摘要翻译：
我们描述了一种在大规模概率模型中进行有效推理的变分近似方法。变分方法是确定性的过程，它提供了边际和条件概率的近似值。它们为基于随机抽样或搜索的近似推断方法提供了替代方案。我们描述了快速医学参考(QMR)网络中诊断推理问题的变分方法。QMR网络是建立在统计和专家知识基础上的大规模概率图形模型。在这个模型中，除了一小部分情况外，精确的概率推断是不可行的。我们在一个大的诊断测试案例集上评估了我们的变分推理算法，并将该算法与最先进的随机抽样方法进行了比较。
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英文标题：
《Variational Probabilistic Inference and the QMR-DT Network》
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作者：
T. S. Jaakkola, M. I. Jordan
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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：
  We describe a variational approximation method for efficient inference in large-scale probabilistic models. Variational methods are deterministic procedures that provide approximations to marginal and conditional probabilities of interest. They provide alternatives to approximate inference methods based on stochastic sampling or search. We describe a variational approach to the problem of diagnostic inference in the `Quick Medical Reference' (QMR) network. The QMR network is a large-scale probabilistic graphical model built on statistical and expert knowledge. Exact probabilistic inference is infeasible in this model for all but a small set of cases. We evaluate our variational inference algorithm on a large set of diagnostic test cases, comparing the algorithm to a state-of-the-art stochastic sampling method.
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PDF链接：
https://arxiv.org/pdf/1105.5462