Evidence functions: a compositional approach to information

Juan José Egozcue Rubí; Vera Pawlowsky Glahn

Ayuda

Evidence functions: a compositional approach to information

J.J. Egozcue ^[1] ; V. Pawlowsky-Glahn ^[2]
1. [1] Universitat Politècnica de Catalunya
  
  Universitat Politècnica de Catalunya
  
  Barcelona, España
2. [2] Universitat de Girona
  
  Universitat de Girona
  
  Gerona, España
Localización: Sort: Statistics and Operations Research Transactions, ISSN 1696-2281, Vol. 42, Nº. 2, 2018, págs. 101-124
Idioma: inglés
Enlaces
- Texto completo (pdf)
Resumen
- The discrete case of Bayes’ formula is considered the paradigm of information acquisition. Prior and posterior probability functions, as well as likelihood functions, called evidence functions, are compositions following the Aitchison geometry of the simplex, and have thus vector character. Bayes’ formula becomes a vector addition. The Aitchison norm of an evidence function is introduced as a scalar measurement of information. A fictitious fire scenario serves as illustration. Two different inspections of affected houses are considered. Two questions are addressed: (a) which is the information provided by the outcomes of inspections, and (b) which is the most informative inspection.
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