# Bayesian Regression with INLA

A book by Xiaofeng Wang, Ryan Yue and Julian Faraway

INLA stands for Integrated Nested Laplace Approximations. It is used for fitting
Latent Gaussian models (LGM).
LGMs include a wide range of commonly
used regression models. Unlike MCMC which uses simulation methods, INLA
uses approximation methods for Bayesian model fitting. Within the class
of LGMs, INLA can fit models much faster than MCMC-based methods.

## Chapters

- Introduction: intro.R and text online
- Theory of INLA: inla.R and
text online
- Linear Regression: blr.R
- Generalized Linear Models: glm.R
- Generalized Linear Mixed Models glmm.R and text online
- Survival Analysis: surv.R
- Random Walk Models for Smoothing: npr.R and
text online
- Gaussian Process Regression gpr.R with text online
- Generalized Additive Models: gam.R and text online
- Errors-in-Variables Regression: eiv.R
- Miscellaneous Topics: misc.R and extreme values text online

## R package

The brinla R package contains data and functions
to support the book.

Visit the INLA website to learn much more including
how to install the INLA R package.

Install the `brinla`

package with:

```
devtools::install_github("julianfaraway/brinla")
```

## Errata

Here are the errata. If you find any other errata, please let us know according to the chapter: Ch3, 4, 6, 10 (Xiaofeng Wang),
Ch2, 7 or 9 (Ryan Yue) or Ch1, 5 or 8 (Julian Faraway).

## Examples

Here are some examples

## Purchase

The book can be purchased at the usual online outlets or from the publishers Routledge