Characterization of Gaussian self-similar stochastic processes using wavelet-based informational tools

Efficient tools to characterize stochastic processes are discussed. Quantifiers originally proposed within the framework of information theory, like entropy and statistical complexity, are translated into wavelet language, which renders the above quantifiers into tools that exhibit the important &qu...

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Detalles Bibliográficos
Autores principales: Zunino, L., Pérez, D.G., Martín, M.T., Plastino, A., Garavaglia, M., Rosso, O.A.
Formato: JOUR
Materias:
Brownian movement
Computational complexity
Computer simulation
Gaussian noise (electronic)
Information theory
Probability distributions
Wavelet transforms
Fractional Gaussian noise
Statistical complexity
Wavelet theory
Wavelet-based informational tools
Random processes
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_15393755_v75_n2_p_Zunino
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
Descripción
Sumario:Efficient tools to characterize stochastic processes are discussed. Quantifiers originally proposed within the framework of information theory, like entropy and statistical complexity, are translated into wavelet language, which renders the above quantifiers into tools that exhibit the important "localization" advantages provided by wavelet theory. Two important and popular stochastic processes, fractional Brownian motion and fractional Gaussian noise, are studied using these wavelet-based informational tools. Exact analytical expressions are obtained for the wavelet probability distribution. Finally, numerical simulations are used to validate our analytical results. © 2007 The American Physical Society.

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