Bayesian regression modeling with INLA / Xiaofeng Wang, Yu Ryan Yue, Julian J. Faraway.
Tipo de material: TextoIdioma: Inglés Series Chapman & Hall/CRC computer science & data analysis seriesDetalles de publicación: Boca Raton, Fl. : CRC Press, c2018Edición: 1rst edDescripción: xii, 312 pISBN:- 9780367572266
- 519.536
Tipo de ítem | Biblioteca actual | Signatura | Estado | Fecha de vencimiento | Código de barras |
---|---|---|---|---|---|
Libro | Biblioteca Manuel Belgrano | 519.536 W 56622 (Navegar estantería(Abre debajo)) | Disponible | 56622 |
Bibliografía: p. 297-308.
1. Introduction -- 2. Theory of INLA -- 3. Bayesian linear regression -- 4. Generalized linear models -- 5. Linear mixed and generalized linear mixed models -- 6. Survival analysis -- 7. Random walk models for smoothing methods -- 8. Gaussian process regression -- 9. Additive and generalized additive models -- 10. Errors-in-variables regression -- 11. Miscellaneous topics in INLA -- Appendix A: Installation -- Appendix B: Uninformative priors in linear regression -- Bibliography.
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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