Buch, Englisch, 324 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 497 g
Buch, Englisch, 324 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 497 g
Reihe: Chapman & Hall/CRC Computer Science & Data Analysis
ISBN: 978-0-367-57226-6
Verlag: Chapman and Hall/CRC
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.
Autoren/Hrsg.
Weitere Infos & Material
Introduction to Bayesian Statistics. Bayesian Hierarchical Modeling. Model-Based Bayesian Inference. Linear and Generalized Linear Models. Linear and Generalized Linear Mixed Models. Zero-Inflated Mixture Models. Survival Analysis. Nonparametric Regression and Additive Models. Functional Regression Models. Measurement Error Models. Quantile Regression.