Intractable Likelihood

Intractable likelihood refers to the challenge of performing Bayesian inference when the probability of observing data given model parameters cannot be directly calculated. Research focuses on developing efficient approximation methods, often employing neural networks (e.g., within simulation-based inference or variational inference frameworks) or alternative approaches like particle methods and score matching to circumvent this difficulty. These advancements enable Bayesian analysis for complex models across diverse fields, including those with high-dimensional data or computationally expensive simulators, ultimately improving the accuracy and efficiency of scientific inference and decision-making.