Interprets MLVI in SBMs as srGW OT projection, proves asymptotic consistency of unregularized srGW, and demonstrates regularized version for simultaneous parameter recovery and model selection.
Rohde, and Heiko Hoffmann
2 Pith papers cite this work, alongside 14 external citations. Polarity classification is still indexing.
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Parameters from statistical reconstruction of Itô process coefficients via normal mixture separation improve autoregressive time series prediction.
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Bridging Maximum Likelihood and Optimal Transport for Efficient Inference and Model Selection in Stochastic Block Models
Interprets MLVI in SBMs as srGW OT projection, proves asymptotic consistency of unregularized srGW, and demonstrates regularized version for simultaneous parameter recovery and model selection.
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Extraction of informative statistical features in the problem of forecasting time series generated by It{\^{o}}-type processes
Parameters from statistical reconstruction of Itô process coefficients via normal mixture separation improve autoregressive time series prediction.