Peng, YijieFu, MichaelThis document is the Online Supplement to ‘Myopic Allocation Policy with Asymptotically Optimal Sampling Rate,’ to be published in the IEEE Transactions of Automatic Control in 2017.In this online appendix, we test the performance of the AOMAP (asymptotically optimal myopic allocation policy) algorithm under the unknown variances scenario and compare it with EI (expected improvement) and OCBA (optimal computing budget allocation).statistical ranking and selectionBayesian frameworkasymptotic sampling ratiooptimal computing budget allocationexpected improvementexpected value of informationknowledge gradientmyopic allocation policyOther