INLA-SPDE Spatio-Temporal Bayesian Optimization is introduced, the first scalable BO framework for spatio-temporal data, that models the log-intensity with a Log-Gaussian Cox Process and performs inference via Integrated Nested Laplace Approximation and Stochastic Partial Differential Equations (INLA-SPDE) approach.
Kai-Chuang Yang, H. Rue, Jakob Zeitler· 0 citations
We introduce a closed-form, refit-free procedure for leave-one-cluster-out (LOCO) cross-validation in multilevel Gaussian Bayesian structural equation models (SEMs), together with predictive scoring of every nested submodel. Conditional independence of clusters given the parameters expresses the cluster-deleted posteri...
Mohammad Alhyari, Haziq Jamil, Hans Montcho et al.· 0 citations
The log Gaussian Cox Process (LGCP) is one of the most widely used models for the analysis of spatial point patterns. Although Bayesian methods and software for fitting LGCPs are now well established, practical tools for model criticism, predictive assessment, and model comparison remain comparatively underdeveloped. T...
Hans Montcho, H. Rue, F. Lindgren et al.· 0 citations
A distance-based prior is proposed that assigns mass decaying exponentially in the Fisher arc-length distance from a user-specified reference correlation matrix, enabling shrinkage toward any target correlation structure rather than being confined to the identity matrix.
A. Freni-Sterrantino, J. van Niekerk, E. Krainski et al.· 2 citations
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