701
Isabella Verdinelli
The Bayesian theory of optimal experimental design for the normal
linear model has been developed under the assumption of known
variance. The insensitivity of specific design criteria to prior
assumptions on the variance distribution has been demonstrated in
special cases, but a general result showing the way in which Bayesian
optimal designs are affected by prior information on the variance is
lacking. This note proves that Bayesian designs are insensitive to
information about the variance in a more general way than previously thought.
Keywords: Bayesian design criteria; Design for prediction; Linear model; Expected Utility Function