Spatial Statistics · Bayesian Inference · INLA
Flexible GMRF Models
A collection of extended Gaussian Markov Random Field models for Bayesian spatial and spatio-temporal inference, implemented within the INLA framework.
A collection of extended Gaussian Markov Random Field models for Bayesian spatial and spatio-temporal inference, implemented within the INLA framework.
Each model extends a classical GMRF to capture richer spatial structure,
implemented via the INLA cgeneric interface for efficient Bayesian inference.
Flexible extension of the BYM2 model, combining structured (ICAR) and unstructured (iid) spatial effects with sub-region-varying precision parameters. Inherits the interpretability and scaling properties of BYM2 while relaxing the stationarity assumption.
Block random-effect selection model with a horseshoe prior on per-block precision. Supports any GMRF precision structure per block (AR1, RW, ICAR, kernel). The block-diagonal precision enables simultaneous selection and inference over multiple candidate random effects.
These models open new directions in non-stationary spatial modeling and flexible Bayesian inference. If you are interested in collaborating on methodology, applications, or software development, I would love to hear from you.
Statistics Program, CEMSE Division
King Abdullah University of Science and Technology (KAUST)