Introduction to Bayesian modelling with INLA (BMIN01)

Starts:  Nov 9, 2020 9:00 AM (IDLW)
Ends:  Nov 13, 2020 5:00 PM (IDLW)

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:

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