latent Gaussian models

On Cross Validation, Log Gaussian Cox Process and Variational Bayes

Hans Montcho, Ph.D. Student, Statistics

Apr 29, 10:00 - 12:00

B2 R5220; Zoom Meeting 99219689653

Bayesian computational technique Bayesian computational statistics latent Gaussian models Point patterns spital point patterns

This dissertation advances Bayesian computation by developing a cross validation approach to assess LGCPs defined on Euclidean, manifolds or network domains and improving skewed posterior approximations for LGMs.

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.