Advanced spatial statistics
Graduate · Math
Syllabus focus
Topics typically covered
Click a topic for the full text and related unit practice.
Graduate Advanced spatial statistics — outline derived from course README sections and typical US statistics syllabi (OpenIntro / standard OER where applicable).
Spatial stochastic processes
- Gaussian random fields — Gaussian random fields
- Variogram modeling and kriging — Variogram modeling and kriging
- Spatial prediction — Spatial prediction
- Uncertainty quantification — Uncertainty quantification
- Anisotropy — Anisotropy
- Nonstationary processes (intro) — Nonstationary processes (intro)
- Lattice data and CAR models — Lattice data and CAR models
Point patterns and areal data
- Poisson and Cox point processes — Poisson and Cox point processes
- K-functions — K-functions
- Spatial clustering tests — Spatial clustering tests
- Spatial autoregressive models for areal data — Spatial autoregressive models for areal data
- Disease mapping — Disease mapping
- BYM models (introduction) — BYM models (introduction)
- Change of support problem — Change of support problem
Computation
- Likelihood — Likelihood
- Bayesian spatial computation — Bayesian spatial computation
- INLA for spatial models (overview) — INLA for spatial models (overview)
- Large spatial datasets: approximations — Large spatial datasets: approximations
- Software: INLA, spBayes, Stan spatial examples — Software: INLA, spBayes, Stan spatial examples
Study units
Each unit includes a study guide, worksheets, review, practice test, and answer key. One unit is free; subscribe for the full class.
- Spatial stochastic processes
Gaussian random fields
Coming soon - Point patterns and areal data
Poisson and Cox point processes
Coming soon - Computation
Likelihood
Coming soon
Notes
Graduate Advanced spatial statistics — outline derived from course README sections and typical US statistics syllabi (OpenIntro / standard OER where applicable). Topic outline: `content/topics/graduate/spatial_grad.json`.