  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.
