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Approximation of stationary solutions of Gaussian driven Stochastic Differential Equations

机译:高斯驱动随机微分方程平稳解的逼近。

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摘要

We study sequences of empirical measures of Euler schemes associated to some non-Markovian SDEs: SDEs driven by Gaussian processes with stationary increments. We obtain the functional convergence of this sequence to a stationary solution to the SDE. Then, we end the paper by some specific properties of this stationary solution. We show that, in contrast to Markovian SDEs, its initial random value and the driving Gaussian process are always dependent. However, under an integral representation assumption, we also obtain that the past of the solution is independent to the future of the underlying innovation process of the Gaussian driving process.
机译:我们研究与一些非马尔可夫SDE相关的Euler方案的经验测度序列:由高斯过程驱动的具有固定增量的SDE。我们获得了该序列到SDE平稳解的功能收敛。然后,我们以该固定解的某些特定属性结束本文。我们表明,与马尔可夫SDE相比,其初始随机值和驱动高斯过程始终是相关的。但是,在积分表示假设下,我们还获得了解决方案的过去与高斯驱动过程的基础创新过程的未来无关的信息。

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