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Pattern Discovery and Computational Mechanics

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Abstract

Computational 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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  • Cosma Rohilla Shalizi & James P. Crutchfield, 2000. "Pattern Discovery and Computational Mechanics," Working Papers 00-01-008, Santa Fe Institute.
    • Handle: RePEc:wop:safiwp:00-01-008
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    1. 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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      Keywords

      Pattern discovery; machine learning; computational mechanics; information; induction; e-machine.;
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