Exploiting causal independence in Bayesian network inference
Exploiting causal independence in Bayesian network inference
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DOI：
10.1613/jair.305
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发表时间：
1996-01-01
影响因子：
5
通讯作者：
Poole, D
中科院分区：
文献类型：
--
作者：
Zhang, NL;Poole, D
提出了一种利用因果独立性进行精确贝叶斯网络推理的新方法。贝叶斯网络可以被看作是将联合概率分解为一组条件概率的乘积。我们提出了一个因果独立的概念，使人们能够进一步因式分解的条件概率的组合，甚至更小的因素，从而获得更细粒度的联合概率因式分解。因果独立的新公式让我们指定一个变量的条件概率，给定它的父母在一个结合和交换算子，如“或”，“总和”或“最大”，对每个父母的贡献。我们，从一个简单的算法VE贝叶斯网络推理，给定的证据和查询变量，使用因式分解找到查询的后验分布。我们展示了如何将该算法扩展到利用因果独立性。基于CPCS网络的医学诊断的实证研究表明，该方法比以前的方法更有效，并且允许在比以前的算法更大的网络中进行推理。
A new method is proposed for exploiting causal independencies in exact Bayesian network inference. A Bayesian network can be viewed as representing a factorization of a joint probability into the multiplication of a set of conditional probabilities. We present a notion of causal independence that enables one to further factorize the conditional probabilities into a combination of even smaller factors and consequently obtain a finer-grain factorization of the joint probability. The new formulation of causal independence lets us specify the conditional probability of a variable given its parents in terms of an associative and commutative operator, such as ''or'', ''sum'' or ''max'', on the contribution of each parent. We, start with a simple algorithm VE for Bayesian network inference that, given evidence and a query variable, uses the factorization to find the posterior distribution of the query. We show how this algorithm can be extended to exploit causal independence. Empirical studies, based on the CPCS networks for medical diagnosis, show that this method is more efficient than previous methods and allows for inference in larger networks than previous algorithms.