Bayesian Inference with Inla

ISBN-13: 9781032174532 / Angielski / Miękka / 2021 / 332 str.

Virgilio Gomez-Rubio

Opis

The Integrated Nested Laplace Approximation (INLA) is a popular method for approximate Bayesian inference. This book provides an introduction to the underlying INLA methodology and practical guidance on how to fit different models with R-INLA and R. This covers a wide range of applications, such as multilevel models, spatial models and survival

Spis treści

1. Introduction to Bayesian Inference. 2. The Integrated Nested Laplace Approximation. 3. Mixed-effects Models. 4. Multilevel Models. 5. Priors in R-INLA. 6. Advanced Features. 7. Spatial Models. 8. Temporal Models. 9. Smoothing. 10. Survival Models. 11. Implementing New Latent Models. 12. Missing Values and Imputation. 13. Mixture models.

O autorze

Virgilio Gómez-Rubio is associate professor in the Department of Mathematics, School of Industrial Engineering, Universidad de Castilla-La Mancha, Albacete, Spain. He has developed several packages on spatial and Bayesian statistics that are available on CRAN, as well as co-authored books on spatial data analysis and INLA including Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA (CRC Press, 2019).