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.

翻译：本文提出了一种基于特征值的小样本近似方法，用于著名的马尔可夫链蒙特卡洛方法，该方法能提供与传统蒙特卡洛方法一致的、不变的稳态分布。所提出的基于特征值的方法将蒙特卡洛所需的路径数量从多达1,000,000条减少到至少10条（取决于仿真时间范围$T$），并获得了具有可比性的、分布稳健的结果（以Wasserstein距离衡量）。该方法还能显著减少稳态分布的方差。