INFORMATION BOTTLENECKS, CAUSAL STATES, AND STATISTICAL RELEVANCE BASES: HOW TO REPRESENT RELEVANT INFORMATION IN MEMORYLESS TRANSDUCTION

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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Year of publication:	2002
Authors:	SHALIZI, COSMA ROHILLA ; CRUTCHFIELD, JAMES P.
Published in:	Advances in Complex Systems (ACS). - World Scientific Publishing Co. Pte. Ltd., ISSN 1793-6802. - Vol. 05.2002, 01, p. 91-95
Publisher:	World Scientific Publishing Co. Pte. Ltd.
Subject:	Information bottleneck \| causal state \| statistical relevance \| memoryless transduction

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Extent:	application/pdf text/html
Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://ebvufind01.dmz1.zbw.eu/10005047426