# A Multifidelity Monte Carlo Method for Realistic Computational Budgets

arXiv: https://arxiv.org/abs/2206.07572 · HTML: https://mlsanity.com/paper/2206.07572/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2022.

## Built on by (most cited first, 1 of 1)

- [Context-aware learning of hierarchies of low-fidelity models for multi-fidelity uncertainty quantification](https://mlsanity.com/paper/2211.10835/) (2022)

## Abstract (arXiv)

A method for the multifidelity Monte Carlo (MFMC) estimation of statistical quantities is proposed which is applicable to computational budgets of any size. Based on a sequence of optimization problems each with a globally minimizing closed-form solution, this method extends the usability of a well known MFMC algorithm, recovering it when the computational budget is large enough. Theoretical results verify that the proposed approach is at least as optimal as its namesake and retains the benefits of multifidelity estimation with minimal assumptions on the budget or amount of available data, providing a notable reduction in variance over simple Monte Carlo estimation.