标题： 基于似然函数的无信息推断中的信息聚合 显示英文标题

标题： Pooling information in likelihood-free inference

摘要： 似然无推断（LFI）方法，例如近似贝叶斯计算，已成为在复杂模型中进行推断的常用工具。许多方法基于从合成数据派生的摘要统计量或差异。然而，确定用于构建后验的哪些摘要统计量或差异仍然是一个具有实际和理论挑战性的问题。我们提出了一种新的池化后验方法，该方法最优地结合了来自多个LFI后验的推断，而不是依赖单一向量的摘要进行推断。这种方法消除了选择单一摘要向量甚至特定LFI算法的需求。我们的方法易于实现，并避免执行涉及所有摘要统计量的高维LFI分析。我们在理论上保证了池化后验均值在渐近频率风险方面的改进性能，并在多个基准示例中展示了该方法的有效性。 显示英文摘要

摘要： Likelihood-free inference (LFI) methods, such as approximate Bayesian computation, have become commonplace for conducting inference in complex models. Many approaches are based on summary statistics or discrepancies derived from synthetic data. However, determining which summary statistics or discrepancies to use for constructing the posterior remains a challenging question, both practically and theoretically. Instead of relying on a single vector of summaries for inference, we propose a new pooled posterior that optimally combines inferences from multiple LFI posteriors. This pooled approach eliminates the need to select a single vector of summaries or even a specific LFI algorithm. Our approach is straightforward to implement and avoids performing a high-dimensional LFI analysis involving all summary statistics. We give theoretical guarantees for the improved performance of the pooled posterior mean in terms of asymptotic frequentist risk and demonstrate the effectiveness of the approach in a number of benchmark examples.