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

Cosma Rohilla Shalizi () and James P. Crutchfield ()
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Cosma Rohilla Shalizi: Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA
James P. Crutchfield: Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA

Advances in Complex Systems (ACS), 2002, vol. 05, issue 01, 91-95

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.

Keywords: Information bottleneck; causal state; statistical relevance; memoryless transduction (search for similar items in EconPapers)
Date: 2002
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DOI: 10.1142/S0219525902000481

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