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An introduction to the modern martin...

Urbina-Romero, Wilfredo.

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An introduction to the modern martingale theory and applications = an analytic view /

Record Type:	Electronic resources : Monograph/item
Title/Author:	An introduction to the modern martingale theory and applications/ by Wilfredo Urbina-Romero, Ricardo Rios.
Reminder of title:	an analytic view /
Author:	Urbina-Romero, Wilfredo.
other author:	Rios, Ricardo.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	xvi, 276 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 1: Introduction -- Chapter 2: Probability and Conditional Expectation -- Chapter 3: Advanced Topics in Martingale Theory -- Chapter 4: Burkholder's inequalities and Davis'sinequality -- Chapter 5: Applications of Martingales.
Contained By:	Springer Nature eBook
Subject:	Martingales (Mathematics) -
Online resource:	https://doi.org/10.1007/978-3-031-88903-5
ISBN:	9783031889035

An introduction to the modern martingale theory and applications = an analytic view /
Urbina-Romero, Wilfredo.

An introduction to the modern martingale theory and applicationsan analytic view /[electronic resource] :by Wilfredo Urbina-Romero, Ricardo Rios. - Cham :Springer Nature Switzerland :2025. - xvi, 276 p. :ill., digital ;24 cm. - Texts in applied mathematics,v. 812196-9949 ;. - Texts in applied mathematics ;v. 81..

Chapter 1: Introduction -- Chapter 2: Probability and Conditional Expectation -- Chapter 3: Advanced Topics in Martingale Theory -- Chapter 4: Burkholder's inequalities and Davis'sinequality -- Chapter 5: Applications of Martingales.

Martingale theory is a cornerstone of modern probability, offering a natural extension of the study of sums of independent random variables. Although its roots can be traced back to the work of Paul Lévy in 1937, it was Joseph L. Doob in the 1940s who formally developed the theory, culminating in his landmark book Stochastic Processes in 1953. Since then, martingale theory has evolved significantly, with deep contributions from mathematicians such as Donald L. Burkholder, Richard Gundy, and Burgess Davis, among others. This is what is now known as advanced martingale theory, which began with the publication of Burkholder's seminal paper Martingale Transforms in 1966. This book provides a comprehensive treatment of both classical and advanced martingale theory. It opens with a historical introduction, exploring foundational functions such as Rademacher, Haar, and Walsh functions, before delving into the core concepts of conditional probability. The classical theory, as developed by Doob, is meticulously presented, followed by an in-depth examination of modern advancements, including Burkholder's inequalities, Burkholder-Davis-Gundy inequality, and their generalizations, as well as good-lambda inequalities. The final chapter showcases a wide range of applications, highlighting the theory's profound impact on Banach space theory, harmonic analysis, and beyond. Intended for graduate students and researchers in probability and analysis, this book serves as both an introduction and a reference, offering a clear and structured approach to a subject that continues to shape mathematical research and its applications.

ISBN: 9783031889035

Standard No.: 10.1007/978-3-031-88903-5doiSubjects--Topical Terms:

646248
Martingales (Mathematics)

LC Class. No.: QA274.5 / .U73 2025

Dewey Class. No.: 519.236

An introduction to the modern martingale theory and applications = an analytic view /
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520 $a Martingale theory is a cornerstone of modern probability, offering a natural extension of the study of sums of independent random variables. Although its roots can be traced back to the work of Paul Lévy in 1937, it was Joseph L. Doob in the 1940s who formally developed the theory, culminating in his landmark book Stochastic Processes in 1953. Since then, martingale theory has evolved significantly, with deep contributions from mathematicians such as Donald L. Burkholder, Richard Gundy, and Burgess Davis, among others. This is what is now known as advanced martingale theory, which began with the publication of Burkholder's seminal paper Martingale Transforms in 1966. This book provides a comprehensive treatment of both classical and advanced martingale theory. It opens with a historical introduction, exploring foundational functions such as Rademacher, Haar, and Walsh functions, before delving into the core concepts of conditional probability. The classical theory, as developed by Doob, is meticulously presented, followed by an in-depth examination of modern advancements, including Burkholder's inequalities, Burkholder-Davis-Gundy inequality, and their generalizations, as well as good-lambda inequalities. The final chapter showcases a wide range of applications, highlighting the theory's profound impact on Banach space theory, harmonic analysis, and beyond. Intended for graduate students and researchers in probability and analysis, this book serves as both an introduction and a reference, offering a clear and structured approach to a subject that continues to shape mathematical research and its applications.
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