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62118634
Brownian Motion and Stochastic Calculus (Graduate Texts in Mathematics, 113)
Item #:
62118634
GBP 36
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What Stands Out
In-depth Analysis
This text provides a thorough exploration of Brownian motion and stochastic calculus, making complex concepts accessible for graduate students and researchers seeking to deepen their understanding of advanced mathematical theories.
Comprehensive Coverage
Covering essential topics with clarity, it addresses both foundational aspects and current developments in stochastic processes, ensuring readers gain a holistic view of the subject matter necessary for academic or professional advancement.
Enhanced Examples
The second edition includes updated examples and exercises, allowing readers to apply theoretical insights practically, which reinforces learning and facilitates better retention of stochastic calculus principles.
Product Details
| Publisher | Springer |
| Publication date | August 25, 1991 |
| Edition | 2nd |
| Language | English |
| Print length | 493 pages |
| ISBN-10 | 0387976558 |
| ISBN-13 | 978-0387976556 |
| Item Weight | 3.35 pounds (1.52 kg) |
| Dimensions | 6.1 x 1.12 x 9.25 inches (15.5 x 2.8 x 23.5 cm) |
| Part of series | Graduate Texts in Mathematics |
Who Should Buy?
-
Graduate Students
Ideal for graduate students pursuing mathematics or related fields, with a focus on stochastic processes and calculus.
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Researchers
Beneficial for researchers needing comprehensive insights into Brownian motion and its applications in probability theory.
-
Professionals
Useful for professionals in finance or data science looking to model random processes with advanced mathematical tools.
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Undergraduates
Not suitable for undergraduate students lacking foundational knowledge in stochastic calculus or advanced mathematics.
Product Description
Brownian Motion and Stochastic Calculus (Graduate Texts in Mathematics, 113)
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Probability & Statistics Editorial Review
The "Brownian Motion and Stochastic Calculus" (Graduate Texts in Mathematics, 113, 2nd Edition) has been well-received by readers, particularly those in graduate studies and areas of mathematical finance and network modeling. Reviewers commend the authors for their precise and meticulous presentation of complex topics, making the material accessible and engaging, even for those with extensive experience in the subject. The book begins by clearly defining foundational concepts like martingales and filtrations, progressing into more intricate theories, including the rigorous treatment of Brownian motion and stochastic integration. Readers have highlighted the logical flow of the book, praising how each chapter builds on the last. The clarity of explanations in chapters related to stochastic processes and their integration stands out, especially for discussions surrounding Ito and Stratonovich integrals. Notably, the connections drawn to practical applications in financial engineering, such as option pricing under the Merton Consumption theory, resonate well with those in finance-focused studies. Additionally, the text allocates significant attention to solving stochastic differential equations and linking them to partial differential equations, making it a useful resource for researchers and practitioners alike. Despite its high praise, some users have pointed out that a solid understanding of measure theoretic probability theory is beneficial for fully grasping the content, although the authors provide sufficient context to aid comprehension even for those with limited backgrounds in the subject. Overall, the text is praised for its structured approach to complex theories, making it a highly recommended resource for graduate students and professionals interested in the advanced study of Brownian motion and stochastic calculus. **
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Pros
- Clear and meticulous presentation of complex topics.
- Logical flow that progressively builds upon previous concepts.
- Comprehensive exercise sections with some solutions provided.
- Well-related to practical applications in finance and quantum field theory.
- Accessible even to those with limited background in measure theory.
Cons
- A good understanding of measure theoretic probability is helpful but not strictly necessary.
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GBP 36
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