Variational Posterior

Variational posterior methods approximate complex probability distributions, crucial for Bayesian inference in scenarios where exact computation is intractable. Current research focuses on improving the accuracy and efficiency of these approximations, particularly within deep learning models, employing techniques like normalizing flows, Gaussian processes, and variational autoencoders to create more flexible and expressive posterior representations. These advancements are significant for various applications, including phylogenetic inference, continual learning, and image restoration, by enabling more robust uncertainty quantification and improved model performance in data-scarce or computationally demanding settings.