Bayesian Regression Modeling With INLA(1st Edition)
Authors:
Xiaofeng Wang, Yu Ryan Yue, Julian J. Faraway
Type:Hardcover/ PaperBack / Loose Leaf
Condition: Used/New
In Stock: 1 Left
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Book details
ISBN: 1498727255, 978-1498727259
Book publisher: Chapman And Hall/CRC
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Customer Reviews
Trusted feedback from verified buyers
DH
I've read a number of texts on Bayesian statistics, and this one provides a very nice balance between theoretical grounding and practical application. The focus on INLA is particularly useful for applied statisticians. My copy arrived swiftly and was well-packaged, ensuring it got to me in great condition.
WT
Bayesian Regression Modeling With INLA, 1st Edition is an outstanding resource for anyone interested in Bayesian statistics. The authors have done a fantastic job of presenting complex concepts in an accessible manner. I was particularly impressed with the practical examples that illustrate the power of INLA for model fitting. As a member, I got an extra discount on the purchase, which was a nice bonus! Highly recommended for statisticians and data scientists alike.
LW
WOW! This book is an essential resource. The explanations are super clear, and the examples really bring the methods to life. I got my copy delivered quickly and it was in perfect condition. Truly a must-have for anyone serious about Bayesian modeling with INLA.
LB
I've been diving into Bayesian methods for a while, and 'Bayesian Regression Modeling With INLA, 1st Edition' provides a solid bridge between theory and practice. The examples are very helpful, making the concepts easy to grasp, even for those not yet experts in INLA. While I did find some chapters a bit dense, it's overall a great addition to my library. And with my subscription, the price was definitely right!
CB
The book serves as a decent introduction to Bayesian regression with INLA, but I found certain sections to be quite dense and challenging without prior knowledge. It still offers valuable insights for those familiar with the basics. Arrived quickly, though, which was nice.





























