B1616
Title: Bayesian optimal designs with high prediction efficiency
Authors: Po Yang - University of Manitoba (Canada) [presenting]
Abstract: Design of experiments is a strategy used to identify the important factors which affect the response. A well-designed experiment plays a vital role in industry since it can provide information to conduct time- and cost-efficient processes. For response surface experiments, the prediction of the response is an important task. We propose Bayesian optimality criteria for constructing optimal designs that have high prediction efficiency and less dependence on an assumed model. The constructed designs are compared with the designs obtained using different optimality criteria.
