Bayesian spatial statistics and modeling represent a robust inferential framework where uncertainty in spatial processes is explicitly quantified through probability distributions. This approach ...

Image analysis and computer vision / Donald Geman and Basilis Gidas -- Oceanographic and atmospheric applications of spatial statistics and digital image analysis / James J. Simpson -- Spatial ...

Spatial Data Science introduces fundamental aspects of spatial data that every data scientist should know before they start working with spatial data. These aspects include how geometries are ...

The rapid advancement of spatial and single-cell omics technologies has revolutionized molecular biosciences by enabling high-resolution profiling of gene ...

This course covers specialized methods and models that have been created for performing statistical analysis on spatial data. Students will learn basic statistical concepts and how to apply them to ...

Protein function is inherently spatial—the same molecule can elicit distinct biological outcomes depending on its localization, interacting partners, and surrounding microenvironment. Conventional ...