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Fast Monte-Carlo

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  • 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
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    File URL: http://arxiv.org/pdf/2605.02085
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