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25955904
Elements of Stochastic Processes: A Computational Approach
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Item #:
25955904
USD 66
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What Stands Out
Computational Focus
Emphasizes practical computational techniques, making complex stochastic processes more accessible through hands-on applications and coding examples, catering to both students and professionals in data-driven fields.
Interactive Learning
Incorporates interactive exercises and real-world datasets, enhancing engagement and retention for learners, empowering them with skills to analyze and model stochastic systems effectively.
Comprehensive Coverage
Covers a wide range of topics from basics to advanced concepts, ensuring a well-rounded understanding, which is crucial for those seeking to excel in probabilistic modeling and decision-making.
Product Details
| Publisher | FE Press, LLC |
| Publication date | October 24, 2017 |
| Language | English |
| Print length | 246 pages |
| ISBN-10 | 0979757673 |
| ISBN-13 | 978-0979757679 |
| Item Weight | 11.3 ounces (320.36 grams) |
| Dimensions | 6 x 0.56 x 9 inches (15.2 x 1.4 x 22.9 cm) |
Who Should Buy?
-
Graduate Students
Ideal for graduate students in applied mathematics or statistics pursuing a comprehensive understanding of stochastic processes.
-
Data Scientists
Data scientists can enhance their modeling skills with methods applied in stochastic processes for complex data analysis.
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Professionals in Finance
Financial professionals will benefit from practical applications of stochastic processes in risk management and option pricing.
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Beginners in Statistics
Not suitable for beginners, as it assumes prior knowledge of advanced statistical concepts and computational techniques.
Product Description
Elements of Stochastic Processes: A Computational Approach
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Probability & Statistics Editorial Review
The 'Elements of Stochastic Processes: A Computational Approach' is a comprehensive textbook that provides a detailed introduction to stochastic processes and their computational implementation. The book covers the fundamental concepts and mathematical tools needed for a solid understanding of stochastic processes. It takes a computational approach to illustrate the concepts and includes numerous examples and exercises to reinforce learning. The authors have done an excellent job of presenting complex ideas in a clear and concise manner. Overall, this book is highly recommended for anyone interested in learning about stochastic processes.
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Pros
- Comprehensive coverage of stochastic processes
- Clear and concise explanations
- Uses a computational approach
- Numerous examples and exercises provided
Cons
- None mentioned in the editorial review
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