发表机构
Chalmers University of Technology; University of Gothenburg（查尔姆斯理工大学; 哥德堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对不可处理似然模型，提出基于高斯混合专家的序贯后验估计与局部摊销比率估计重要性采样校正，并引入局部化基于仿真的校准，在三个案例中验证有效性。
AI 中文摘要
我们考虑对具有不可处理似然但可进行前向仿真的模型参数进行基于仿真的贝叶斯推断（SBI）。基于高斯混合专家代理，作为第一个贡献，我们开发了一种用于后验估计的序贯程序，其中逐步局部化的、数据知情的条件密度近似被用作提议分布。随后，通过引入局部摊销的似然-证据比率估计器的重要性采样步骤，对后验分布的最终代理进行校正。第二个贡献是局部化基于仿真的校准（SBC）。局部化SBC在比可用后验更宽的邻域上探测校准，且额外计算成本较低。局部化SBC不是为每个模拟伪观测重复运行完整的推断程序，而是对每个局部设计仅拟合一次局部代理和比率估计器，并在所有伪观测中重用它们。这种方法对SBI应用特别有吸引力。我们在三个案例研究上评估了这两项贡献，包括一个真实数据的流行病学应用。
英文摘要
We consider simulation-based Bayesian inference (SBI) for the parameters of models with intractable likelihoods but tractable forward simulation. Building on Gaussian mixtures-of-experts surrogates, as a first contribution we develop a sequential procedure for posterior estimation in which progressively localized, data informed, conditional density approximations are used as proposal distributions. A final surrogate of the posterior distribution is then corrected using an importance sampling step that introduces a locally amortized likelihood-to-evidence ratio estimator. A second contribution is localized simulation-based calibration (SBC). Localized SBC probe calibration over neighborhoods that are deliberately broader than the available posterior, at low additional computational cost. Rather than repeatedly running the full inference procedure for each simulated pseudo-observation, localized SBC fits the local surrogate and ratio estimator only once for each local design, and reuses them across all pseudo-observations. This approach is particularly appealing for SBI applications. We evaluate both contributions on three case studies, including a real-data epidemiological application.
Comments42 pages, 23 figures
