def log_prior(self, ks=None):
        lp = 0
        for k in ks:
            # lp += (self.alpha * np.log(self.weights[k,1:])).sum()
            # lp += (-self.beta * self.weights[k,1:]).sum()
            if self.alpha > 1:
                lp += (self.alpha -1) * np.log(self.weights[k,1:]).sum()
            lp += (-self.beta * self.weights[k,1:]).sum()
        return lp
Compute the log prior probability of log W
dataset/ETHPy150Open slinderman/pyhawkes/pyhawkes/models.py/DiscreteTimeStandardHawkesModel.log_prior