Exact Bayesian Inference

Exact Bayesian inference aims to compute the full posterior distribution of model parameters, providing a principled way to quantify uncertainty in various applications. Current research focuses on developing efficient algorithms for intractable problems, including leveraging techniques like Rao-Blackwellization, probability generating functions, and structured encodings to handle high-dimensional discrete or continuous data, often within probabilistic programming frameworks. These advancements enable exact inference in previously intractable scenarios, improving the accuracy and reliability of models in diverse fields such as deep learning, time series analysis, and generative modeling, while also offering improved scalability and interpretability compared to approximate methods.