Influence-based independence

Ozgur L. Ozcep, Felix Kuhr, Ralf Moller

Abstract

Conditional independence structures describe independencies of one set of variables from another set of variables conditioned upon a third set of variables. These structures are invaluable means for compact representations of knowledge because independencies can be exploited for useful factorizations. Conditional independence structures appear in different disguise in various areas of knowledge representation, be it the conditional independence of sets of random variables in probabilistic graphical models such as Bayesian networks or as conditional functions related to belief revision, or as independencies induced by (embedded) multivalued dependencies in data bases. This paper investigates conditional independencies for Boolean functions using Fourier analysis. We define three notions of independence based on the notion of influence of a variable on a function and draw connections to multivalued dependencies.

OriginalspracheEnglisch
Seiten26 - 31
Seitenumfang6
PublikationsstatusVeröffentlicht - 2019
Veranstaltung32nd International Florida Artificial Intelligence Research Society Conference - https://sites.google.com/view/flairs-32homepage/home, Sarasota, USA / Vereinigte Staaten
Dauer: 19.05.201922.05.2019

Tagung, Konferenz, Kongress

Tagung, Konferenz, Kongress32nd International Florida Artificial Intelligence Research Society Conference
KurztitelFLAIRS 2019
Land/GebietUSA / Vereinigte Staaten
OrtSarasota
Zeitraum19.05.1922.05.19

Strategische Forschungsbereiche und Zentren

  • Zentren: Zentrum für Künstliche Intelligenz Lübeck (ZKIL)
  • Querschnittsbereich: Intelligente Systeme

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