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Statistical models with double power-law behavior - François Caron
Bayesian nonparametric approaches, in particular the Pitman-Yor process and the associated two-parameter Chinese Restaurant process, have been successfully used in applications where the data exhibit a power-law behavior. Examples include natural language processing, natural images or networks. There is growing empirical evidence suggesting that some datasets exhibit a two-regime power-law behavior: one regime for small frequencies, and a second regime, with a different exponent, for high frequencies. In this talk, I will introduce a class of Bayesian nonparametric models exhibiting this double power-law behavior. I will present in particular two models within this class with interpretable parameters. Markov chain Monte Carlo algorithms are derived to estimate the parameters of these models. I will show that the proposed models provide a better fit than the Pitman-Yor process on various datasets.

This is joint work with Fadhel Ayed and Juho Lee.

Sep 16, 2020 05:00 PM in Paris

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