The course covers all three main branches of spatial statistics, namely, (1) the continuum spatial variations, (2) the discrete spatial variations and, (3) the spatial point patterns. Topics include positive definite functions, geostatistics, variograms, kriging, conditional simulations, Markov random fields, conditional and intrinsic autoregressions, Ising and Potts models, pseudolikelihood, MCMC, Inference for spatial generalized linear and mixed models, Spatial Poisson, and other point processes. The computer software R is used for examples and homework problems.
Prerequisites: CSE 131; Math 233; Math 309 or Math 429; multivariable-calculus-based probability and mathematical statistics (Math/SDS 493-494 or Math/SDS 3211/4211); Math/SDS 439. Prior knowledge of R at the level introduced in Math/SDS 439 is assumed.
Course Attributes: FA NSM; AS NSM
