structure training. This paper is dedicated to the automation of this process for algebraic Bayesian networks. This work relates to the PC-algorithm for algebraic Bayesian network secondary structure training. The algorithm is based on the PC-algorithm for Belief Bayesian networks training. The algorithm pseudocode and usage example are described. The provided algorithm helps investigate the full-automated machine learning of algebraic Bayesian networks. Earlier, the structure was provided by experts.
引用
收藏
页码：267 / 273
页数：7
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