def predict_quantiles(self, X, quantiles=(<NUM_LIT>, <NUM_LIT>), Y_metadata=None, kern=None, likelihood=None):
m, v = self._raw_predict(X,  full_cov=False, kern=kern)<EOL>if likelihood is None:<EOL><INDENT>likelihood = self.likelihood<EOL><DEDENT>quantiles = likelihood.predictive_quantiles(m, v, quantiles, Y_metadata=Y_metadata)<EOL>if self.normalizer is not None:<EOL><INDENT>quantiles = [self.normalizer.inverse_mean(q) for q in quantiles]<EOL><DEDENT>return quantiles<EOL>
Get the predictive quantiles around the prediction at X

:param X: The points at which to make a prediction
:type X: np.ndarray (Xnew x self.input_dim)
:param quantiles: tuple of quantiles, default is (2.5, 97.5) which is the 95% interval
:type quantiles: tuple
:param kern: optional kernel to use for prediction
:type predict_kw: dict
:returns: list of quantiles for each X and predictive quantiles for interval combination
:rtype: [np.ndarray (Xnew x self.output_dim), np.ndarray (Xnew x self.output_dim)]