Compute the expectation:
    expectation[n] = <x_n K_{x_n, Z}>_p(x_n)
        - K_{.,} :: Linear kernel
    or the equivalent for MarkovGaussian

    :return: NxDxM

def _expectation(p, mean, none, kern, feat, nghp=None):
    """
    Compute the expectation:
    expectation[n] = <x_n K_{x_n, Z}>_p(x_n)
        - K_{.,} :: Linear kernel
    or the equivalent for MarkovGaussian

    :return: NxDxM
    """
    return tf.matrix_transpose(expectation(p, (kern, feat), mean))