Posterior Distribution

Posterior distributions, representing the updated beliefs about model parameters after observing data, are central to Bayesian inference. Current research focuses on efficiently approximating these distributions, often employing deep learning architectures like variational autoencoders, normalizing flows, and neural processes, to handle complex models and high-dimensional data. This work is crucial for improving uncertainty quantification in diverse fields, from astrophysics and medical imaging to machine learning and engineering, enabling more robust and reliable predictions and decisions in the face of uncertainty. The development of more efficient and accurate methods for posterior estimation is a significant ongoing challenge.