Fundamentals of Probability: A First Course [recurso electrónico] / by Anirban DasGupta.

Por: DasGupta, Anirban [author.]Colaborador(es): SpringerLink (Online service)Tipo de material: TextoTextoSeries Springer Texts in StatisticsEditor: New York, NY : Springer New York, 2010Descripción: XVI, 494p. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9781441957801Tema(s): Mathematics | Distribution (Probability theory) | Mathematics | Probability Theory and Stochastic ProcessesFormatos físicos adicionales: Printed edition:: Sin títuloClasificación CDD: 519.2 Clasificación LoC:QA273.A1-274.9QA274-274.9Recursos en línea: Libro electrónicoTexto
Contenidos:
Introducing Probability -- The Birthday and Matching Problems -- Conditional Probability and Independence -- Integer-Valued and Discrete Random Variables -- Generating Functions -- Standard Discrete Distributions -- Continuous Random Variables -- Some Special Continuous Distributions -- Normal Distribution -- Normal Approximations and the Central Limit Theorem -- Multivariate Discrete Distributions -- Multidimensional Densities -- Convolutions and Transformations -- Markov Chains and Applications -- Urn Models in Physics and Genetics.
En: Springer eBooksResumen: This is a text encompassing all of the standard topics in introductory probability theory, together with a significant amount of optional material of emerging importance. The emphasis is on a lucid and accessible writing style, mixed with a large number of interesting examples of a diverse nature. The text will prepare students extremely well for courses in more advanced probability and in statistical theory and for the actuary exam. The book covers combinatorial probability, all the standard univariate discrete and continuous distributions, joint and conditional distributions in the bivariate and the multivariate case, the bivariate normal distribution, moment generating functions, various probability inequalities, the central limit theorem and the laws of large numbers, and the distribution theory of order statistics. In addition, the book gives a complete and accessible treatment of finite Markov chains, and a treatment of modern urn models and statistical genetics. It includes 303 worked out examples and 810 exercises, including a large compendium of supplementary exercises for exam preparation and additional homework. Each chapter has a detailed chapter summary. The appendix includes the important formulas for the distributions in common use and important formulas from calculus, algebra, trigonometry, and geometry. Anirban DasGupta is Professor of Statistics at Purdue University, USA. He has been the main editor of the Lecture Notes and Monographs series, as well as the Collections series of the Institute of Mathematical Statistics, and is currently the Co-editor of the Selected Works in Statistics and Probability series, published by Springer. He has been an associate editor of the Annals of Statistics, Journal of the American Statistical Association, Journal of Statistical Planning and Inference, International Statistical Review, Sankhya, and Metrika. He is the author of Asymptotic Theory of Statistics and Probability, 2008, and of 70 refereed articles on probability and statistics. He is a Fellow of the Institute of Mathematical Statistics.
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Colección de Libros Electrónicos QA273 .A1-274.9 (Browse shelf(Abre debajo)) 1 No para préstamo 371500-2001

Introducing Probability -- The Birthday and Matching Problems -- Conditional Probability and Independence -- Integer-Valued and Discrete Random Variables -- Generating Functions -- Standard Discrete Distributions -- Continuous Random Variables -- Some Special Continuous Distributions -- Normal Distribution -- Normal Approximations and the Central Limit Theorem -- Multivariate Discrete Distributions -- Multidimensional Densities -- Convolutions and Transformations -- Markov Chains and Applications -- Urn Models in Physics and Genetics.

This is a text encompassing all of the standard topics in introductory probability theory, together with a significant amount of optional material of emerging importance. The emphasis is on a lucid and accessible writing style, mixed with a large number of interesting examples of a diverse nature. The text will prepare students extremely well for courses in more advanced probability and in statistical theory and for the actuary exam. The book covers combinatorial probability, all the standard univariate discrete and continuous distributions, joint and conditional distributions in the bivariate and the multivariate case, the bivariate normal distribution, moment generating functions, various probability inequalities, the central limit theorem and the laws of large numbers, and the distribution theory of order statistics. In addition, the book gives a complete and accessible treatment of finite Markov chains, and a treatment of modern urn models and statistical genetics. It includes 303 worked out examples and 810 exercises, including a large compendium of supplementary exercises for exam preparation and additional homework. Each chapter has a detailed chapter summary. The appendix includes the important formulas for the distributions in common use and important formulas from calculus, algebra, trigonometry, and geometry. Anirban DasGupta is Professor of Statistics at Purdue University, USA. He has been the main editor of the Lecture Notes and Monographs series, as well as the Collections series of the Institute of Mathematical Statistics, and is currently the Co-editor of the Selected Works in Statistics and Probability series, published by Springer. He has been an associate editor of the Annals of Statistics, Journal of the American Statistical Association, Journal of Statistical Planning and Inference, International Statistical Review, Sankhya, and Metrika. He is the author of Asymptotic Theory of Statistics and Probability, 2008, and of 70 refereed articles on probability and statistics. He is a Fellow of the Institute of Mathematical Statistics.

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