Data Mining and Knowledge Discovery Via Statistical Mechanics in Nonlinear Stochastic Systems

40 Pages Posted: 2 Jul 1997

See all articles by Lester Ingber

Lester Ingber

Physical Studies Institute LLC

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Abstract

A modern calculus of multivariate nonlinear multiplicative Gaussian-Markovian systems provides models of many complex systems faithful to their nature, e.g., by not prematurely applying quasi linear approximations for the sole purpose of easing analysis. To handle these complex algebraic constructs, sophisticated numerical tools have been developed, e.g., methods of adaptive simulated annealing (ASA) global optimization and of path integration (PATHINT). In-depth application to three quite different complex systems have yielded some insights into the benefits to be obtained by application of these algorithms and tools, in statistical mechanical descriptions of neocortex (short-term memory and electroencephalography), financial markets (interest rate and trading models), and combat analysis (baselining simulations to exercise data).

JEL Classification: C10

Suggested Citation

Ingber, Lester, Data Mining and Knowledge Discovery Via Statistical Mechanics in Nonlinear Stochastic Systems. Available at SSRN: https://ssrn.com/abstract=39500 or http://dx.doi.org/10.2139/ssrn.39500

Lester Ingber (Contact Author)

Physical Studies Institute LLC ( email )

Warrenton, OR 97146
United States

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