def _d_log_prior_d_W(self, k):
        """
        Use a gamma prior on W (it is log concave for alpha >= 1)

        and
        LN p(W)       = (\alpha-1)LN W - \beta W
        d/dW LN p(W)) = (\alpha -1)/W  - \beta
        """
        d_log_prior_d_W = np.zeros_like(self.weights[k,:])
        if self.alpha > 1.0:
            d_log_prior_d_W[1:] += (self.alpha-1) * self.weights[k,1:]

        d_log_prior_d_W[1:] += -self.beta
        return d_log_prior_d_W
dataset/ETHPy150Open slinderman/pyhawkes/pyhawkes/models.py/DiscreteTimeStandardHawkesModel._d_log_prior_d_W/operator `/` -> `*`