Bayesian design for minimizing prediction uncertainty in bivariate spatial responses with applications to air quality monitoring.

ABSTRACT: Model-based geostatistical design involves the selection of locations to collect data to minimize an expected loss function over a set of all possible locations. The loss function is specified to reflect the aim of data collection, which, for geostatistical studies, could be to minimize the prediction uncertainty at unobserved locations. In this paper, we propose a new approach to design such studies via a loss function derived through considering the entropy about the model predictions and the parameters of the model. The approach includes a multivariate extension to generalized linear spatial models, and thus can be used to design experiments with more than one response. Unfortunately, evaluating our proposed loss function is computationally expensive so we provide an approximation such

SUBMITTER: Senarathne SGJ 

PROVIDER: S-EPMC11497340 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature