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

Cosma Rohilla Shalizi; James P. Crutchfield

doi:10.1142/s0219525902000481

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arXiv · nlin/0006025

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

Cosma Rohilla Shalizi·James P. Crutchfield

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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.

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Cosma Rohilla Shalizi, James P. Crutchfield. 2000-06-16. Information Bottlenecks, Causal States, and Statistical Relevance Bases: How to Represent Relevant Information in Memoryless Transduction. https://doi.org/10.1142/s0219525902000481

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