On a Multiplicative Algorithm for Computing Bayesian D-optimal Designs

Abstract

We use the minorization-maximization principle (Lange, Hunter and Yang 2000) to establish the monotonicity of a multiplicative algorithm for computing Bayesian D-optimal designs. This proves a conjecture of Dette, Pepelyshev and Zhigljavsky (2008).

References in corpus (2)

• Strict Monotonicity and Convergence Rate of Titterington's Algorithm for Computing D-optimal Designs
• Improved EM for Mixture Proportions with Applications to Nonparametric ML Estimation for Censored Data