  Hawkes process are very popular mathematical tools for modelling phenomena
exhibiting a \textit{self-exciting} or \textit{self-correcting} behaviour.
Typical examples are earthquakes occurrence, wild-fires, drought,
capture-recapture, crime violence, trade exchange, and social network activity.
The widespread use of Hawkes process in different fields calls for fast,
reproducible, reliable, easy-to-code techniques to implement such models. We
offer a technique to perform approximate Bayesian inference of Hawkes process
parameters based on the use of the R-package \inlabru. The \inlabru R-package,
in turn, relies on the INLA methodology to approximate the posterior of the
parameters. Our Hawkes process approximation is based on a decomposition of the
log-likelihood in three parts, which are linearly approximated separately. The
linear approximation is performed with respect to the mode of the parameters'
posterior distribution, which is determined with an iterative gradient-based
method. The approximation of the posterior parameters is therefore
deterministic, ensuring full reproducibility of the results. The proposed
technique only requires the user to provide the functions to calculate the
different parts of the decomposed likelihood, which are internally linearly
approximated by the R-package \inlabru. We provide a comparison with the
\bayesianETAS R-package which is based on an MCMC method. The two techniques
provide similar results but our approach requires two to ten times less
computational time to converge, depending on the amount of data.
