Marginal Likelihood

Marginal likelihood quantifies the probability of observed data given a model, serving as a crucial tool for Bayesian model selection and hyperparameter optimization. Current research focuses on efficient computation of marginal likelihood, particularly within Gaussian processes and neural networks, employing techniques like iterative linear system solvers, variational inference, and annealed importance sampling to address computational challenges in high-dimensional settings. These advancements improve the scalability and accuracy of Bayesian methods, impacting diverse fields from machine learning and scientific modeling to hyperparameter tuning in deep learning, ultimately leading to more robust and reliable models.