45 citations · 341 across the 31 of their papers we have counts for
4 papers · 1 filter
Graphical Gaussian Process Models for Highly Multivariate Spatial Data
Debangan Dey, Abhirup Datta, Sudipto Banerjee
For multivariate spatial Gaussian process (GP) models, customary specifications of cross-covariance functions do not exploit relational inter-variable graphs to ensure process-leve…
Spatial Factor Modeling: A Bayesian Matrix-Normal Approach for Misaligned Data
Lu Zhang, Sudipto Banerjee
Multivariate spatially-oriented data sets are prevalent in the environmental and physical sciences. Scientists seek to jointly model multiple variables, each indexed by a spatial l…
Highly Scalable Bayesian Geostatistical Modeling via Meshed Gaussian Processes on Partitioned Domains
Michele Peruzzi, Sudipto Banerjee, Andrew O. Finley
We introduce a class of scalable Bayesian hierarchical models for the analysis of massive geostatistical datasets. The underlying idea combines ideas on high-dimensional geostatist…
High-dimensional Multivariate Geostatistics: A Bayesian Matrix-Normal Approach
Lu Zhang, Sudipto Banerjee, Andrew O. Finley
Joint modeling of spatially-oriented dependent variables is commonplace in the environmental sciences, where scientists seek to estimate the relationships among a set of environmen…