论文标题
从条件信念功能中生成非破坏性样本
Non-Destructive Sample Generation From Conditional Belief Functions
论文作者
论文摘要
本文提出了一种新方法，以从有条件的有条件信念功能的有限但无聊的子集中产生样本。它假定沿贝叶斯网络结构的信念函数的分解（分解）。它应用了一般的条件信念功能。
This paper presents a new approach to generate samples from conditional belief functions for a restricted but non trivial subset of conditional belief functions. It assumes the factorization (decomposition) of a belief function along a bayesian network structure. It applies general conditional belief functions.
