量子化与鞅耦合

Pub Date : 2020-12-18 DOI:10.30757/alea.v19-01
B. Jourdain, G. Pagès
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引用次数: 5

摘要

当用两个有限支持的概率测度逼近两个有序概率测度时,量化提供了一种非常自然的方法来保持凸阶。事实上,当紧支持凸阶支配原始概率测度时,它小于其任何对偶量化,而支配原始测度大于其任何平稳(因此大于其任何二次最优)原始量化。此外,量化误差对应于每个原始概率测度与其量化之间的鞅耦合。这允许证明原始概率测度之间的任何鞅耦合都可以通过它们在Wassertein距离中的量化之间的鞅耦合来近似,其速率由量化误差给定,但也可以在更精细地适应的Wassertein-distance中。因此,尽管到目前为止,(弱)鞅最优运输问题关于边缘分布的稳定性仅在维度1中建立,但随着量化点数达到∞,它们为量化边缘数值计算的值函数在任何维度上都收敛于原始概率测度的值。
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Quantization and martingale couplings
Quantization provides a very natural way to preserve the convex order when approximating two ordered probability measures by two finitely supported ones. Indeed, when the convex order dominating original probability measure is compactly supported, it is smaller than any of its dual quantizations while the dominated original measure is greater than any of its stationary (and therefore any of its quadratic optimal) primal quantization. Moreover, the quantization errors then correspond to martingale couplings between each original probability measure and its quantization. This permits to prove that any martingale coupling between the original probability measures can be approximated by a martingale coupling between their quantizations in Wassertein distance with a rate given by the quantization errors but also in the much finer adapted Wassertein distance. As a consequence, while the stability of (Weak) Martingale Optimal Transport problems with respect to the marginal distributions has only been established in dimension 1 so far, their value function computed numerically for the quantized marginals converges in any dimension to the value for the original probability measures as the numbers of quantization points go to ∞.
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