Information Bottlenecks, Causal States, and Statistical Relevance Bases: How to Represent Relevant Information in Memoryless Transduction

Cosma Rohilla Shalizi and James P. Crutchfield

Working Papers from Santa Fe Institute

Abstract: Discovering 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.

Date: 2000-07
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Persistent link: https://EconPapers.repec.org/RePEc:wop:safiwp:00-07-035

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