Standard syllabus
Advanced spatial statistics · Graduate · Statistics
Topics
Spatial stochastic processes
- Gaussian random fields
- Variogram modeling and kriging
- Spatial prediction and uncertainty quantification
- Anisotropy and nonstationary processes (intro)
- Lattice data and CAR models
Point patterns and areal data
- Poisson and Cox point processes
- K-functions and spatial clustering tests
- Spatial autoregressive models for areal data
- Disease mapping and BYM models (introduction)
- Change of support problem
Computation
- Likelihood and Bayesian spatial computation
- INLA for spatial models (overview)
- Large spatial datasets: approximations
- Software: INLA, spBayes, Stan spatial examples
Pricing
Graduate-level rates are set on consultation. See the pricing page for K–12 and undergraduate rates.