diff --git a/docs/examples/emulator/robustgasp/plot_scalargasp.py b/docs/examples/emulator/robustgasp/plot_scalargasp.py index e4143bc1a667a01aa47d4038aec3252183843bd8..710b163905f8e3558e262c2969267a96c9c84051 100644 --- a/docs/examples/emulator/robustgasp/plot_scalargasp.py +++ b/docs/examples/emulator/robustgasp/plot_scalargasp.py @@ -104,4 +104,15 @@ plt.fill_between(testing_input, predictions[:, 1], predictions[:, 2], alpha=0.3, plt.xlabel('x') plt.ylabel('emulator-predicted y') plt.xlim(testing_input[0], testing_input[-1]) -_ = plt.legend() \ No newline at end of file +_ = plt.legend() + + +# %% md +# +# Above example shows how to train a GP emulator based on noise-free training data, +# which is often the case of emulating a deterministic simulator. If you are dealing +# with noisy training data, you can +# +# - set the parameter ``nugget`` to a desired value, or +# - set ``nugget`` to :math:`0` and ``nugget_est`` to `True`, meaning that ``nugget`` +# is estimated from the noisy training data. \ No newline at end of file