Original title: How Sir Harold Jeffreys would create a belief function based on data
Authors: Daniel, Milan ; Jiroušek, Radim ; Kratochvíl, Václav
Document type: Papers
Conference/Event: Workshop on Uncertainty Processing - WUPES 2025 /13./, Třešť (CZ), 20250604
Year: 2025
Language: eng
Abstract: Not all normalized nonnegative monotone set functions are belief functions. This paper investigates ways to modify them to obtain a belief function that preserves some of their properties. The problem is motivated by an approach to data-based learning of belief function models. The approach is based on the idea that classical methods of mathematical statistics can provide estimates of lower bounds for unknown probabilities. Thus, methods of mathematical statistics can be used to obtain a reasonable rough estimate, which is further elaborated to obtain a desired belief function model.
Keywords: belief function; confidence interval; learning
Host item entry: Proceedings of the 13th Workshop on Uncertainty Processing (WUPES’25), ISBN 978-80-7378-525-3
Note: Související webová stránka: https://wupes.utia.cas.cz/2025/Proceedings.pdf#page=101

Institution: Institute of Information Theory and Automation AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: http://library.utia.cas.cz/separaty/2025/MTR/jirousek-0636593.pdf
Original record: https://hdl.handle.net/11104/0367706

Permalink: http://www.nusl.cz/ntk/nusl-680524


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Research > Institutes ASCR > Institute of Information Theory and Automation
Conference materials > Papers