Truncated proposals for scalable and hassle-free simulation-based inference

TL;DR

TSNPE introduces a truncated proposal approach for simulation-based inference, improving efficiency and robustness, enabling scalable and accurate posterior estimation in complex scientific models.

Contribution

The paper proposes TSNPE, a novel sequential inference method using truncated proposals to overcome optimization challenges and improve scalability in simulation-based inference.

Findings

TSNPE performs comparably to existing methods on benchmark tasks.

TSNPE successfully infers posteriors in neuroscience problems where previous methods fail.

TSNPE enables efficient coverage testing for complex models.

Abstract

Simulation-based inference (SBI) solves statistical inverse problems by repeatedly running a stochastic simulator and inferring posterior distributions from model-simulations. To improve simulation efficiency, several inference methods take a sequential approach and iteratively adapt the proposal distributions from which model simulations are generated. However, many of these sequential methods are difficult to use in practice, both because the resulting optimisation problems can be challenging and efficient diagnostic tools are lacking. To overcome these issues, we present Truncated Sequential Neural Posterior Estimation (TSNPE). TSNPE performs sequential inference with truncated proposals, sidestepping the optimisation issues of alternative approaches. In addition, TSNPE allows to efficiently perform coverage tests that can scale to complex models with many parameters. We demonstrate that TSNPE performs on par with previous methods on established benchmark tasks. We then apply TSNPE to two challenging problems from neuroscience and show that TSNPE can successfully obtain the posterior distributions, whereas previous methods fail. Overall, our results demonstrate that TSNPE is an efficient, accurate, and robust inference method that can scale to challenging scientific models.