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Information Bottlenecks, Causal States, and Statistical Relevance Bases: How to Represent Relevant Information in Memoryless Transduction | Cosma Rohilla Shalizi
; James P. Crutchfield
; | Date: |
16 Jun 2000 | Journal: | Advances in Complex Systems, vol. 5, pp. 91--95 (2002) | Subject: | Adaptation and Self-Organizing Systems; Disordered Systems and Neural Networks; Data Analysis, Statistics and Probability; Learning | nlin.AO cond-mat.dis-nn cs.LG physics.data-an | Affiliation: | Santa Fe Institute | 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. | Source: | arXiv, nlin.AO/0006025 | Services: | Forum | Review | PDF | Favorites |
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