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首页> 外文会议>PRICAI'98 : Topics in artificial intelligence >Context-Specific Independence, Decomposition of Conditional Probabilities, and Inference in Bayesian Networks
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Context-Specific Independence, Decomposition of Conditional Probabilities, and Inference in Bayesian Networks

机译：贝叶斯网络中特定于上下文的独立性，条件概率的分解和推断

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摘要

Three kinds of independence axe of interest in the context of Bayesian networks, namely conditional independence, independence of causal influence, and context-specific independence. It is well-known that conditional independence enables one to factorize a joint probability into a list of conditional probabilities and thereby renders inference feasible. It has recently been shown that independence of causal influence leads to further factorizations of some of the conditional probabilities and consequently makes inference faster. This paper studies context-specific independence. We show that context-specific independence can be used to further decompose some of the conditional probabilities. We present an inference algorithm that takes advantage of the decompositions and provide, for the first time, empirical evidence that demonstrates the computational benefits of exploiting context-specific independence.
机译：在贝叶斯网络环境中，三种感兴趣的独立性轴是条件独立性，因果影响的独立性和特定于上下文的独立性。众所周知，条件独立性使人们能够将联合概率分解为条件概率列表，从而使推断可行。最近显示，因果影响的独立性导致某些条件概率的进一步因式分解，因此使推理更快。本文研究了特定于上下文的独立性。我们证明了特定于上下文的独立性可以用来进一步分解某些条件概率。我们提出了一种利用分解优势的推理算法，并首次提供了经验证据，证明了利用上下文特定的独立性的计算优势。

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