Spatial Statistics & Hierarchical Modeling for Dependent Data

3 Credits

This is a course on analytical and computational methods of spatial statistics for modeling, inference and prediction for dependent data, including point-referenced (geostatistical), areal and point process data. The methods (including uncertainty quantification using hierarchical Bayesian models) generalize to Gaussian process and GMRF models popular in machine learning and other areas of engineering. The course is targeted a students in data sciences, engineering and life sciences.