摘要翻译：
我们引入了一个框架来表示各种有趣的问题作为对概率模型程序执行的推理。我们将这样一个问题的“解决方案”表示为一个指导程序，它与模型程序一起运行，并影响模型程序的随机选择，导致模型程序从不同的分布中取样，而不是从其先验分布中取样。理想情况下，指南程序会影响模型程序，使其在给定证据的情况下从后面取样。我们展示了如何通过对引导模型程序的多次执行采样来有效地估计真后验分布和引导模型程序诱导的分布之间的KL-散度（直到一个加性常数）。此外，我们还展示了如何在重要抽样中使用指南程序作为建议分布来统计证明证据概率的下界，以及假设和证据的概率的下界。我们可以用这两个界的商作为给定证据的假设的条件概率的估计。因此，我们将推理问题转化为一个启发式搜索更好的指导程序。
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英文标题：
《Variational Program Inference》
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作者：
Georges Harik and Noam Shazeer
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最新提交年份：
2010
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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：
  We introduce a framework for representing a variety of interesting problems as inference over the execution of probabilistic model programs. We represent a "solution" to such a problem as a guide program which runs alongside the model program and influences the model program's random choices, leading the model program to sample from a different distribution than from its priors. Ideally the guide program influences the model program to sample from the posteriors given the evidence. We show how the KL- divergence between the true posterior distribution and the distribution induced by the guided model program can be efficiently estimated (up to an additive constant) by sampling multiple executions of the guided model program. In addition, we show how to use the guide program as a proposal distribution in importance sampling to statistically prove lower bounds on the probability of the evidence and on the probability of a hypothesis and the evidence. We can use the quotient of these two bounds as an estimate of the conditional probability of the hypothesis given the evidence. We thus turn the inference problem into a heuristic search for better guide programs.
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PDF链接：
https://arxiv.org/pdf/1006.0991