  Hawkes Processes are a type of point process for modeling self-excitation,
i.e., when the occurrence of an event makes future events more likely to occur.
The corresponding self-triggering function of this type of process may be
inferred through an Unconstrained Optimization-based method for maximization of
its corresponding Loglikelihood function. Unfortunately, the non-convexity of
this procedure, along with the ill-conditioning of the initialization of the
self- triggering function parameters, may lead to a consequent instability of
this method. Here, we introduce Renormalization Factors, over four types of
parametric kernels, as a solution to this instability. These factors are
derived for each of the self-triggering function parameters, and also for more
than one parameter considered jointly. Experimental results show that the
Maximum Likelihood Estimation method shows improved performance with
Renormalization Factors over sets of sequences of several different lengths.
