Optimal Predictive Inference in Log-Gaussian Random Fields
Victor De Oliveira
Department of Management Science and Statistics
The University of Texas at San Antonio
This talk reviews work on optimal predictive inference in log-Gaussian random fields. The two problems to be considered are:
(a) prediction of process values at unmeasured locations and
(b) prediction of process integrals over bounded regions.
Optimal predictors, within certain classes, are given for problems (a)
and (b) as well as comparisons with other commonly used predictors.
Shortest prediction intervals, within certain classes, are also given
for problem (a). Finally, a brief discussion is given about some
difficulties in computing prediction intervals for problem (b).