Yun Yang, University of Maryland, College Park

Title: Simulation-based Inference via Structured Score Matching

Abstract: Simulation-based inference (SBI) provides an effective framework for statistical analysis when the likelihood is intractable but model simulations are available. This talk presents a unified SBI framework that leverages structured score matching for both frequentist and Bayesian inference. On the frequentist side, we develop a likelihood-free approach that combines score matching with gradient-based optimization and bootstrap procedures for parameter estimation and uncertainty quantification. On the Bayesian side, we integrate score matching with Langevin dynamics to efficiently explore complex posterior landscapes in moderate- to high-dimensional settings.  In both cases, we design tailored score-matching estimators and architectural regularizations that embed the statistical structure of log-likelihood scores, which in turn improves estimation accuracy and scalability.