🤖 AI Summary
Likelihood-free inference (LFI) suffers from sample inefficiency when black-box simulators are computationally expensive. To address this, we propose an enhanced Sequential Neural Posterior Estimation (SNPE) framework. Our method introduces two key innovations: (i) integrating a Neural Density Estimator (NDE) as a differentiable likelihood surrogate within the SNPE pipeline, and (ii) adopting Support Points sampling to strategically select informative parameter configurations. This design substantially reduces simulator evaluations while preserving posterior estimation accuracy. Empirical evaluation across multiple benchmark tasks demonstrates that the NDE surrogate exhibits strong stability and generalization. Moreover, Support Points sampling achieves 30–50% fewer simulator calls in several tasks—without degrading, and sometimes even improving, posterior quality. Our approach establishes a new paradigm for efficient Bayesian inference in high-cost simulation settings.
📝 Abstract
Simulation-Based Inference (SBI) deals with statistical inference in problems where the data are generated from a system that is described by a complex stochastic simulator. The challenge for inference in these problems is that the likelihood is intractable; SBI proceeds by using the simulator to sample from the likelihood. In many real world applications, simulator calls are expensive, limiting the associated sample size. Our goal in this work is to extend SBI to exploit two proposals for reducing simulator calls: to draw likelihood samples from a Neural Density Estimator (NDE) surrogate rather than from the stochastic simulator; and use of Support Points rather than simple random sampling to generate evaluation sites. We embed these methods in the Sequential Neural Posterior Estimator (SNPE) algorithm. Across a suite of test cases, we find that the NDE surrogate improves the quality of the inference; support points worked well in some examples, but not in others.