Scalable Simulation-Based Inference through Optimization-based Acceleration

Music City Center 

In cases where simulation-based models lack a tractable likelihood, Bayesian inference traditionally relies on Approximate Bayesian Computation (ABC). More recently, generative models have been used to approximate the posterior distribution directly. However, the computational cost of generating enough simulated samples to resemble the observed dataset typically grows exponentially with the dimensionality of the parameter space. To address this scalability issue, we propose an optimization-driven approach to inference. We also provide theoretical analysis demonstrating how our approach scales more efficiently with the dimensionality of the parameter space, offering a more practical solution for high-dimensional simulator models.

Simulation-based Inference

Optimization-based Sampling

Optimal Transport