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 ...

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 ...

Detecting gene–environment interactions with rare variants is critical in dissecting the etiology of common diseases. Interactions with rare haplotype variants (rHTVs) are of particular interest. At ...

Advances in plant imaging and computer vision have transformed agriculture and biology by enabling continuous and objective trait quantification. However, monitoring large plant populations or ...

Operational risk is an important quantitative topic as a result of the Basel II regulatory requirements. Operational risk models need to incorporate internal and external loss data observations in ...