Full Bayesian

Full Bayesian methods aim to obtain the complete probability distribution over model parameters, providing comprehensive uncertainty quantification beyond point estimates. Current research focuses on developing efficient algorithms, such as Markov Chain Monte Carlo (MCMC) and variational inference, often coupled with advanced model architectures like Bayesian neural networks and hierarchical priors (e.g., horseshoe priors) to address scalability challenges in high-dimensional problems, including structure learning and regression. These advancements enable more robust and reliable inference in diverse applications, from feature extraction and causal discovery to cosmological modeling and uncertainty quantification in complex systems.