论文标题
具有棘手可能性的模型的基于KLD的近似实验设计
An approximate KLD based experimental design for models with intractable likelihoods
论文作者
论文摘要
数据收集是统计推断和数据科学的关键步骤，统计实验设计（ED）的目标是找到可以为推理提供大多数信息的数据收集设置。在这项工作中，我们考虑了一种特殊类型的ED问题，即可能以封闭形式可用的可能性。在这种情况下，基于流行的信息理论kullback-leibler Divergence（KLD）设计标准不能直接使用，因为它需要评估可能性函数。为了解决该问题，我们得出了一个新的实用程序功能，该功能是原始KLD实用程序的下限。该下限是根据数据空间中两个或多个熵的求和来表达的，因此可以通过熵估计方法有效地评估。我们提供了几个数值示例，以证明该方法的性能。
Data collection is a critical step in statistical inference and data science, and the goal of statistical experimental design (ED) is to find the data collection setup that can provide most information for the inference. In this work we consider a special type of ED problems where the likelihoods are not available in a closed form. In this case, the popular information-theoretic Kullback-Leibler divergence (KLD) based design criterion can not be used directly, as it requires to evaluate the likelihood function. To address the issue, we derive a new utility function, which is a lower bound of the original KLD utility. This lower bound is expressed in terms of the summation of two or more entropies in the data space, and thus can be evaluated efficiently via entropy estimation methods. We provide several numerical examples to demonstrate the performance of the proposed method.
