Bayesian Inference With Inla

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 models, The book will also cover recent research on how to extend the types of models that can be fitted with INLA and R-INLA. This will include built-in features in R-INLA to define new latent models directly in R as well as combining INLA with numerical integration and MCMC methods.

List of contents

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.

About the author

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).

Product details

• Authors: Virgilio Gomez-Rubio, Virgilio (Universidad De Castilla-La Gomez-Rubio, Gomez-Rubio Virgilio
• Publisher: Taylor & Francis Ltd.
• Languages: English
• Product format: Hardback
• Released: 17.02.2020
• EAN: 9781138039872
• ISBN: 978-1-138-03987-2
• No. of pages: 316
• Subjects: Natural sciences, medicine, IT, technology > Medicine > General; Epidemiology & medical statistics, SCIENCE / Life Sciences / General, REFERENCE / General, MATHEMATICS / Probability & Statistics / General, MEDICAL / Biochemistry, MEDICAL / Biostatistics, Probability & statistics, Probability and statistics, Epidemiology and Medical statistics