Approximate Bayesian Computation

Approximate Bayesian Computation (ABC) is a family of computational methods for performing Bayesian inference when the likelihood function of a model is intractable, relying instead on simulations and comparisons of simulated and observed data. Current research focuses on improving ABC's efficiency and accuracy, particularly through the integration of neural networks (e.g., neural posterior estimation) and advanced sampling techniques (e.g., tree-based bandits, sequential Monte Carlo), often addressing high-dimensional problems and incorporating domain expertise to guide the selection of summary statistics. These advancements are enabling more robust and efficient inference in diverse fields, from bioprocess modeling and material science to robotics and policy evaluation, where traditional Bayesian methods are computationally infeasible.