Latent Gaussian models are an extremely popular, flexible class of models. Bayesian inference for these models is, however, tricky and time consuming. Recently, Rue Martino and Chopin introduced the Integrated Nested Laplace Approximation (INLA) method for deterministic fast approximate inference. In this talk we will outline the INLA approximation and its related R package. We will focus on using INLA for survival and point process models and demonstrate some of the new features. Finally we will discuss possible extensions for INLA.

Keywords: INLA; Bayesian computing; Point processes; Survival analysis

Biography: Daniel Simpson is a postdoc in statistics at the Norwegian University of Science and Technology in Trondheim.