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.

翻译：贝叶斯参数推断对于复杂随机模拟器而言具有挑战性，原因在于其似然函数难以处理。现有的基于仿真的推断方法通常需要大量模拟计算，在高维参数空间或部分输出信息不充分的问题中使用成本高昂。我们针对可微分模拟器提出一种新方法，能以显著降低的运行时间实现精确的后验推断。该方法建立在优化蒙特卡洛框架之上，将随机模拟器的推断问题重新表述为确定性优化问题。随后应用基于梯度的方法高效导航至后验高密度区域，避免在低概率区域进行无效模拟。基于JAX的实现通过关键方法组件的向量化进一步提升了性能。包括高维参数空间、信息不充分输出、多观测场景及多模态后验在内的广泛实验表明：我们的方法在显著缩短运行时间的同时，其精度始终能与最先进方法相匹配，并常超越之。