Sara Martino, PhD, Norwegian University of Science and Technology, Norway

Title: A new computational approach to fit hierarchical models with INLA.

Abstract:Sara is Associate Professor at the Department of Mathematical Science at the Norwegian University of Science and Technology, Norway. Her research focuses on spatial and computational Bayesian statistics with a particular focus on approximate Bayesian inference methods. She is interested in applications in a wide range of scientific fields from ecology and air quality to multistate survival models.

The integrated nested Laplace approximation (INLA) is a deterministic approach to Bayesian inference on latent Gaussian models (LGMs) and focuses on fast and accurate approximation of posterior marginals for the parameters in the models. Recently, methods have been developed to extend this class of models to those that can be expressed as conditional LGMs by fixing some of the parameters in the models to descriptive values.

These methods differ in the manner descriptive values are chosen. In this talk we propose to combine importance sampling with INLA (IS-INLA), and extends this approach with the more robust adaptive multiple importance sampling algorithm combined with INLA (AMIS-INLA). We compare these approaches and existing methods on a series of applications with simulated and observed datasets and evaluates their performance based on accuracy, efficiency, and robustness. The applications show that the AMIS-INLA approach, in general, outperforms the other methods compared, but the IS-INLA algorithm could be considered for faster inference when good proposals are available.

Please visit our website for the Zoom Link: https://www.mcgill.ca/epi-biostat-occh/seminars-events/seminars/biostati...

Web site : https://www.mcgill.ca/epi-biostat-occh/seminars-events/seminars/biostatistics