标题： 排列加速近似贝叶斯计算 显示英文标题

标题： Permutations accelerate Approximate Bayesian Computation

摘要： 近似贝叶斯计算（ABC）方法已成为在似然函数难以处理或计算成本过高的情况下进行推断的重要工具。 然而，它们的可扩展性在分层或高维模型中仍然是一个主要挑战。 在本文中，我们引入了permABC，这是一种新的ABC框架，专为同时具有全局和局部参数的场景设计，其中观测值被分组到可交换的隔间中。 基于顺序蒙特卡洛ABC（ABC-SMC）框架，permABC通过基于排列的匹配利用隔间的可交换性，显著提高了计算效率。 随后，我们开发了两种进一步的、互补的顺序策略：Over Sampling，通过暂时增加模拟隔间的数量来促进早期阶段的接受；Under Matching，通过仅匹配数据子集来放松接受条件。 这些技术即使在高维情况下也能实现稳健和可扩展的推断。 通过合成和现实世界的实验——包括对法国94个地区早期新冠疫情期间的分层易感-感染-恢复模型——我们展示了我们的方法在准确性和效率方面取得的实际提升。 显示英文摘要

摘要： Approximate Bayesian Computation (ABC) methods have become essential tools for performing inference when likelihood functions are intractable or computationally prohibitive. However, their scalability remains a major challenge in hierarchical or high-dimensional models. In this paper, we introduce permABC, a new ABC framework designed for settings with both global and local parameters, where observations are grouped into exchangeable compartments. Building upon the Sequential Monte Carlo ABC (ABC-SMC) framework, permABC exploits the exchangeability of compartments through permutation-based matching, significantly improving computational efficiency. We then develop two further, complementary sequential strategies: Over Sampling, which facilitates early-stage acceptance by temporarily increasing the number of simulated compartments, and Under Matching, which relaxes the acceptance condition by matching only subsets of the data. These techniques allow for robust and scalable inference even in high-dimensional regimes. Through synthetic and real-world experiments -- including a hierarchical Susceptible-Infectious-Recover model of the early COVID-19 epidemic across 94 French departments -- we demonstrate the practical gains in accuracy and efficiency achieved by our approach.