使用链事件图的传播
Propagation using Chain Event Graphs
摘要：
链事件图（Chain Event Graph，CEG）是一种用于描述状态空间高度不对称且不具有自然乘积结构的问题的条件独立性的图模型。在这篇文章中，我们提出了一个概率传播算法，它使用的拓扑结构的CEG建立一个运输商CEG。有趣的是，运输商CEG是直接类似于三角贝叶斯网络（BN）在更传统的连接树传播算法与BN。传播方法使用的因子分解公式也类似于（但不同于）使用的集团和BN分离器上的潜力。看起来，当应用于存在显著不对称性的上下文时，该方法通常比BN算法更有效。
英文摘要：
A Chain Event Graph (CEG) is a graphial model which designed to embody conditional independencies in problems whose state spaces are highly asymmetric and do not admit a natural product structure. In this paer we present a probability propagation algorithm which uses the topology of the CEG to build a transporter CEG. Intriungly,the transporter CEG is directly analogous to the triangulated Bayesian Network (BN) in the more conventional junction tree propagation algorithms used with BNs. The propagation method uses factorization formulae also analogous to (but different from) the ones using potentials on cliques and separators of the BN. It appears that the methods will be typically more efficient than the BN algorithms when applied to contexts where there is significant asymmetry present.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
