Fast Monte-Carlo

Irene Aldridge

Papers from arXiv.org

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.

Date: 2026-05
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Published in 2025 Winter Simulation Conference (WSC), Seattle, WA, USA, 2025, pp. 2051-2062

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