Introduction to Bayesian modelling with INLA (BMIN02)

oliver-hookerOliver Hooker
  • 27 Jul

Introduction to Bayesian modelling with INLA (BMIN02)

4 October 2021 - 8 October 2021

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Course Overview:
The aim of the course is to introduce you to Bayesian inference using the integrated nested Laplace approximation (INLA) method and its associated R-INLA package. This course will cover the basics on the INLA methodology as well as practical modelling of different types of data.

By the end of the course participants should:

Understand the basics of Bayesian inference.
Understand how the INLA method works and its main differences with MCMC methods.
Be able to fit models with the R-INLA package.
Know how to interpret the output from model fitting.
Be confident with the use of INLA for data analysis.
Understand the different models that can be fit with INLA.
Know how to define the different parts of a model with INLA.
Be able to develop new latent effects not implemented in the R-INLA package.
Know how to define new priors not included in the R-INLA package.
Have the confidence to use INLA for their own projects.

This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.

TIME ZONE – Central European Standard Time (CEST) – however all sessions will be recorded and made available allowing attendees from different time zones to follow a day behind (please email for full details or to discuss how we can accommodate you).

Monday 4th – Classes from 10:00 to 17:00

Introduction to the course
Key concepts related to Bayesian inference
Models with conjugate priors
Introduction to Bayesian hierarchical models
Computational methods for Bayesian inference
Introduction to the INLA methodology
Fitting generalized linear models with INLA and the R-INLA package
Understanding and manipulating the output from model fitting with R-INLA

Tuesday 5th – Classes from 10:00 to 17:00

Fitting generalized linear mixed models with R-INLA
Types of latent effects in R-INLA
Models with i.i.d. latent effects
Fitting multilevel models with R-INLA
Models with correlated latent effects
Fitting time series models with R-INLA

Wednesday 6th – Classes from 10:00 to 17:00

Priors in R-INLA
Setting priors in R-INLA
Introduction to Penalized Complexity priors (PC-priors)
Defining new priors in R-INLA
Spatially correlated random effects
Fitting spatial models with R-INLA
Visualizing the output from spatial models and mapping

Thursday 7th – Classes from 10:00 to 17:00

Advanced features in R-INLA
Computing linear combinations of the latent effects
Fitting models with several likelihoods
Models with shared terms
Adding linear constraints to the latent effects
Implementing new latent models in R-INLA
Imputation and missing covariates in R-INLA

Friday 8th – Classes from 10:00 to 17:00

Case studies and own data.


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