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July 10, 2026SIAM/ASA Journal on Uncertainty QuantificationOpen Access

Unbiased Approximations for Stationary Distributions of McKean-Vlasov SDEs

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Authors

EAElsiddig AwadelkarimNCNeil K. ChadaAJAjay Jasra

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Overview

Randomized trial develops unbiased estimators for stationary distributions in stochastic processes, indicating novel methods.

Key Points

  • This research aims to create unbiased estimators for approximating the stationary distribution of McKean-Vlasov stochastic differential equations.
  • Developed unbiased estimators based on unbiased Monte Carlo methods.
  • Proved unbiasedness under specific theoretical assumptions.
  • Conducted numerical experiments on various McKean-Vlasov stochastic differential equations.
  • Demonstrated the unbiased estimator's effectiveness through various numerical tests.
  • Showed the applicability of the estimator on real-world models like the Currie–Weiss model.
  • Provided insights into ergodicity results concerning discretized processes.

Cite This Study

Awadelkarim et al. (2026) studied this question.

synapsesocial.com/papers/6a508afd6eeac72a4379fe0chttps://doi.org/10.1137/25m172392x
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  1. 1Well-posedness and averaging principle for non-Gaussian McKean–Vlasov stochastic differential equations with locally Lipschitz coefficients2025
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  3. 3Nonparametric moment method for scalarMcKean-Vlasovstochasticdifferential equations2025 · 2 citations
  4. 4Numerical scheme for the invariant measure of highly nonlinear McKean-Vlasov stochastic differential equation2026
  5. 5Stationary distributions of McKean-Vlasov SDEs with jumps: existence, uniqueness, and multiplicity2025