4 papers
Bayesian Spatiotemporal Wombling
Aritra Halder, Didong Li, Sudipto Banerjee
Stochastic process models for spatiotemporal data underlying random fields find substantial utility in a range of scientific disciplines. Subsequent to predictive inference on the…
On Statistical Inference for Rates of Change in Spatial Processes over Riemannian Manifolds
Didong Li, Aritra Halder, Sudipto Banerjee
Statistical inference for spatial processes from partially realized or scattered data has seen voluminous developments in diverse areas ranging from environmental sciences to busin…
The Nearest-Neighbor Derivative Process: Modeling Spatial Rates of Change in Massive Datasets
Jiawen Chen, Aritra Halder, Yun Li +2
Gaussian processes (GPs) are instrumental in modeling spatial processes, offering precise interpolation and prediction capabilities across fields such as environmental science and…
nimblewomble: An R package for Bayesian Wombling with nimble
Aritra Halder, Sudipto Banerjee
This exposition presents nimblewomble, a software package to perform wombling, or boundary analysis, using the nimble Bayesian hierarchical modeling language in the R statistical c…