New Gaussian Approximation Bounds for Markov Chains Developed

Researchers have developed new Gaussian approximation bounds for sums of multivariate martingale differences derived from uniformly ergodic Markov chains. These bounds, expressed in higher-order Wasserstein distance, achieve an optimal rate of O(n^{-1/2}) for fixed dimensions and orders. The methodology introduces novel techniques to manage the complexities of temporal dependence and higher-order Wasserstein distances, potentially offering broader applications in statistical analysis under such conditions. AI

IMPACT Advances statistical methods for analyzing complex data, potentially impacting AI model training and evaluation.

RANK_REASON Academic paper published on arXiv detailing statistical methods. [lever_c_demoted from research: ic=1 ai=0.4]

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• arXiv
• Facebook
• Gaussian Approximation
• Koike
• Multivariate Martingale Sums
• Netflix
• Ornstein–Uhlenbeck process
• Uniformly Ergodic Markov Chains
• Wasserstein metric

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