Simulation-based inference stops training only the posterior net: summary statistics take the lead, and training size grows only polynomially in observation dimension
A simulation-based inference framework jointly learns a low-dimensional summary statistic and an amortized posterior estimator with non-asymptotic guarantees: in exponential-family and smooth-model settings, the training size needed for a target accuracy grows only polynomially with observation dimension, not exponentially as in classical nonparametric conditional distribution estimation.