摘要翻译：
在大型贝叶斯网络模型中，随机抽样算法是精确算法的一种有吸引力的替代方法，但在证据极不可能的情况下，随机抽样算法在证据推理中表现不佳。为了解决这一问题，我们提出了一种自适应重要性抽样算法AIS-BN，该算法在极端条件下也有很好的收敛速度，并且似乎一直优于现有的抽样算法。这一性能改进的三个来源是：（1）基于有限维积分中重要抽样的理论性质和贝叶斯网络的结构优势的两种重要函数初始化启发式；（2）重要函数的平滑学习方法；（3）用于组合算法不同阶段样本的动态加权函数。我们测试了AIS-BN算法的性能以及两个最先进的通用抽样算法，即似然加权（Fung and Chang,1989；Shachter and Peot，1989)和自重要抽样（Shachter and Peot，1989)。在我们的测试中，我们使用了科学界可用的三个大型真实贝叶斯网络模型：CPCS网络（Pradhan et al.，1994)、探路者网络(Heckerman，Horvitz和Nathwani，1990)和安第斯网络(Conati，Gertner，VanLehn和Druzdzel，1997)，证据不太可能达到10^-41。虽然AIS-BN算法的性能始终优于其他两种算法，但在大多数测试用例中，其结果精度都有了数量级的提高。尽管我们无法在此报告数值结果，但在给定精度的情况下，速度的提高更为显著，因为其他算法几乎从未达到AIS-BN算法的前几次迭代所达到的精度。
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英文标题：
《AIS-BN: An Adaptive Importance Sampling Algorithm for Evidential
  Reasoning in Large Bayesian Networks》
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作者：
J. Cheng, M. J. Druzdzel
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最新提交年份：
2011
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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。
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英文摘要：
  Stochastic sampling algorithms, while an attractive alternative to exact algorithms in very large Bayesian network models, have been observed to perform poorly in evidential reasoning with extremely unlikely evidence. To address this problem, we propose an adaptive importance sampling algorithm, AIS-BN, that shows promising convergence rates even under extreme conditions and seems to outperform the existing sampling algorithms consistently. Three sources of this performance improvement are (1) two heuristics for initialization of the importance function that are based on the theoretical properties of importance sampling in finite-dimensional integrals and the structural advantages of Bayesian networks, (2) a smooth learning method for the importance function, and (3) a dynamic weighting function for combining samples from different stages of the algorithm. We tested the performance of the AIS-BN algorithm along with two state of the art general purpose sampling algorithms, likelihood weighting (Fung and Chang, 1989; Shachter and Peot, 1989) and self-importance sampling (Shachter and Peot, 1989). We used in our tests three large real Bayesian network models available to the scientific community: the CPCS network (Pradhan et al., 1994), the PathFinder network (Heckerman, Horvitz, and Nathwani, 1990), and the ANDES network (Conati, Gertner, VanLehn, and Druzdzel, 1997), with evidence as unlikely as 10^-41. While the AIS-BN algorithm always performed better than the other two algorithms, in the majority of the test cases it achieved orders of magnitude improvement in precision of the results. Improvement in speed given a desired precision is even more dramatic, although we are unable to report numerical results here, as the other algorithms almost never achieved the precision reached even by the first few iterations of the AIS-BN algorithm.
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PDF链接：
https://arxiv.org/pdf/1106.0253