Pattern Discovery and Computational Mechanics

Cosma Rohilla Shalizi and James P. Crutchfield

Working Papers from Santa Fe Institute

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.

Keywords: Pattern discovery; machine learning; computational mechanics; information; induction; e-machine. (search for similar items in EconPapers)
Date: 2000-01
New Economics Papers: this item is included in nep-cmp and nep-evo
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Persistent link: https://EconPapers.repec.org/RePEc:wop:safiwp:00-01-008

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