Expectation Propagation

Expectation propagation (EP) is an approximate inference algorithm used to estimate complex probability distributions in various models, aiming to improve accuracy and efficiency compared to other methods like variational inference. Current research focuses on enhancing EP's robustness to noise through novel optimization techniques and integrating it with other methods such as graph convolutions for improved performance in specific applications like massive MIMO detection and Bayesian neural networks. These advancements are impacting fields ranging from wireless communication and systems biology to machine learning, enabling more accurate and efficient inference in challenging probabilistic models.