基于扩展Shafer-Shenoy和惰性传播的多段贝叶斯网络的推理
Inference in Multiply Sectioned Bayesian Networks with Extended Shafer-Shenoy and Lazy Propagation
摘要：
随着贝叶斯网络应用于更大和更复杂的问题领域，搜索灵活的建模和更有效的推理方法是一个持续的努力。多分段贝叶斯网络（MSBN）将贝叶斯网络的HUGIN推理扩展为一个灵活建模和分布式推理的框架，而惰性传播扩展了Shafer-Shenoy和HUGIN推理方法，降低了空间复杂度。我们应用Shafer-Shenoy和惰性传播的推理MSBN。MSBN框架和惰性传播的结合为非常大的领域中的建模和推理提供了更好的框架。它保留了MSBN的建模灵活性，并降低了运行时的空间复杂度，允许在给定相同的计算资源的更大的域的精确推理。
英文摘要：
As Bayesian networks are applied to larger and more complex problem domains, search for flexible modeling and more efficient inference methods is an ongoing effort. Multiply sectioned Bayesian networks (MSBNs) extend the HUGIN inference for Bayesian networks into a coherent framework for flexible modeling and distributed inference.Lazy propagation extends the Shafer-Shenoy and HUGIN inference methods with reduced space complexity. We apply the Shafer-Shenoy and lazy propagation to inference in MSBNs. The combination of the MSBN framework and lazy propagation provides a better framework for modeling and inference in very large domains. It retains the modeling flexibility of MSBNs and reduces the runtime space complexity, allowing exact inference in much larger domains given the same computational resources.
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