Fast and Robust Simulation-Based Inference With Optimization Monte Carlo

TL;DR

This paper introduces an efficient simulation-based Bayesian inference method for complex stochastic simulators, leveraging optimization and gradient techniques to reduce computational costs while maintaining high accuracy.

Contribution

It reformulates inference as deterministic optimization problems within the Optimization Monte Carlo framework, enabling faster and more robust posterior estimation.

Findings

Achieves accurate posterior inference with fewer simulations.

Outperforms existing methods in high-dimensional and complex scenarios.

Reduces runtime significantly while maintaining or improving accuracy.

Abstract

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.