论文标题
统一的摘要统计选择，以进行近似贝叶斯计算
Unifying Summary Statistic Selection for Approximate Bayesian Computation
论文作者
论文摘要
从大数据集中提取低维汇总统计数据对于有效（无可能的）推断至关重要。我们表征了不同类别的摘要，并证明了它们对于正确分析降低降低算法的重要性。我们证明，在模型的先前预测分布中最大程度地限制了预期的后熵（EPE），这是许多现有方法。它们等同于或特殊或有限的案例，可以最大程度地减少EPE。我们提供了一个统一的框架，用于获得信息摘要，为从业者提供具体的建议，并提出了一种实用方法，以获取高保真摘要，我们为基准和实践示例提供了效用。
Extracting low-dimensional summary statistics from large datasets is essential for efficient (likelihood-free) inference. We characterize different classes of summaries and demonstrate their importance for correctly analysing dimensionality reduction algorithms. We demonstrate that minimizing the expected posterior entropy (EPE) under the prior predictive distribution of the model subsumes many existing methods. They are equivalent to or are special or limiting cases of minimizing the EPE. We offer a unifying framework for obtaining informative summaries, provide concrete recommendations for practitioners, and propose a practical method to obtain high-fidelity summaries whose utility we demonstrate for both benchmark and practical examples.
