HUGS：在连接树中结合精确推理和Gibbs抽样
HUGS: Combining Exact Inference and Gibbs Sampling in Junction Trees
摘要：
Dawid、Kjaerulff和Lauritzen（1994年）初步描述了蒙特卡罗抽样方法与结点树中精确局部计算的混合。利用这两种方法的优势，这种混合推理方法有可能扩大类的问题，可以解决有限的资源，以及解决问题，否则抵制精确的解决方案。本文提供了这样一个混合方案的一个特定的实例，即，在离散贝叶斯网络的精确推理和吉布斯抽样相结合的详细描述。我们认为，这种组合要求的扩展通常的消息传递计划的普通连接树。
英文摘要：
Dawid, Kjaerulff and Lauritzen (1994) provided a preliminary description of a hybrid between Monte-Carlo sampling methods and exact local computations in junction trees. Utilizing the strengths of both methods, such hybrid inference methods has the potential of expanding the class of problems which can be solved under bounded resources as well as solving problems which otherwise resist exact solutions. The paper provides a detailed description of a particular instance of such a hybrid scheme; namely, combination of exact inference and Gibbs sampling in discrete Bayesian networks. We argue that this combination calls for an extension of the usual message passing scheme of ordinary junction trees.
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。
