Fast Expectation Propagation for Heteroscedastic, Lasso-Penalized, and Quantile Regression

Expectation propagation (EP) is an approximate Bayesian inference (ABI) method which has seen widespread use across machine learning and statistics, owing to its accuracy and speed. However, it is often difficult to apply EP to models with complex likelihoods, where the EP updates do not have a tractable form and need to be calculated using methods such as multivariate numerical quadrature. These methods increase run time and reduce the appeal of EP as a fast approximate method. In this paper, …

accuracy apply bayesian bayesian inference form inference lasso machine machine learning propagation quantile regression speed statistics tractable updates