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Probability metrics have become an indispensable part of modern statistics and machine learning, and they play a quintessential role in various applications, including statistical hypothesis testing and generative modeling.
Approximate bayesian computation with the sliced-wasserstein distance
Nadjahi, K., De Bortoli, V., Durmus, A., Badeau, R., and Şimşekli, U · 1910
Earlier work this paper cites.
On the composition of elementary errors: First paper: Mathematical deductions
Cramér, H · 1928
Earlier work this paper cites.
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Müller, A · 1997
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Later among the works it cites.
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