Glen Shafer

Glen Shafer is a statistician known for developing belief functions, a mathematical framework that models uncertainty more flexibly than traditional probability. His approach allows for combining evidence from different sources, accommodating both precise and imprecise information. Shafer's work underpins the Dempster-Shafer theory, which offers a way to represent and reason about uncertainty in fields like artificial intelligence, data analysis, and decision-making, providing a more nuanced understanding when information is incomplete or ambiguous.