IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2605.02085.html

Fast Monte-Carlo

Author

Listed:
  • Irene Aldridge

Abstract

This paper proposes an eigenvalue-based small-sample approximation of the celebrated Markov Chain Monte Carlo that delivers an invariant steady-state distribution that is consistent with traditional Monte Carlo methods. The proposed eigenvalue-based methodology reduces the number of paths required for Monte Carlo from as many as 1,000,000 to as few as 10 (depending on the simulation time horizon $T$), and delivers comparable, distributionally robust results, as measured by the Wasserstein distance. The proposed methodology also produces a significant variance reduction in the steady-state distribution.

Suggested Citation

  • Irene Aldridge, 2026. "Fast Monte-Carlo," Papers 2605.02085, arXiv.org.
    • Handle: RePEc:arx:papers:2605.02085
    as

    Download full text from publisher

    File URL: https://arxiv.org/pdf/2605.02085
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Roger R. Crane & Frank B. Brown & Robert O. Blanchard, 1955. "An Analysis of a Railroad Classification Yard," Operations Research, INFORMS, vol. 3(3), pages 262-271, August.
    2. Alfred Blumstein, 1957. "A Monte Carlo Analysis of the Ground Controlled Approach System," Operations Research, INFORMS, vol. 5(3), pages 397-408, June.
    3. Dimitris Bertsimas & Mac Johnson & Nathan Kallus, 2015. "The Power of Optimization Over Randomization in Designing Experiments Involving Small Samples," Operations Research, INFORMS, vol. 63(4), pages 868-876, August.
    4. Gallant, A. Ronald & Hong, Han & Khwaja, Ahmed, 2018. "A Bayesian approach to estimation of dynamic models with small and large number of heterogeneous players and latent serially correlated states," Journal of Econometrics, Elsevier, vol. 203(1), pages 19-32.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fernanda Bravo & Taylor C. Corcoran & Elisa F. Long, 2022. "Flexible Drug Approval Policies," Manufacturing & Service Operations Management, INFORMS, vol. 24(1), pages 542-560, January.
    2. Yuhang Wu & Zeyu Zheng & Guangyu Zhang & Zuohua Zhang & Chu Wang, 2025. "Nonstationary A/B Tests: Optimal Variance Reduction, Bias Correction, and Valid Inference," Management Science, INFORMS, vol. 71(6), pages 4707-4727, June.
    3. Jinglong Zhao, 2024. "Experimental Design For Causal Inference Through An Optimization Lens," Papers 2408.09607, arXiv.org, revised Aug 2024.
    4. Aristotelis Epanomeritakis & Davide Viviano, 2025. "Learning What to Learn: Experimental Design when Combining Experimental with Observational Evidence," Papers 2510.23434, arXiv.org, revised Dec 2025.
    5. Eszter Czibor & David Jimenez‐Gomez & John A. List, 2019. "The Dozen Things Experimental Economists Should Do (More of)," Southern Economic Journal, John Wiley & Sons, vol. 86(2), pages 371-432, October.
    6. Guofang Huang & Hong Luo & Jing Xia, 2019. "Invest in Information or Wing It? A Model of Dynamic Pricing with Seller Learning," Management Science, INFORMS, vol. 65(12), pages 5556-5583, December.
    7. Guiteras, Raymond P. & Levine, David I. & Polley, Thomas H., 2016. "The pursuit of balance in sequential randomized trials," Development Engineering, Elsevier, vol. 1(C), pages 12-25.
    8. Rauf Ahmad & Per Johansson & Mårten Schultzberg, 2024. "Is Fisher inference inferior to Neyman inference for policy analysis?," Statistical Papers, Springer, vol. 65(6), pages 3425-3445, August.
    9. Hanming Fang & Edward Kung, 2021. "Why do life insurance policyholders lapse? The roles of income, health, and bequest motive shocks," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 88(4), pages 937-970, December.
    10. Lin, Bo-Liang & Wang, Zhi-Mei & Ji, Li-Jun & Tian, Ya-Ming & Zhou, Guo-Qing, 2012. "Optimizing the freight train connection service network of a large-scale rail system," Transportation Research Part B: Methodological, Elsevier, vol. 46(5), pages 649-667.
    11. Cousineau, Martin & Verter, Vedat & Murphy, Susan A. & Pineau, Joelle, 2023. "Estimating causal effects with optimization-based methods: A review and empirical comparison," European Journal of Operational Research, Elsevier, vol. 304(2), pages 367-380.
    12. Nikhil Bhat & Vivek F. Farias & Ciamac C. Moallemi & Deeksha Sinha, 2020. "Near-Optimal A-B Testing," Management Science, INFORMS, vol. 66(10), pages 4477-4495, October.
    13. Tsionas, Mike & Patel, Pankaj C. & Guedes, Maria João, 2022. "Endogenous efficiency of the dynamic profit maximization in the intertemporal production models of venture behavior," International Journal of Production Economics, Elsevier, vol. 246(C).
    14. Jinglong Zhao & Zijie Zhou, 2022. "Pigeonhole Design: Balancing Sequential Experiments from an Online Matching Perspective," Papers 2201.12936, arXiv.org, revised May 2024.
    15. Jason J. Sauppe & Sheldon H. Jacobson, 2017. "The role of covariate balance in observational studies," Naval Research Logistics (NRL), John Wiley & Sons, vol. 64(4), pages 323-344, June.
    16. Tsionas, Mike G. & Malikov, Emir & Kumbhakar, Subal C., 2020. "Endogenous dynamic efficiency in the intertemporal optimization models of firm behavior," European Journal of Operational Research, Elsevier, vol. 284(1), pages 313-324.
    17. Dimitris Bertsimas & Nishanth Mundru, 2023. "Optimization-Based Scenario Reduction for Data-Driven Two-Stage Stochastic Optimization," Operations Research, INFORMS, vol. 71(4), pages 1343-1361, July.
    18. Mogues, Tewodaj & Van Campenhout, Bjorn & Miehe, Caroline & Kabunga, Nassul, 2023. "The impact of community-based monitoring on public service delivery: A randomized control trial in Uganda," World Development, Elsevier, vol. 172(C).
    19. Jinglong Zhao & Zijie Zhou, 2025. "Pigeonhole Design: Balancing Sequential Experiments from an Online Matching Perspective," Management Science, INFORMS, vol. 71(3), pages 1889-1908, March.
    20. Dimitris Bertsimas & Bradley Sturt, 2020. "Computation of Exact Bootstrap Confidence Intervals: Complexity and Deterministic Algorithms," Operations Research, INFORMS, vol. 68(3), pages 949-964, May.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2605.02085. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: https://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.