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...

• Main Author: Wang, Xiaofeng
• Other Authors: Yue, Yu, 1981-, Faraway, Julian J. (Julian James)
• Format: Book
• Language: English
• Published: Boca Raton, Fl. : CRC Press, c2018
• Edition: 1rst ed.
• Series: Chapman & Hall/CRC computer science & data analysis series
• Subjects: ANALISIS DE REGRESION; REGRESION LINEAL
• Online Access: https://ar1lib.org/book/3580359/87333a; Información sobre Wang; Información sobre Yue; Información sobre Faraway

Table of Contents:

• 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.