Simulation-based Criticism of Complex Biological Models with Neural Evidence Estimation
The Neural Evidence Estimation (NEE) method uses normalizing flows to learn a complex model's marginal likelihood directly from simulations, bypassing noisy Monte Carlo methods. This allows AI to reliably select the best biological model among several possibilities and flag models that are poorly specified, offering a rigorous way to test scientific assumptions.