Bayesian Inference

Bayesian inference is a statistical framework for updating beliefs about unknown parameters based on observed data, aiming to quantify uncertainty and make robust predictions. Current research emphasizes developing efficient algorithms, such as those based on neural networks (e.g., simulation-based inference, variational autoencoders), to handle complex models and high-dimensional data, often incorporating techniques like amortized inference and gradient-based methods (e.g., Stein variational gradient descent). These advancements are significantly impacting various scientific fields, enabling more accurate and reliable inference in applications ranging from cosmology and medical diagnostics to robotics and materials science.