IntroductionPurpose of this bookThis book depicts the path from Bayesian inference to deep learning as a single long-form technical volume. There is one central theme: how can we handle uncertainty in ...

Towards Reinforcement Learning-based Flow Space OptimizationA Deep Paradigm Shift in Modern Bayesian Inference — From the Limits of MCMC/Variational Inference to Neural Processes and GFlowNetsIntroduc ...

Geophysical data offer incomplete clues about the subsurface. Bayesian inversion turns them into plausible scenarios and ...

Bayesian networks, also known as Bayes nets, belief networks, or decision networks, are a powerful tool for understanding and reasoning about complex systems under uncertainty. They are essentially ...

Non-Bayesian learning in social networks refers to a paradigm in which distributed agents iteratively update probabilistic beliefs about an underlying state by combining private observations with the ...

In the ever-evolving toolkit of statistical analysis techniques, Bayesian statistics has emerged as a popular and powerful methodology for making decisions from data in the applied sciences. Bayesian ...

Floods remain among the deadliest and most economically destructive natural hazards on Earth, and the window in which authorities can act before catastrophic inundation is often measured in hours ...