English, Article edition: 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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Bookmark: http://trove.nla.gov.au/version/143940
Physical Description
  • article
Language
  • English

Edition details

Title
  • INFORMATION BOTTLENECKS, CAUSAL STATES, AND STATISTICAL RELEVANCE BASES: HOW TO REPRESENT RELEVANT INFORMATION IN MEMORYLESS TRANSDUCTION
Author
  • COSMA ROHILLA SHALIZI
  • JAMES P. CRUTCHFIELD
Physical Description
  • article
Notes
  • 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.
  • Information bottleneck, causal state, statistical relevance, memoryless transduction
  • RePEc:wsi:acsxxx:v:05:y:2002:i:01:p:91-95
Language
  • English
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