Pattern Discovery and Computational Mechanics
AbstractComputational mechanics is a method for discovering, describing and quantifying patterns, using tools from statistical physics. It contructs optimal, minimal models of stochastic processes and their underlying causal structures. These models tell us about the intrinsic computation embedded within a process -- how it stores and transforms information. Here we summarize the mathematics of computational mechanics, especially recent optimality and uniqueness results. We also expound the principles and motivations underlying computational mechanics, emphasizing its connections to the minimum description length principle, PAC theory, and other aspects of machine learning.
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Bibliographic InfoPaper provided by Santa Fe Institute in its series Working Papers with number 00-01-008.
Date of creation: Jan 2000
Date of revision:
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Pattern discovery; machine learning; computational mechanics; information; induction; e-machine.;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2000-02-28 (All new papers)
- NEP-CMP-2000-03-12 (Computational Economics)
- NEP-EVO-2000-02-28 (Evolutionary Economics)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Cosma Rohilla Shalizi & James P. Crutchfield, 1999. "Computational Mechanics: Pattern and Prediction, Structure and Simplicity," Working Papers 99-07-044, Santa Fe Institute.
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