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Using the Theory of Belief Functions
Using the Theory of Belief Functions for evidence calculus. Basic probability assignments, or mass functions, can be defined on the subsets of a set of possible values and combined. A mass function can be extended to a larger frame. Marginalization, i.e. reduction to a smaller frame can also be done. These features can be combined to analyze small belief networks and take into account situations where information cannot be satisfactorily described by probability distributions.
- Version1.8.0
- R version≥ 3.5.0
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?No
- Last release09/03/2024
Documentation
- VignetteBayes_Rule
- VignetteCaptain_Example
- VignetteCrime_Scene
- VignetteCrime_Scene_Commonality
- VignetteEvidential_Modelling
- VignetteHolmes_Burglary
- VignetteIntroduction to Belief Functions
- VignettePJM_example_DSC
- VignettePJM_example_DSC_Multivalued_Map
- VignettePJM_example_DSC_Simplified
- VignetteReliability_Proof_Machinery
- VignetteSimple_Implication
- VignetteTemplate
- VignetteThe Monty Hall Game
- VignetteThe original peter, John and Mary example
- VignettePeeling algorithm on Zadeh's Example
- MaterialREADME
- MaterialNEWS
Team
Peiyuan Zhu
Claude Boivin
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