DISTRIBUTED INFERENCE IN BAYESIAN NETWORKS
DISTRIBUTED INFERENCE IN BAYESIAN NETWORKS
复制标题
贝叶斯网络中的分布式推理
DOI：
10.1080/01969729408902314
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发表时间：
1994
期刊：
影响因子：
--
通讯作者：
J. Mira
中科院分区：
文献类型：
--
作者：
F. Díez;J. Mira
贝叶斯网络起源于分布式推理的框架。在单连接网络中，存在一种优雅的推理算法，该算法可以并行实现，每个节点都有一个处理器。它可以扩展到利用OR门，这是一种原因之间的交互模型，简化了知识获取和证据传播。我们还讨论了处理一般网络的两种精确方法和一种近似方法。它将展示所有这些算法如何允许分布式实现。
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.