Information Bottlenecks, Causal States, and Statistical Relevance Bases: How to Represent Relevant Information in Memoryless Transduction
<|reference_start|>Information Bottlenecks, Causal States, and Statistical Relevance Bases: How to Represent Relevant Information in Memoryless Transduction: 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.<|reference_end|>
@article{shalizi2000information,
    title={Information Bottlenecks, Causal States, and Statistical Relevance Bases:
  How to Represent Relevant Information in Memoryless Transduction},
    author={Cosma Rohilla Shalizi and James P. Crutchfield (Santa Fe Institute)},
    journal={Advances in Complex Systems, vol. 5, pp. 91--95 (2002)},
    year={2000},
    doi={10.1142/S0219525902000481},
    archivePrefix={arXiv},
    eprint={nlin/0006025},
    primaryClass={nlin.AO cond-mat.dis-nn cs.LG physics.data-an}
}