The experimental results obtained in Section 5 look impressive and confirm the
relevance of using a Bayesian approach for the Network Inference problem.
However, we believe that many other aspects of Bayesian Inference could and
should be exploited in the context of Network Inference. We wish to explore
this in future work and only highlight a few possible directions here:
\begin{itemize}
    \item obtain formal guarantees on the convergence of measure of the
    Bayesian posterior. Similarly to convergence rate results obtained with MLE
    estimation, we believe that convergence results could also be obtained in
    the Bayesian setting, at least in restricted settings or by making certain
    assumptions about the network being learned.
    \item strengthening the experimental results by systematically studying how
    different network properties impact the speedup induced by active learning.
    \item finish formally deriving the update equations when using Bohning
    approximations for Variational Inference.
    \item extend the combined Variational Inference and Bohning approximation
    to Hawkes processes to obtain a unified Bayesian framework for both
    discrete-time and continuous-time models.
    \item explore the impact of using more expressive (in particular
    non-factorized) on the speed of convergence, both in offline and active
    online learning.
\end{itemize}
