Evolutionary statistical procedures computation di baragona roberto (12 risultati)

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Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This proposed text appears to be a good introduction to evolutionary computation for use in applied statistics research. The authors draw from a vast base of knowledge about the current literature in both the design of evolutionary algorithms and statistical techniques. Modern statistical research is on the threshold of solving increasingly complex problems in high dimensions, and the generalization of its methodology to parameters whose estimators do not follow mathematically simple distributions is underway. Many of these challenges involve optimizing functions for which analytic solutions are infeasible. Evolutionary algorithms represent a powerful and easily understood means of approximating the optimum value in a variety of settings. The proposed text seeks to guide readers through the crucial issues of optimization problems in statistical settings and the implementation of tailored methods (including both stand-alone evolutionary algorithms and hybrid crosses of these procedures with standard statistical algorithms like Metropolis-Hastings) in a variety of applications. This book would serve as an excellent reference work for statistical researchers at an advanced graduate level or beyond, particularly those with a strong background in computer science.…

Taschenbuch. Condizione: Neu. Evolutionary Statistical Procedures | An Evolutionary Computation Approach to Statistical Procedures Designs and Applications | Roberto Baragona (u. a.) | Taschenbuch | Statistics and Computing | xii | Englisch | 2013 | Springer | EAN 9783642266546 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

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Buch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The proposed text offers a solid introduction to evolutionary computation for use in applied statistics research. The authors draw from a vast base of knowledge about the current literature in both the design of evolutionary algorithms and statistical techniques. Modern statistical research is in the process of solving increasingly complex problems in high dimensions and its methodology is now being generalized to parameters whose estimators do not follow mathematically simple distributions. Many of these challenges involve optimizing functions for which analytic solutions are not feasible. Evolutionary algorithms present a powerful and easily understood means of approximating the optimum value in a variety of settings. The proposed text seeks to guide readers through the crucial issues of optimization problems in statistical settings and the implementation of tailored methods (including both stand-alone evolutionary algorithms and hybrid methods).…