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

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

2000-06-16

arxiv.org/abs/nlin/0006025v1

Advances in Complex Systems, vol. 5, pp. 91--95 (2002)

Nonlinear Sciences

Adaptation and Self-Organizing Systems

3 pages, no figures, submitted to PRE as a "brief report". Revision: added an acknowledgements section originally omitted by a

Scientific paper

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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