Statistical inference with belief functions: A survey
For researchers and practitioners needing uncertainty quantification with limited data, this survey provides a structured overview of belief function inference methods.
This survey reviews methods for statistical inference using belief functions, a framework for handling uncertainty when data is scarce. It covers key contributions in learning belief measures from data.
Belief functions are a powerful and popular framework for the mathematical characterisation of uncertainty, in particular in situations in which lack of data renders learning a probability distribution for the problem impractical. The first step in a reasoning chain based on belief functions is inference: how to learn a belief measure from the available data. In this survey we focus, in particular, on making inference from statistical data, and review the most significant contributions in the area.