This two-day short course will be on Bayesian Models using INLA.
The main purpose of this course is to present new recent developments in integrated nested Laplace approximation (INLA) that is a method for approximate Bayesian inference. Although the INLA methodology focuses on models that can be expressed as latent Gaussian Markov random fields (GMRF), this encompasses a large family of models that are used in practice. INLA has been established as an alternative to other methods such as Markov chain Monte Carlo because of its speed and ease of use via the R-INLA package. The lectures would include sections of presentations of the new results and practical sections with coding to illustrate the results for considering some applications.
For further information and registration form, please see the short course webpage: https://inlashortcourse.weebly.com/.
