Simulation Statistical Foundations and Methodology

Simulation Statistical Foundations and Methodology

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In this book, we study theoretical and practical aspects of computing methods for mathematical modelling of nonlinear systems. A number of computing techniques are considered, such as methods of operator approximation with any given accuracy; operator interpolation techniques including a non-Lagrange interpolation; methods of system representation subject to constraints associated with concepts of causality, memory and stationarity; methods of system representation with an accuracy that is the best within a given class of models; methods of covariance matrix estimation; methods for low-rank matrix approximations; hybrid methods based on a combination of iterative procedures and best operator approximation; and methods for information compression and filtering under condition that a filter model should satisfy restrictions associated with causality and different types of memory. As a result, the book represents a blend of new methods in general computational analysis, and specific, but also generic, techniques for study of systems theory ant its particular branches, such as optimal filtering and information compression. - Best operator approximation, - Non-Lagrange interpolation, - Generic Karhunen-Loeve transform - Generalised low-rank matrix approximation - Optimal data compression - Optimal nonlinear filteringConsequently, experiments with random numbers whose experimental outcomes are random variables of known ... For example, if the log-and-trig method is invoked in order to generate p successive pairs of independently distributed Gaussian random variables (of mean zero ... has itself a (univariate) Gaussian distribution of mean zero and of variance 02/(Zp); hence, for any real number c agt; 0, PH XI 2anbsp;...

Title:Simulation Statistical Foundations and Methodology
Author:Anatoli Torokhti, Phil Howlett
Publisher:Academic Press - 1972-09-29


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