(self, n_samples)
return p0[:, np.newaxis]
Returns N samples from the prior.

        Parameters
        ----------
        n_samples : int
            The number of samples that will be drawn.

        Returns
        -------
        (N, D) np.array
            The samples from the prior.
Returns N samples from the prior.
Returns N samples from the prior .
def sample_from_prior(self, n_samples):
        """
        Returns N samples from the prior.

        Parameters
        ----------
        n_samples : int
            The number of samples that will be drawn.

        Returns
        -------
        (N, D) np.array
            The samples from the prior.
        """

        lamda = np.abs(self.rng.standard_cauchy(size=n_samples))

        p0 = np.log(np.abs(self.rng.randn() * lamda * self.scale))
        return p0[:, np.newaxis]
def sample_from_prior ( self , n_samples ) : lamda = np . abs ( self . rng . standard_cauchy ( size = n_samples ) ) p0 = np . log ( np . abs ( self . rng . randn ( ) * lamda * self . scale ) ) return p0 [ : , np . newaxis ]