def from_moments_iso(mu, sigma_sq, Z=0.):
dim = mu.shape[-1]
return Distribution(mu, psd_matrices.EyeMatrix(sigma_sq, dim), Z)
def conditional_for(self, i):
Lambda = psd_matrices.EyeMatrix(self._Lambda.elt(i, i), 1)
return Potential(self._J_diff[..., i:i + 1].copy(), Lambda,
self._Z_diff).translate(self._X[..., i:i + 1])
def from_moments_iso(mu, sigma_sq):
sigma_sq = np.asarray(sigma_sq)
return Distribution(mu, psd_matrices.EyeMatrix(
sigma_sq, mu.shape[-1])).to_potential()