latent Gaussian models

Criticism and robustification of latent Gaussian models

Rafael Medeiros Cabral, Ph.D. Student, Statistics

May 28, 15:00 - 16:00

B1 L4 R4102

latent Gaussian models

Latent Gaussian models (LGM) are widely used but struggle with certain datasets that contain non-Gaussian features, such as sudden jumps or spikes. This dissertation aims to provide tools for researchers to check the adequacy of the fitted LGM (criticism); if the check fails, offer efficient and user-friendly implementations of latent non-Gaussian models, which lead to more robust inferences (robustification).

Haavard Rue

Program Chair, Statistics

Bayesian computational statistics bayesian methodology latent Gaussian models spatial statistics

Professor Haavard Rue is an internationally recognized expert in Bayesian computational statistics.