HOURS: 9:50 - 11:25; Tues. - Thus. 

Online

This class is intended for students that have a background in statistical methods and modeling that includes Bayesian statistics. The class is focused on models for data that are spatially referenced. The class will have a strong emphasis on model based geostatistic methods with Bayesian inference. Geostatistics refers to models for random processes that are indexed at fixed locations that are irregularly scattered. We will look into the theoretical properties of those models as well as into the computational issues involved in the estimation of their parameters. Familiarity with R, linear models, Bayesian methods and MCMC methods will be assumed.

This class is restricted to graduate student enrollment. Undergraduates with the appropriate pre-requisites can ask the instructor for permission.

Instructor 

Bruno Sansó, BE 361 C