Time Schedule:
Peter Guttorp
STAT 593
Seattle Campus
Distribution-free inference, game and decision theory, advanced theory of estimation (including sequential estimation), robustness, advanced probability theory, stochastic processes or empirical processes. Prerequisite: permission of instructor. Offered: Sp.
Class description
This is a one-week intensive course in spatial and space-time modeling for environmental data. It will cover geostatistical tools, covariance modeling, nonstationarity, hierarchical models and Bayesian tools. There will be lectures in the morning and pratical computer exercises in the afternoons.
Student learning goals
General method of instruction
Recommended preparation
Understanding of regression, multivariate normal distribution, likelihood and Bayesian statistics at the level of STAT 512-513.
Class assignments and grading
