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4513747
Bayesian Data Analysis (Chapman & Hall / CRC Texts in Statistical Science)
87% of respondents would recommend this to a friend
Item #:
4513747
FJD 252
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What Stands Out
Comprehensive Coverage
The book provides a thorough exploration of Bayesian methods, covering both theoretical foundations and practical applications, making it essential for statisticians and researchers alike.
Updated Examples
Includes modern, real-world examples and datasets that illustrate Bayesian techniques, enhancing understanding and engagement for readers from diverse fields.
Accessible Learning
Written in a clear, engaging style, this edition is designed for both beginners and experienced practitioners, making complex topics easier to grasp and apply.
Product Details
| Publisher | Chapman & Hall |
| Publication date | November 7, 2013 |
| Edition | 3rd |
| Language | English |
| Print length | 675 pages |
| ISBN-10 | 1439840954 |
| ISBN-13 | 978-1439840955 |
| Item Weight | 2.94 pounds (1.33 kg) |
| Dimensions | 7.3 x 1.4 x 10.1 inches (18.5 x 3.6 x 25.7 cm) |
| Part of series | Chapman & Hall/CRC Texts in Statistical Science |
Who Should Buy?
-
Statisticians
Statisticians seeking to enhance their knowledge of Bayesian methods will find comprehensive theoretical and practical insights.
-
Graduate Students
Graduate students studying statistics or data science can benefit from in-depth examples and applications for their coursework.
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Data Scientists
Data scientists interested in advanced analytical techniques will find it valuable for implementing Bayesian approaches in real-world scenarios.
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Beginners
Beginners without prior statistics knowledge may struggle with the advanced concepts and mathematical rigor presented in the book.
Product Description
Bayesian Data Analysis (Chapman & Hall / CRC Texts in Statistical Science)
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Probability & Statistics Editorial Review
Bayesian Data Analysis (Chapman & Hall / CRC Texts in Statistical Science) offers a comprehensive introduction to Bayesian analysis, featuring updated chapters on Basis Function, Gaussian Process, Finite Mixture, and Dirichlet Process models. The improvements in layout enhance readability, providing more information per page. This book's structured exercises challenge readers and reinforce understanding, making it suitable for both beginners with basic statistics knowledge and seasoned econometricians. Reviewers commend its clear explanations and practical examples, highlighting its role as a reference in Bayesian literature. However, the book expects a foundational knowledge of classical statistics, making it a more demanding read for some.
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Pros
- Comprehensive coverage of Bayesian analysis topics
- Improved layout enhances readability and engagement
- Includes challenging exercises for deeper understanding
- Clear explanations and practical research examples
- Widely referenced in statistical literature
Cons
- Requires prior knowledge of classical statistics
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