Compositional amortized inference for large-scale hierarchical Bayesian models

Hierarchical Bayesian modeling is the default approach in Bayesian analysis. However, existing Bayesian methods struggle to fit hierarchical models with many parameters on massive data sets. To address this issue, we develop a divide-and-conquer method based on score-based diffusion models. Our method scales to hundreds of thousands of parameters and enables fully Bayesian fluoresence lifetime imaging (FLI) under low signal-to-noise conditions.