Contextual probability estimation from data samples – a generalisation

Hui Wang*, Bowen Wang

*Corresponding author for this work

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


Contextual probability (G) provides an alternative, efficient way of estimating (primary) probability (P) in a principled way. G is defined in terms of P in a combinatorial way, and they have a simple linear relationship. Consequently, if one is known, the other can be calculated. It turns out G can be estimated based on a set of data samples through a simple process called neighbourhood counting. Many results about contextual probability are obtained based on the assumption that the event space is the power set of the sample space. However, the real world is usually not the case. For example, in a multidimensional sample space, the event space is typically the set of hyper tuples which is much smaller than the power set. In this paper, we generalise contextual probability to multidimensional sample space where the attributes may be categorical or numerical. We present results about the normalisation constant, the relationship between G and P and the neighbourhood counting process.

Original languageEnglish
Title of host publicationRough Sets - International Joint Conference, IJCRS 2018, Proceedings
EditorsQuang-Thuy Ha, Małgorzata Przybyła-Kasperek, Tianrui Li, Hung Son Nguyen
PublisherSpringer Verlag
Number of pages13
ISBN (Electronic)9783319993683
ISBN (Print)9783319993676
Publication statusPublished - 15 Aug 2018
Externally publishedYes
EventInternational Joint Conference on Rough Sets, IJCRS 2018 - Quy Nhon, Viet Nam
Duration: 20 Aug 201824 Aug 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11103 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Joint Conference on Rough Sets, IJCRS 2018
Country/TerritoryViet Nam
CityQuy Nhon

Bibliographical note

Funding Information:
Hui Wang gratefully acknowledges support by EU Horizon 2020 Programme (700381, ASGARD).

Publisher Copyright:
© Springer Nature Switzerland AG 20118.


  • Contextual probability
  • Neighbourhood counting
  • Probability estimation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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