摘要翻译：
近年来，混合了连续和离散变量的贝叶斯网络受到了越来越多的关注。提出了一种条件线性高斯BNs（CLG BNs）的精确信度更新体系结构。该体系结构是使用Lauritzen&Jensen[6]和Cowell[2]操作的惰性传播的扩展。通过将团势和分隔符势分解为一组因子，该体系结构利用了由图的结构和证据引起的独立性和无关性。通过实例说明了由此产生的好处。初步的经验性能评估结果表明，所提出的体系结构具有很大的潜力。
---
英文标题：
《Belief Update in CLG Bayesian Networks With Lazy Propagation》
---
作者：
Anders L. Madsen
---
最新提交年份：
2012
---
分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
--

---
英文摘要：
  In recent years Bayesian networks (BNs) with a mixture of continuous and discrete variables have received an increasing level of attention. We present an architecture for exact belief update in Conditional Linear Gaussian BNs (CLG BNs). The architecture is an extension of lazy propagation using operations of Lauritzen & Jensen [6] and Cowell [2]. By decomposing clique and separator potentials into sets of factors, the proposed architecture takes advantage of independence and irrelevance properties induced by the structure of the graph and the evidence. The resulting benefits are illustrated by examples. Results of a preliminary empirical performance evaluation indicate a significant potential of the proposed architecture.
---
PDF链接：
https://arxiv.org/pdf/1206.6854