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28 March 2024
 
  » arxiv » nlin.AO/0006025

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Information Bottlenecks, Causal States, and Statistical Relevance Bases: How to Represent Relevant Information in Memoryless Transduction
Cosma Rohilla Shalizi ; James P. Crutchfield ;
Date 16 Jun 2000
Journal Advances in Complex Systems, vol. 5, pp. 91--95 (2002)
Subject Adaptation and Self-Organizing Systems; Disordered Systems and Neural Networks; Data Analysis, Statistics and Probability; Learning | nlin.AO cond-mat.dis-nn cs.LG physics.data-an
AffiliationSanta Fe Institute
AbstractDiscovering relevant, but possibly hidden, variables is a key step in constructing useful and predictive theories about the natural world. This brief note explains the connections between three approaches to this problem: the recently introduced information-bottleneck method, the computational mechanics approach to inferring optimal models, and Salmon’s statistical relevance basis.
Source arXiv, nlin.AO/0006025
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