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A method for the multifidelity Monte Carlo (MFMC) estimation of statisticalquantities is proposed which is applicable to computational budgets of anysize. Based on a sequence of optimization problems each with a globallyminimizing closed-form solution, this method extends the usability of a wellknown MFMC algorithm, recovering it when the computational budget is largeenough. Theoretical results verify that the proposed approach is at least asoptimal as its namesake and retains the benefits of multifidelity estimationwith minimal assumptions on the budget or amount of available data, providing anotable reduction in variance over simple Monte Carlo estimation.

A Multifidelity Monte Carlo Method for Realistic Computational Budgets