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104402284
Probability Theory, An Analytic View
товар №:
104402284
KGS 9418
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The third edition provides a rigorous yet entertaining introduction to probability theory, making it ideal for first-year graduate students.
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What Stands Out
Comprehensive Coverage
This book provides an in-depth exploration of probability theory, ensuring a thorough understanding for students and professionals alike, making it a vital resource for anyone looking to master the subject.
Analytic Approach
Employing a unique analytic perspective, this title demystifies complex concepts, allowing readers to grasp intricate theories and applications, which distinguishes it from standard probability texts.
Updated Content
Published in November 2024, this book features the latest research and advancements in probability theory, ensuring relevance and accuracy for contemporary academic and practical applications.
Информация о продукте
- The third edition of this highly regarded text provides a rigorous, yet entertaining, introduction to probability theory and the analytic ideas and tools on which the modern theory relies. The main changes are the inclusion of the Gaussian isoperimetric inequality plus many improvements and clarifications throughout the text. With more than 750 exercises, it is ideal for first-year graduate students with a good grasp of undergraduate probability theory and analysis. Starting with results about independent random variables, the author introduces weak convergence of measures and its application to the central limit theorem, and infinitely divisible laws and their associated stochastic processes. Conditional expectation and martingales follow before the context shifts to infinite dimensions, where Gaussian measures and weak convergence of measures are studied. The remainder is devoted to the mutually beneficial connection between probability theory and partial differential equations, culminating in an explanation of the relationship of Brownian motion to classical potential theory.
| Publisher | Cambridge University Press |
| Publication date | 21 Nov. 2024 |
| Edition | 3rd |
| Language | English |
| Print length | 468 pages |
| ISBN-10 | 1009549006 |
| ISBN-13 | 978-1009549004 |
| Item weight | 850 g |
| Dimensions | 17.81 x 2.69 x 25.4 cm |
Who Should Buy?
-
Students of Mathematics
Ideal for students seeking a thorough understanding of probability theory concepts and analytic methods used in mathematics.
-
Data Scientists
Beneficial for data scientists who require a solid foundation in probability for analyzing data and algorithms.
-
Researchers in Statistics
Suitable for researchers needing in-depth perspectives on probability theory to apply in various statistical research contexts.
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Casual Readers
Not suitable for casual readers looking for a light introduction to probability; this book is academically rigorous.
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Mathematical Analysis Editorial Review
Probability Theory, An Analytic View is a comprehensive text published by Cambridge University Press, set to be released on 21 Nov. 2024. This 3rd edition spans 468 pages, providing a deep dive into the analytical aspects of probability theory. With a focus on clarity and rigor, it aims to serve both students and professionals looking to enhance their understanding of probability concepts. The book is presented in English and contains insightful examples and applications, making it suitable for learners of various backgrounds. Its sizeable weight and dimensions ensure that it is a substantial resource for study and reference.
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Плюсы
- Comprehensive coverage of probability concepts
- Analytical approach enhances understanding
- Suitable for both students and professionals
- Published by a reputable publisher
- Well-structured content for ease of learning
Минусы
- Weight might make it less portable for some readers
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KGS 9418
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Особенности и преимущества
- Rigorous yet entertaining introduction to probability theory.
- Ideal for first-year graduate students with a solid foundation.
- Includes the Gaussian isoperimetric inequality.
- Over 750 exercises to enhance understanding.
- Covers weak convergence of measures and its application to the central limit theorem.
- Explores the connection between probability theory and partial differential equations.
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