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4614202
Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan 2nd Edition
90% of respondents would recommend this to a friend
Item #:
4614202
SAR 306
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What Stands Out
Comprehensive Tutorial
Offers an in-depth tutorial on Bayesian data analysis, guiding readers through complex concepts with practical examples using R, JAGS, and Stan.
Real-World Applications
Equips users with the tools to apply Bayesian techniques to real-world data problems, enhancing practical understanding and analytical skills.
Updated Techniques
Includes the latest advancements and methodologies in Bayesian analysis, ensuring readers are informed about the current best practices in data science.
Product Details
| Publisher | Academic Press |
| Publication date | November 17, 2014 |
| Edition | 2nd |
| Language | English |
| Print length | 776 pages |
| ISBN-10 | 0124058884 |
| ISBN-13 | 978-0124058880 |
| Item Weight | 3.7 pounds (1.68 kg) |
| Dimensions | 7.8 x 1.7 x 9.3 inches (19.8 x 4.3 x 23.6 cm) |
Who Should Buy?
-
Students in Statistics
Ideal for students seeking foundational knowledge in Bayesian data analysis methods and practical applications using R and JAGS.
-
Data Scientists
Comprehensive resource for data scientists looking to deepen their understanding of Bayesian techniques for modeling uncertainty.
-
Research Professionals
Useful for researchers needing robust statistical frameworks to analyze complex data and interpret results using Bayesian approaches.
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Beginners in Statistics
May overwhelm absolute beginners unfamiliar with statistical concepts or programming, making it unsuitable without prior knowledge.
Product Description
Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan 2nd Edition
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Probability & Statistics Editorial Review
Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan is a highly recommended book for beginners and experts alike. The author explains concepts thoroughly and provides supportive visualizations, making it easy to understand and fast to read. The book covers the underlying ideas that build Bayesian analysis and also goes through applied aspects and crucial aspects of model interpretation. Although some readers have mentioned issues with the R code provided, the overall content and explanations in the book are deemed excellent and practical. However, there have been complaints about the readability of equations in the Kindle version. Despite this, the book is still regarded as an outstanding resource for anyone interested in learning Bayesian methods.
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Pros
- Thorough explanations in simple language
- Supportive visualizations
- Excellent coverage of Bayesian analysis
- Practical and clear explanations
- Sound theory
- Useful for beginners with some statistical knowledge
Cons
- Issues with R code
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