Abstract
This chapter deals with the use of the Statistical Complexity Measure, as defined by Lopez Ruiz, Mancini and Calbet [Phys. Lett. A 209 (1995) 321–326] and modified by Rosso and coworkers [P. W. Lamberti, M. T. Martin, A. Plastino, O. A. Rosso; Physica A 334 (2004) 119–131] to characterize pseudo random number generators (PRNG’s) obtained from chaotic dynamical systems. It is shown that two probability distribution functions are required for a proper characterization: the first one is based on the histogram and is used to characterize the uniformity of the values in the time series; the second one is based on the permutation procedure proposed by Bandt and Pompe [Phys. Rev. Lett. 88 (2002) 174102] and characterize the uniformity of patterns of several consecutive values of the time series.
Keywords: Chaos, Random number generators, Entropy, Statistical Complexity.
Related Books
Advances in Sampling Theory-Ratio Method of Estimation
Advances in Time Series Forecasting
Advances in Time Series Forecasting
Exponential Data Fitting and its Applications
Predictive Analytics Using Statistics and Big Data: Concepts and Modeling
Probability Theory for Fuzzy Quantum Spaces with Statistical Applications
Reliability Calculations with the Stochastic Finite Element
Recent Advances in Lifetime and Reliability Models
Introductory Statistical Procedures with SPSS