DISTRIBUTED INFERENCE IN BAYESIAN NETWORKS

被引：7
作者：
DIEZ, FJ
MIRA, J
机构：
[1] Departamento de Informatica y Automatica LINED, UNED, Madrid
来源：
CYBERNETICS AND SYSTEMS | 1994年 / 25卷 / 01期
关键词：
Algorithms - Approximation theory - Artificial intelligence - Data acquisition - Logic gates - Mathematical models - Parallel processing systems;
D O I：
10.1080/01969729408902314
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
Bayesian networks originated as a framework for distributed reasoning. In singly connected networks, there exists an elegant inference algorithm that can be implemented in parallel having a processor for every node. It can be extended to take advantage of the OR-gate, a model of interaction among causes that simplifies knowledge acquisition and evidence propagation. We also discuss two exact and one approximate methods for dealing with general networks. It will be shown how all these algorithms admit distributed implementations.
引用
收藏
页码：39 / 61
页数：23
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