Darwinian Networks

被引：3

作者：

Butz, Cory J. ^{[1
]}

Oliveira, Jhonatan S. ^{[1
]}

dos Santos, Andre E. ^{[1
]}

机构：

[1] Univ Regina, Dept Comp Sci, Regina, SK S4S 0A2, Canada

来源：

ADVANCES IN ARTIFICIAL INTELLIGENCE (AI 2015) | 2015年 / 9091卷

关键词：

Bayesian networks; d-separation; Variable elimination; INFERENCE;

D O I：

10.1007/978-3-319-18356-5_2

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We suggest Darwinian networks (DNs) as a simplification of working with Bayesian networks (BNs). DNs adapt a handful of well-known concepts in biology into a single framework that is surprisingly simple, yet remarkably robust. With respect to modeling, on one hand, DNs not only represent BNs, but also faithfully represent the testing of independencies in a more straightforward fashion. On the other hand, with respect to two exact inference algorithms in BNs, DNs simplify each of them, while unifying both of them.

引用

页码：16 / 29

页数：14

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