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Wasserstein and weighted metrics for multidimensional Gaussian distributions

Известия Саратовского университета. Новая серия. Серия: Математика. Механика. Информатика. 2023. Vol. 23. No. 4. P. 422–434.
Kelbert M., Сухов Ю. М.

We present a number of low and upper bounds for L\эevy – ´Prokhorov, Wasserstein, Frechet, and Hellinger distances between ´ probability distributions of the same or different dimensions. The weighted (or context-sensitive) total variance and Hellinger distances are introduced. The upper and low bounds for these weighted metrics are proved. The low bounds for the minimum of different errors in sensitive hypothesis testing are proved

Research target: Mathematics
Language: English
Full text
DOI
Keywords: Wasserstein distanceTotal variation distanceDobrushin's inequality
Publication based on the results of:
Nonstationary random environments and their applications (2023)
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