Imprecise Probability

Imprecise probability theory addresses the limitations of traditional probability models by acknowledging and quantifying uncertainty in probability assignments themselves, often represented as sets of probabilities or intervals. Current research focuses on developing robust algorithms and models, such as credal valuation networks and generative models using multiple transformers, to handle imprecise probabilities in various applications, including decision-making under uncertainty (e.g., robust POMDPs) and sequential data analysis. This framework is proving valuable for improving the reliability and interpretability of probabilistic models in fields ranging from climate change modeling to machine learning, where uncertainty is inherent and often crucial to consider.