DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression
DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression
复制标题
DR-ABC：基于核的分布回归的近似贝叶斯计算
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Y. Teh
中科院分区：
文献类型：
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作者：
Jovana Mitrovic;D. Sejdinovic;Y. Teh
由于评估昂贵或难以处理的似然函数，在复杂的生成模型中执行精确的后验推理通常很困难或不可能。近似贝叶斯计算 (ABC) 是一种推理框架，它根据通过一组预定义的汇总统计量测量的观察数据和模拟数据之间的相似性构建真实似然的近似值。尽管选择适当的针对特定问题的汇总统计量至关重要地影响似然近似的质量，从而影响 ABC 中后验样本的质量，但只有少数有原则的通用方法来选择或构建此类汇总统计量。在本文中，我们使用基于内核的分布回归为此任务开发了一个新颖的框架。我们使用基于核的分布回归对数据分布和汇总统计的最佳选择（相对于损失函数）之间的函数关系进行建模。我们表明，我们的方法可以使用大规模核学习的随机傅立叶特征框架以计算和统计有效的方式实现。除此之外，与解决玩具和现实世界问题的相关方法相比，我们的框架显示出卓越的性能。
Performing exact posterior inference in complex generative models is often difficult or impossible due to an expensive to evaluate or intractable likelihood function. Approximate Bayesian computation (ABC) is an inference framework that constructs an approximation to the true likelihood based on the similarity between the observed and simulated data as measured by a predefined set of summary statistics. Although the choice of appropriate problem-specific summary statistics crucially influences the quality of the likelihood approximation and hence also the quality of the posterior sample in ABC, there are only few principled general-purpose approaches to the selection or construction of such summary statistics. In this paper, we develop a novel framework for this task using kernel-based distribution regression. We model the functional relationship between data distributions and the optimal choice (with respect to a loss function) of summary statistics using kernel-based distribution regression. We show that our approach can be implemented in a computationally and statistically efficient way using the random Fourier features framework for large-scale kernel learning. In addition to that, our framework shows superior performance when compared to related methods on toy and real-world problems.
影响因子：
9.8
作者：
Wu, Michael C.;Kraft, Peter;Lin, Xihong
通讯作者：
Lin, Xihong