Neural Posterior Estimation

Neural Posterior Estimation (NPE) is a machine learning approach to Bayesian inference, aiming to efficiently approximate complex posterior distributions when the likelihood function is intractable but simulations of the model are feasible. Current research focuses on improving the accuracy and scalability of NPE methods, exploring architectures like normalizing flows and incorporating techniques such as importance sampling and preconditioning to address limitations in existing algorithms (e.g., Sequential NPE, Flow Matching Posterior Estimation). These advancements are significantly impacting various scientific fields, enabling faster and more reliable inference in applications ranging from materials science and astrophysics to biology and epidemiology.