Probability With a View Towards Statistics Volume I

This book PDF is perfect for those who love Mathematics genre, written by J. Hoffman-Jorgensen and published by Routledge which was released on 22 November 2017 with total hardcover pages 630. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Probability With a View Towards Statistics Volume I books below.

Probability With a View Towards Statistics  Volume I
Author : J. Hoffman-Jorgensen
File Size : 49,5 Mb
Publisher : Routledge
Language : English
Release Date : 22 November 2017
ISBN : 9781351421584
Pages : 630 pages
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Probability With a View Towards Statistics Volume I by J. Hoffman-Jorgensen Book PDF Summary

Volume I of this two-volume text and reference work begins by providing a foundation in measure and integration theory. It then offers a systematic introduction to probability theory, and in particular, those parts that are used in statistics. This volume discusses the law of large numbers for independent and non-independent random variables, transforms, special distributions, convergence in law, the central limit theorem for normal and infinitely divisible laws, conditional expectations and martingales. Unusual topics include the uniqueness and convergence theorem for general transforms with characteristic functions, Laplace transforms, moment transforms and generating functions as special examples. The text contains substantive applications, e.g., epidemic models, the ballot problem, stock market models and water reservoir models, and discussion of the historical background. The exercise sets contain a variety of problems ranging from simple exercises to extensions of the theory.

Probability With a View Towards Statistics  Volume I

Volume I of this two-volume text and reference work begins by providing a foundation in measure and integration theory. It then offers a systematic introduction to probability theory, and in particular, those parts that are used in statistics. This volume discusses the law of large numbers for independent and non-independent

Get Book
Probability With a View Towards Statistics  Volume II

Volume II of this two-volume text and reference work concentrates on the applications of probability theory to statistics, e.g., the art of calculating densities of complicated transformations of random vectors, exponential models, consistency of maximum estimators, and asymptotic normality of maximum estimators. It also discusses topics of a pure

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Probability With a View Towards Statistics  Volume II

Volume II of this two-volume text and reference work concentrates on the applications of probability theory to statistics, e.g., the art of calculating densities of complicated transformations of random vectors, exponential models, consistency of maximum estimators, and asymptotic normality of maximum estimators. It also discusses topics of a pure

Get Book
Probability With a View Towards Statistics

Volume I of this two-volume text and reference work begins by providing a foundation in measure and integration theory. It then offers a systematic introduction to probability theory, and in particular, those parts that are used in statistics. This volume discusses the law of large numbers for independent and non-independent

Get Book
Probability With a View Towards Statistics

Volume II of this two-volume text and reference work concentrates on the applications of probability theory to statistics, e.g., the art of calculating densities of complicated transformations of random vectors, exponential models, consistency of maximum estimators, and asymptotic normality of maximum estimators. It also discusses topics of a pure

Get Book
High Dimensional Probability

An integrated package of powerful probabilistic tools and key applications in modern mathematical data science.

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Probability

Well known for the clear, inductive nature of its exposition, this reprint volume is an excellent introduction to mathematical probability theory. It may be used as a graduate-level text in one- or two-semester courses in probability for students who are familiar with basic measure theory, or as a supplement in

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