Dempster is a statistical method used for combining evidence from different sources to reach a conclusion. It is particularly useful in situations where information is uncertain or incomplete. The method assigns a degree of belief to various hypotheses, allowing for a more nuanced understanding of the data. The Dempster-Shafer theory, developed by Arthur Dempster and later expanded by Glenn Shafer, provides a framework for reasoning with uncertainty. This theory is applied in various fields, including artificial intelligence, decision-making, and risk assessment, helping to improve the accuracy of predictions and decisions based on uncertain information.