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Information Bottlenecks, Causal States, And Statistical Relevance Bases: How To Represent Relevant Information In Memoryless Transduction

Author

Listed:
  • 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)

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.

Suggested Citation

  • Cosma Rohilla Shalizi & James P. Crutchfield, 2002. "Information Bottlenecks, Causal States, And Statistical Relevance Bases: How To Represent Relevant Information In Memoryless Transduction," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 5(01), pages 91-95.
    • Handle: RePEc:wsi:acsxxx:v:05:y:2002:i:01:n:s0219525902000481
    DOI: 10.1142/S0219525902000481
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    Cited by:

    1. Robin Lamarche-Perrin & Sven Banisch & Eckehard Olbrich, 2016. "The Information Bottleneck Method For Optimal Prediction Of Multilevel Agent-Based Systems," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 19(01n02), pages 1-45, February.

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