摘要翻译：
本文提出了一个通用而有效的框架，用于从任意不确定信息中进行概率推理和学习。它利用有限混合模型、共轭族和因式分解的计算性质。变量的联合概率密度和（客观或主观）观测的似然函数都用一种特殊的混合模型近似，这样就可以不需要数值积分而直接得到任何所需的条件分布。我们发展了一种扩展的期望最大化(EM)算法，用于从不确定的训练样本（间接观测）中估计混合模型的参数。因此，关于输入和输出值的任何确切或不确定的信息都在推理和学习阶段得到一致的处理。这种能力在某些情况下非常有用，但在大多数替代方法中都找不到。从标准概率原理出发，对所提出的框架进行了形式化证明，并在非参数模式分类、非线性回归和模式完成等领域给出了说明性例子。最后，通过一个实际应用的实验和与标准数据库的对比结果，证明了该方法在广泛应用中的有效性。
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英文标题：
《Probabilistic Inference from Arbitrary Uncertainty using Mixtures of
  Factorized Generalized Gaussians》
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作者：
M. C. Garrido, P. E. Lopez-de-Teruel, A. Ruiz
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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：
  This paper presents a general and efficient framework for probabilistic inference and learning from arbitrary uncertain information. It exploits the calculation properties of finite mixture models, conjugate families and factorization. Both the joint probability density of the variables and the likelihood function of the (objective or subjective) observation are approximated by a special mixture model, in such a way that any desired conditional distribution can be directly obtained without numerical integration. We have developed an extended version of the expectation maximization (EM) algorithm to estimate the parameters of mixture models from uncertain training examples (indirect observations). As a consequence, any piece of exact or uncertain information about both input and output values is consistently handled in the inference and learning stages. This ability, extremely useful in certain situations, is not found in most alternative methods. The proposed framework is formally justified from standard probabilistic principles and illustrative examples are provided in the fields of nonparametric pattern classification, nonlinear regression and pattern completion. Finally, experiments on a real application and comparative results over standard databases provide empirical evidence of the utility of the method in a wide range of applications.
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PDF链接：
https://arxiv.org/pdf/1105.3635