Prior Density

Prior density estimation focuses on accurately determining the probability distribution of unknown parameters before observing data, a crucial step in Bayesian inference and various machine learning tasks. Current research emphasizes developing flexible and robust methods for estimating these priors, employing neural networks, hybrid energy-based models, and generative models to capture complex shapes and high-dimensional distributions. These advancements improve the accuracy and efficiency of Bayesian inference in diverse applications, including 3D reconstruction, out-of-distribution detection, and phylogenetic parameter inference, ultimately leading to more reliable and informative results.