Bayesian Optimisation vs. Input Uncertainty Reduction
研究了仿真优化中如何平衡运行仿真与收集真实数据以降低输入不确定性，提出贝叶斯信息收集与优化算法，自动决定每一步该仿真还是收集数据，并证明收敛性。
Simulators often require calibration inputs estimated from real-world data, and the estimate can significantly affect simulation output. Particularly when performing simulation optimisation to find an optimal solution, the uncertainty in the inputs significantly affects the quality of the found solution. One remedy is to search for the solution that has the best performance on average over the uncertain range of inputs yielding an optimal compromise solution. We consider the more general setting where a user may choose between either running simulations or querying an external data source, improving the input estimate and enabling the search for a more targeted, less compromised solution. We explicitly examine the trade-off between simulation and real data collection to find the optimal solution of the simulator with the true inputs. Using a value of information procedure, we propose a novel unified simulation optimisation procedure called Bayesian Information Collection and Optimisation that, in each iteration, automatically determines which of the two actions (running simulations or data collection) is more beneficial. We theoretically prove convergence in the infinite budget limit and perform numerical experiments demonstrating that the proposed algorithm is able to automatically determine an appropriate balance between optimisation and data collection.