Semi-Amortized Bayesian Inference with Reusable Local Posteriors

We develop semi-amortized Bayesian inference (SABI) for inference in complex Bayesian models. We amortize only the difficult parts of a Bayesian model and combine neural with conventional MCMC updates for estimating all model parameters. The approach scales to complex hierarchical and spatiotemporal models with tens of thousands of parameters.