Bayesian regression modeling with INLA /
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...
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| Other Authors: | , |
| Format: | Book |
| Language: | English |
| Published: |
Boca Raton, Fl. :
CRC Press,
c2018
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| Edition: | 1rst ed. |
| Series: | Chapman & Hall/CRC computer science & data analysis series
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| Subjects: | |
| Online Access: | https://ar1lib.org/book/3580359/87333a Información sobre Wang Información sobre Yue Información sobre Faraway |
| Summary: | 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. |
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| Physical Description: | xii, 312 p. |
| Bibliography: | Bibliografía: p. 297-308. |
| ISBN: | 9780367572266 |