  This paper studies nonparametric estimation of parameters of multivariate
Hawkes processes. We consider the Bayesian setting and derive posterior
concentration rates. First rates are derived for L1-metrics for stochastic
intensities of the Hawkes process. We then deduce rates for the L1-norm of
interactions functions of the process. Our results are exemplified by using
priors based on piecewise constant functions, with regular or random partitions
and priors based on mixtures of Betas distributions. Numerical illustrations
are then proposed with in mind applications for inferring functional
connec-tivity graphs of neurons.
