Variational Distribution

Variational distributions are approximate probability distributions used in Bayesian inference to simplify complex posterior calculations. Current research focuses on improving the accuracy and efficiency of these approximations, exploring diverse approaches like normalizing flows, mixture models, and contrastive methods within various architectures including variational autoencoders and Gaussian processes. These advancements enable more robust and scalable Bayesian inference in challenging applications such as optimal experimental design, continual learning, and large-scale data analysis, ultimately leading to more reliable and informative results across numerous scientific fields.