The Inferential Complexity of Bayesian and Credal Networks
The Inferential Complexity of Bayesian and Credal Networks
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贝叶斯和 Credal 网络的推理复杂性
DOI：
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发表时间：
2005
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通讯作者：
Fabio Gagliardi Cozman
中科院分区：
文献类型：
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作者：
Cassio Polpo de Campos;Fabio Gagliardi Cozman
本文提出了关于表示概率（贝叶斯网络）和表示区间和集值概率（凭证网络）的图论模型复杂性的新结果。我们定义了一类新的有界宽度网络，并为贝叶斯网络引入了一个新的决策问题——极大后验。我们提出了贝叶斯网络和可信网络之间的新联系，并提出了贝叶斯网络（最可能的解释与观测，最大后验）和可信网络（概率边界后验，最有可能的解释与没有观测，最大后验）的新结果。
This paper presents new results on the complexity of graph-theoretical models that represent probabilities (Bayesian networks) and that represent interval and set valued probabilities (credal networks). We define a new class of networks with bounded width, and introduce a new decision problem for Bayesian networks, the maximin a posteriori. We present new links between the Bayesian and credal networks, and present new results both for Bayesian networks (most probable explanation with observations, maximin a posteriori) and for credal networks (bounds on probabilities a posteriori, most probable explanation with and without observations, maximum a posteriori).