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books
| book details |
Bayesian Regression Modeling with INLA
By (author) Xiaofeng Wang, By (author) Yu Yue Ryan, By (author) Julian J. Faraway
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| on special |
normal price: R 2 254.95
Price: R 2 142.95
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| book description |
INLA stands for Integrated Nested Laplace Approximations, which is a new method for fitting a broad class of Bayesian regression models. No samples of the posterior marginal distributions need to be drawn using INLA, so it is a computationally convenient alternative to Markov chain Monte Carlo (MCMC), the standard tool for Bayesian inference. Bayesian Regression Modeling with INLA covers a wide range of modern regression models and focuses on the INLA technique for building Bayesian models using real-world data and assessing their validity. A key theme throughout the book is that it makes sense to demonstrate the interplay of theory and practice with reproducible studies. Complete R commands are provided for each example, and a supporting website holds all of the data described in the book. An R package including the data and additional functions in the book is available to download. The book is aimed at readers who have a basic knowledge of statistical theory and Bayesian methodology. It gets readers up to date on the latest in Bayesian inference using INLA and prepares them for sophisticated, real-world work.
| product details |
Normally shipped |
Publisher | Taylor & Francis Ltd
Published date | 30 Jun 2020
Language |
Format | Paperback / softback
Pages | 324
Dimensions | 234 x 156 x 0mm (L x W x H)
Weight | 453g
ISBN | 978-0-3675-7226-6
Readership Age |
BISAC | mathematics / probability & statistics / general
| other options |
Normally shipped |
Readership Age |
Normal Price | R 2 652.95
Price | R 2 520.95
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