| Information Bottlenecks, Causal States, and Statistical Relevance Bases: How to Represent Relevant Information in Memoryless Transduction (2000) | |||||||||
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| 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.. Comment: 3 pages, no figures, submitted to PRE as a "brief report". Revision: added an acknowledgements section originally omitted by a LaTeX bug | |||||||||
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