Bayesian optimal experimental design has immense potential to inform the
collection of data so as to subsequently enhance our understanding of a variety
of processes. However, a major impediment is the difficulty in evaluating
optimal designs for problems with large, or high-dimensional, design spaces. We
propose an efficient search heuristic suitable for general optimisation
problems, with a particular focus on optimal Bayesian experimental design
problems. The heuristic evaluates the objective (utility) function at an
initial, randomly generated set of input values. At each generation of the
algorithm, input values are "accepted" if their corresponding objective
(utility) function satisfies some acceptance criteria, and new inputs are
sampled about these accepted points. We demonstrate the new algorithm by
evaluating the optimal Bayesian experimental designs for the previously
considered death, pharmacokinetic and logistic regression models. Comparisons
to the current "gold-standard" method are given to demonstrate the proposed
algorithm as a computationally-efficient alternative for moderately-large
design problems (i.e., up to approximately 40-dimensions).

我们提出了一种适用于一般优化问题的高效搜索启发式算法，特别针对 Bayesian 最优实验设计问题，并通过评估死亡，药物动力学和逻辑回归模型的最优 Bayesian 实验设计来展示其优越性，作为中等规模设计问题（即大约 40 个维度）的计算高效性替代方法。