(self, theta)
return lp
Returns the log probability of theta. Note: theta should
        be on a log scale.

        Parameters
        ----------
        theta : (D,) numpy array
            A hyperparameter configuration in log space.

        Returns
        -------
        float
            The log probability of theta
Returns the log probability of theta. Note: theta should
        be on a log scale.
Returns the log probability of theta . Note : theta should be on a log scale .
def lnprob(self, theta):
        """
        Returns the log probability of theta. Note: theta should
        be on a log scale.

        Parameters
        ----------
        theta : (D,) numpy array
            A hyperparameter configuration in log space.

        Returns
        -------
        float
            The log probability of theta
        """
        lp = 0
        lp += self.ln_prior_alpha.lnprob(theta[0])
        lp += self.horseshoe.lnprob(1 / theta[-1])

        return lp
def lnprob ( self , theta ) : lp = 0 lp += self . ln_prior_alpha . lnprob ( theta [ 0 ] ) lp += self . horseshoe . lnprob ( 1 / theta [ - 1 ] ) return lp