We present a version of Elfving's theorem for the Bayesian D-optimality criterion in nonlinear regression models. The Bayesian optimal design can be characterized as a design which allows a representation of a (uniquely determined) boundary point of a convex subset of $L^2$-integrable functions. A similar characterization is given for the Bayesian c-optimality criterion where a (possible) nonlinear function of the unknown parameters has to be estimated. The results are illustrated in the example of an exponential growth model using a gamma prior distribution.
"A note on Bayesian c- and D-optimal designs in nonlinear regression models." Ann. Statist. 24 (3) 1225 - 1234, June 1996. https://doi.org/10.1214/aos/1032526965