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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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
id todo:paper_15393755_v75_n2_p_Zunino
record_format dspace
spelling todo:paper_15393755_v75_n2_p_Zunino2023-10-03T16:22:14Z Characterization of Gaussian self-similar stochastic processes using wavelet-based informational tools Zunino, L. Pérez, D.G. Martín, M.T. Plastino, A. Garavaglia, M. Rosso, O.A. 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 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. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_15393755_v75_n2_p_Zunino
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic 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
spellingShingle 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
Zunino, L.
Pérez, D.G.
Martín, M.T.
Plastino, A.
Garavaglia, M.
Rosso, O.A.
Characterization of Gaussian self-similar stochastic processes using wavelet-based informational tools
topic_facet 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
description 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.
format JOUR
author Zunino, L.
Pérez, D.G.
Martín, M.T.
Plastino, A.
Garavaglia, M.
Rosso, O.A.
author_facet Zunino, L.
Pérez, D.G.
Martín, M.T.
Plastino, A.
Garavaglia, M.
Rosso, O.A.
author_sort Zunino, L.
title Characterization of Gaussian self-similar stochastic processes using wavelet-based informational tools
title_short Characterization of Gaussian self-similar stochastic processes using wavelet-based informational tools
title_full Characterization of Gaussian self-similar stochastic processes using wavelet-based informational tools
title_fullStr Characterization of Gaussian self-similar stochastic processes using wavelet-based informational tools
title_full_unstemmed Characterization of Gaussian self-similar stochastic processes using wavelet-based informational tools
title_sort characterization of gaussian self-similar stochastic processes using wavelet-based informational tools
url http://hdl.handle.net/20.500.12110/paper_15393755_v75_n2_p_Zunino
work_keys_str_mv AT zuninol characterizationofgaussianselfsimilarstochasticprocessesusingwaveletbasedinformationaltools
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AT martinmt characterizationofgaussianselfsimilarstochasticprocessesusingwaveletbasedinformationaltools
AT plastinoa characterizationofgaussianselfsimilarstochasticprocessesusingwaveletbasedinformationaltools
AT garavagliam characterizationofgaussianselfsimilarstochasticprocessesusingwaveletbasedinformationaltools
AT rossooa characterizationofgaussianselfsimilarstochasticprocessesusingwaveletbasedinformationaltools
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