Bayesian nonparametric models are able to learn complex distributional patterns in the data by leveraging on infinite-dimensional parameters, typically consisting in vectors of random measures. To perform a principled BNP model comparison one thus needs a measure of discrepancy between vectors of random measures. In recent works the authors have proposed two different metrics based on the Wasserstein distance. We here provide new perspectives to our findings, highlighting a universal relation between the two metrics.

Wasserstein distance and applications to Bayesian nonparametrics / Catalano, Marta; Lavevant, Hugo; Lijoi, Antonio; Pruenster, Igor. - Book of Short Papers, (2022), pp. - (51st Scientific Meeting of the Italian Statistical Society (SIS 2022), Caserta, June 22-24, 2022.).

Wasserstein distance and applications to Bayesian nonparametrics

Catalano, Marta;
2022

Abstract

Bayesian nonparametric models are able to learn complex distributional patterns in the data by leveraging on infinite-dimensional parameters, typically consisting in vectors of random measures. To perform a principled BNP model comparison one thus needs a measure of discrepancy between vectors of random measures. In recent works the authors have proposed two different metrics based on the Wasserstein distance. We here provide new perspectives to our findings, highlighting a universal relation between the two metrics.
2022
9788891932310
Bayesian model comparison, Levy measure, Wasserstein distance
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11385/232223
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