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University of Cambridge > Talks.cam > Isaac Newton Institute Seminar Series > Inference in generative models using the Wasserstein distance

## Inference in generative models using the Wasserstein distanceAdd to your list(s) Download to your calendar using vCal - Christian Robert (CNRS & Université Paris-Dauphine )
- Friday 07 July 2017, 11:45-12:30
- Seminar Room 1, Newton Institute.
If you have a question about this talk, please contact info@newton.ac.uk. SINW01 - Scalable statistical inference In purely generative models, one can simulate data given parameters but not necessarily evaluate the likelihood. We use Wasserstein distances between empirical distributions of observed data and empirical distributions of synthetic data drawn from such models to estimate their parameters. Previous interest in the Wasserstein distance for statistical inference has been mainly theoretical, due to computational limitations. Thanks to recent advances in numerical transport, the computation of these distances has become feasible, up to controllable approximation errors. We leverage these advances to propose point estimators and quasi-Bayesian distributions for parameter inference, first for independent data. For dependent data, we extend the approach by using delay reconstruction and residual reconstruction techniques. For large data sets, we propose an alternative distance using the Hilbert space-filling curve, which computation scales as This talk is part of the Isaac Newton Institute Seminar Series series. ## This talk is included in these lists:- All CMS events
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