论文标题
近似贝叶斯计算中的广义后期
Generalized Posteriors in Approximate Bayesian Computation
论文作者
论文摘要
复杂的模拟器已成为许多科学学科的无处不在工具，提供了自然和社会现象的高保真性，隐含的概率模型。不幸的是，他们通常缺乏常规统计分析所需的障碍。近似贝叶斯计算（ABC）已成为基于模拟的推断的关键方法，其中使用模拟器中的样品近似真正的模型可能性和后验。在本文中，我们在ABC和广义贝叶斯推断（GBI）之间建立了联系。首先，我们将ABC中的接受/拒绝步骤重新解释为隐式定义的错误模型。然后，我们认为这些隐式错误模型将始终被拼写错误。尽管ABC后期通常被视为近似标准贝叶斯后部的必要邪恶，但这使我们能够将ABC重新解释为潜在的鲁棒化策略。这使我们建议在ABC中使用GBI，这是我们从经验上探索的用例。
Complex simulators have become a ubiquitous tool in many scientific disciplines, providing high-fidelity, implicit probabilistic models of natural and social phenomena. Unfortunately, they typically lack the tractability required for conventional statistical analysis. Approximate Bayesian computation (ABC) has emerged as a key method in simulation-based inference, wherein the true model likelihood and posterior are approximated using samples from the simulator. In this paper, we draw connections between ABC and generalized Bayesian inference (GBI). First, we re-interpret the accept/reject step in ABC as an implicitly defined error model. We then argue that these implicit error models will invariably be misspecified. While ABC posteriors are often treated as a necessary evil for approximating the standard Bayesian posterior, this allows us to re-interpret ABC as a potential robustification strategy. This leads us to suggest the use of GBI within ABC, a use case we explore empirically.
