Reasoning with interval-valued probabilities

Janith Heendeni, Kamal Premaratne, Manohar N. Murthi

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Adequate representative statistical training data needed for machine learning algorithms are often unavailable and, when available, they are often mired in incomplete/missing data. Imputation of such data must be guided by the relationships among different variables and/or by data 'missingness' mechanisms. Interval-valued (IV) probabilities are better suited in situations where such information is unavailable. We take the viewpoint that IV probabilities (IVPs) emerge from a single underlying probability distribution about which one has only partial information. PrBounds, the IVPs that this vantage point engenders, offer a fresh perspective of the IV counterpart notions of conditioning and independence and enable reasoning to be carried out in much the same manner as one would with probabilities. When the attribute values are unknown/missing or are known to lie within a set of values, PrBounds can be efficiently learnt by a frequency counting method. The probabilities associated with an arbitrary imputation strategy, including the underlying 'true' probabilities, are guaranteed to lie within the PrBounds learnt in this manner. We present an experiment to illustrate the proposed framework.

Original languageEnglish (US)
Title of host publicationProceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9780578647098
StatePublished - Jul 2020
Event23rd International Conference on Information Fusion, FUSION 2020 - Virtual, Pretoria, South Africa
Duration: Jul 6 2020Jul 9 2020

Publication series

NameProceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020


Conference23rd International Conference on Information Fusion, FUSION 2020
Country/TerritorySouth Africa
CityVirtual, Pretoria


  • Data imputation
  • Incomplete data
  • Interval-valued probabilities
  • Missing data

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Information Systems
  • Information Systems and Management
  • Instrumentation


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