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Probability Theory (Courant Lecture Notes)
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A top expert in probability theory presents essential topics in measure theory and probability distributions, covering both independent and dependent random variables.
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Product Details
| Publisher | Amer Mathematical Society |
| Publication date | January 1, 2001 |
| Language | English |
| Print length | 167 pages |
| ISBN-10 | 0821828525 |
| ISBN-13 | 978-0821828526 |
| Item Weight | 7.4 ounces (209.79 grams) |
| Dimensions | 7.25 x 0.5 x 10.25 inches (18.4 x 1.3 x 26 cm) |
Who Should Buy?
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University Students
Ideal for advanced undergraduate or graduate students studying mathematics or statistics, seeking a comprehensive understanding of probability theory.
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Mathematics Instructors
Useful for teachers and lecturers who need a resource for teaching probability concepts and theory in higher education.
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Self-learners
Great for individuals pursuing self-study in probability, providing clear explanations and examples to deepen understanding.
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Beginners
Not suitable for those new to mathematics or probability, as it assumes prior knowledge of basic concepts.
Product Description
Probability Theory (Courant Lecture Notes)
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Probability & Statistics Editorial Review
The Probability Theory Courant Lecture Notes has received generally positive reviews, with readers praising its minimalist approach and readability. However, some readers found it too concise and lacking in detail. The book is recommended for first year graduate probability courses and is described as beautifully written. However, some key concepts are left to exercises, which may make it less suitable for self-study. Overall, the book is a valuable resource for those taking a course in probability theory and seeking foundational knowledge in the subject. **
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Pros
- Minimalist approach
- Readable and well-written
- Valuable resource for foundational knowledge in probability theory
Cons
- Too concise for some readers
Product Price History
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Features & Benefits
- Topics in probability theory from a first-year graduate course
- Introduction to characteristic functions and weak convergence
- Proofs of weak and strong limit theorems for sums of independent random variables
- Focus on dependent random variables, martingales, and Markov chains
- Includes examples, exercises, and standard results in the field
- Suitable text for first-year graduate course in probability
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