Abstract

Evidence or Dempster-Shafer theory is used to model information which is both uncertain and imprecise. Such a piece of information can be captured by the mathematical model of a hint. It is shown how hints can be combined and used to judge hypotheses by degrees of support and plausibility. Applications of this theory to statistical inference, diagnostics and risk analysis, and to decision analysis are discussed. The practical implementation of Dempster-Shafer theory depends on appropriate computational architectures both for modeling and for the inference mechanisms. A fundamental scheme for local computation in hypertrees is presented.KeywordsStatistical InferenceMultivalued MappingBelief FunctionEvidential ReasoningEvidence TheoryThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.