Bayesian Model Selection And Statistical Modeling(1st Edition)
Authors:
Tomohiro Ando
Type:Hardcover/ PaperBack / Loose Leaf
Condition: Used/New
In Stock: 1 Left
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Book details
ISBN: 1439836140, 9781439836149
Book publisher: Chapman And Hall/CRC
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Customer Reviews
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LH
Bayesian Model Selection And Statistical Modeling is a must-read for anyone serious about advancing in statistical analysis. The depth of content is impressive, catering to both beginners and advanced users. The models and examples are highly relevant to real-world applications, which is refreshing compared to other texts I've read. Plus, I managed to snag a discount with my online subscription, making it even more worth it. Superb purchase!
SC
I have been studying statistics for a few years now, and this book provided a good balance of theoretical perspectives and practical applications. The chapters are well-structured, and the authors have done a good job explaining the complexities involved in Bayesian modeling. However, it's quite an intense read, so not for the faint of heart. Got an additional discount with my premium membership, and it delivered right on time!
JF
This book is a comprehensive guide through the complexities of Bayesian model selection. The authors have done a fantastic job breaking down difficult concepts with clear explanations and practical examples. As someone relatively new to Bayesian statistics, I found this book incredibly helpful in understanding the foundational principles and advanced techniques. Also, it was delivered much quicker than expected, which was a pleasant surprise!
BR
This book serves as a great resource for understanding Bayesian models. The practical examples are most helpful, making complex theories much easier to grasp. While the book is dense at times, the content quality makes up for it. Delivery was timely and hassle-free. Anyone looking to deepen their knowledge in Bayesian statistics should consider this a valuable addition to their library.
GD
Somewhat useful for entry-level understanding, but lacks depth in parts.





























