# September 24

## Announcements

- Added to Sept. 22 lecture notes: How do you find the full-conditional distributions for the Bayesian linear model?
- Please complete the teaching teaching assessment by next Tuesday.

## Extreme precipitation in Kansas

- What we will need to learn
- How to use R as a geographic information system
- New general tools from statistics
- Gaussian process
- Metropolis and Metropolis–Hastings algorithms
- Gibbs sampler

- How to use the hierarchical modeling framework to describe kriging
- Hierarchical Bayesian model vs. “empirical” hierarchical model

- Specialized language used in spatial statistics (e.g., range, nugget, variogram)

### Kriging

- Most of what I present can be found in Wikle et al. (2019; pgs. 137-164) and Hooten and Hefley (2019; 175-189)
- Classic reference is Cressie (1993; Ch. 3)
- Use the hierarchical modeling framework to describe kriging
- Fit Bayesian kriging model to precipitation and demonstrate how to answer the problems in assignment #2
- Summary of kriging
- Positives and negatives to using this approach to solve problems in assignment #2.