Informative Prior

Informative priors aim to improve the accuracy and efficiency of Bayesian inference by incorporating prior knowledge into model training. Current research focuses on developing methods to learn these priors from data, often employing neural networks (e.g., unrolled networks, Bayesian neural networks) and Gaussian processes, and exploring various variational inference techniques to handle complex posterior distributions. This work is significant because incorporating informative priors can lead to more reliable and data-efficient models across diverse applications, including medical diagnosis, autonomous driving, and molecule generation, ultimately enhancing the robustness and interpretability of machine learning systems.