Bayesian semiparametric modelling of phase-varying point processes

Bastian Galasso, Yoav Zemel, Miguel de Carvalho

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

We propose a Bayesian semiparametric approach for registration of multiple point processes. Our approach entails modelling the mean measures of the phase-varying point processes with a Bernstein-Dirichlet prior, which induces a prior on the space of all warp functions. Theoretical results on the support of the induced priors are derived, and posterior consistency is obtained under mild conditions. Numerical experiments suggest a good performance of the proposed methods, and a climatology real-data example is used to showcase how the method can be employed in practice.
Original languageEnglish
Number of pages31
JournalElectronic Journal of Statistics
Publication statusAccepted/In press - 27 Dec 2021

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