A Basic Course in Measure and Probability

Theory for Applications

Nonfiction, Science & Nature, Mathematics, Probability, Statistics
Cover of the book A Basic Course in Measure and Probability by Ross Leadbetter, Stamatis Cambanis, Vladas Pipiras, Cambridge University Press
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Author: Ross Leadbetter, Stamatis Cambanis, Vladas Pipiras ISBN: 9781139861748
Publisher: Cambridge University Press Publication: January 30, 2014
Imprint: Cambridge University Press Language: English
Author: Ross Leadbetter, Stamatis Cambanis, Vladas Pipiras
ISBN: 9781139861748
Publisher: Cambridge University Press
Publication: January 30, 2014
Imprint: Cambridge University Press
Language: English

Originating from the authors' own graduate course at the University of North Carolina, this material has been thoroughly tried and tested over many years, making the book perfect for a two-term course or for self-study. It provides a concise introduction that covers all of the measure theory and probability most useful for statisticians, including Lebesgue integration, limit theorems in probability, martingales, and some theory of stochastic processes. Readers can test their understanding of the material through the 300 exercises provided. The book is especially useful for graduate students in statistics and related fields of application (biostatistics, econometrics, finance, meteorology, machine learning, and so on) who want to shore up their mathematical foundation. The authors establish common ground for students of varied interests which will serve as a firm 'take-off point' for them as they specialize in areas that exploit mathematical machinery.

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Originating from the authors' own graduate course at the University of North Carolina, this material has been thoroughly tried and tested over many years, making the book perfect for a two-term course or for self-study. It provides a concise introduction that covers all of the measure theory and probability most useful for statisticians, including Lebesgue integration, limit theorems in probability, martingales, and some theory of stochastic processes. Readers can test their understanding of the material through the 300 exercises provided. The book is especially useful for graduate students in statistics and related fields of application (biostatistics, econometrics, finance, meteorology, machine learning, and so on) who want to shore up their mathematical foundation. The authors establish common ground for students of varied interests which will serve as a firm 'take-off point' for them as they specialize in areas that exploit mathematical machinery.

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