Wiener Chaos: Moments, Cumulants and Diagrams [recurso electrónico] : A survey with computer implementation / by Giovanni Peccati, Murad S. Taqqu.

Por: Peccati, Giovanni [author.]Colaborador(es): Taqqu, Murad S [author.] | SpringerLink (Online service)Tipo de material: TextoTextoSeries Bocconi & Springer Series ; 1Editor: Milano : Springer Milan, 2011Descripción: XIII, 274 p. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9788847016798Tema(s): Mathematics | Combinatorics | Distribution (Probability theory) | Mathematics | Probability Theory and Stochastic Processes | Combinatorics | Measure and IntegrationFormatos físicos adicionales: Printed edition:: Sin títuloClasificación CDD: 519.2 Clasificación LoC:QA273.A1-274.9QA274-274.9Recursos en línea: Libro electrónicoTexto En: Springer eBooksResumen: The concept of Wiener chaos generalizes to an infinite-dimensional setting the properties of orthogonal polynomials associated with probability distributions on the real line. It plays a crucial role in modern probability theory, with applications ranging from Malliavin calculus to stochastic differential equations and from probabilistic approximations to mathematical finance. This book is concerned with combinatorial structures arising from the study of chaotic random variables related to infinitely divisible random measures. The combinatorial structures involved are those of partitions of finite sets, over which Möbius functions and related inversion formulae are defined. This combinatorial standpoint (which is originally due to Rota and Wallstrom) provides an ideal framework for diagrams, which are graphical devices used to compute moments and cumulants of random variables. Several applications are described, in particular, recent limit theorems for chaotic random variables. An Appendix presents a computer implementation in MATHEMATICA for many of the formulae.
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The concept of Wiener chaos generalizes to an infinite-dimensional setting the properties of orthogonal polynomials associated with probability distributions on the real line. It plays a crucial role in modern probability theory, with applications ranging from Malliavin calculus to stochastic differential equations and from probabilistic approximations to mathematical finance. This book is concerned with combinatorial structures arising from the study of chaotic random variables related to infinitely divisible random measures. The combinatorial structures involved are those of partitions of finite sets, over which Möbius functions and related inversion formulae are defined. This combinatorial standpoint (which is originally due to Rota and Wallstrom) provides an ideal framework for diagrams, which are graphical devices used to compute moments and cumulants of random variables. Several applications are described, in particular, recent limit theorems for chaotic random variables. An Appendix presents a computer implementation in MATHEMATICA for many of the formulae.

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