Sampling Parallelism for Fast and Efficient Bayesian Learning

ArXi:2604.04736v1 Announce Type: cross Machine learning models, and deep neural networks in particular, are increasingly deployed in risk-sensitive domains such as healthcare, environmental forecasting, and finance, where reliable quantification of predictive uncertainty is essential. However, many uncertainty quantification (UQ) methods remain difficult to apply due to their substantial computational cost.