Random measures are a key component of many nonparametric models in Bayesian Statistics. Their infinite-dimensionality guarantees remarkable flexibility and generality but makes the investigation of theoretical and inferential properties more demanding. In this talk we underline how several of these properties can be investigated through suitable distances between the laws of the random measures. Some crucial desiderata for such distances are metrization of weak convergence, numerical estimation through samples, and tractability of analytical bounds: we achieve them by relying on optimal transport and integral probability metrics. Applications of our findings include measuring dependence in Bayesian nonparametric models, defining merging rates of opinions, developing two-sample tests for measure-valued data, and quantifying the error in approximate posterior inference.
24 Aprile 2026, ore 12
Marta Catalano,
LUISS University
In person: Room V (4th floor) building CU002 Scienze Statistiche
Webinar: https://uniroma1.zoom.us/j/83625004899?pwd=bXCtz0mp759PUh2lkqT0BUoVa0Uegg.1
ID riunione: 836 2500 4899
Passcode: 123456