用优化代替随机采样，大幅提升复杂模型的参数推断速度与精度
Fast and Robust Simulation-Based Inference With Optimization Monte Carlo
- 将贝叶斯推断转化为确定性优化问题，避免无效模拟
- 在高维参数空间和低信息输出场景下仍保持高精度
- 基于JAX实现向量化加速，适合需要快速迭代的研究者
复杂随机模拟器的贝叶斯参数推断因似然函数不可解析而困难。现有模拟推断方法常需大量模拟，在高维参数空间或部分无信息输出的问题中成本高昂。本文针对可微分模拟器提出新方法，通过重构优化蒙特卡洛框架，将随机模拟推断转化为确定性优化问题。利用梯度方法高效搜索后验高密度区域，避开低概率区域的浪费性模拟。基于JAX的实现对关键组件进行向量化，显著提升性能。大量实验表明，该方法在高维参数空间、无信息输出、多观测值及多峰后验等场景下，精度持续匹配甚至超越当前最优方法，同时大幅降低运行时间。
原文摘要 · Abstract (English)
Bayesian parameter inference for complex stochastic simulators is challenging due to intractable likelihood functions. Existing simulation-based inference methods often require large number of simulations and become costly to use in high-dimensional parameter spaces or in problems with partially uninformative outputs. We propose a new method for differentiable simulators that delivers accurate posterior inference with substantially reduced runtimes. Building on the Optimization Monte Carlo framework, our approach reformulates inference for stochastic simulators in terms of deterministic optimization problems. Gradient-based methods are then applied to efficiently navigate toward high-density posterior regions and avoid wasteful simulations in low-probability areas. A JAX-based implementation further enhances the performance through vectorization of key method components. Extensive experiments, including high-dimensional parameter spaces, uninformative outputs, multiple observations and multimodal posteriors show that our method consistently matches, and often exceeds, the accuracy of state-of-the-art approaches, while reducing the runtime by a substantial margin.
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