  Kernel Bayes' rule has been proposed as a nonparametric kernel-based method
to realize Bayesian inference in reproducing kernel Hilbert spaces. However, we
demonstrate both theoretically and experimentally that the prediction result by
kernel Bayes' rule is in some cases unnatural. We consider that this phenomenon
is in part due to the fact that the assumptions in kernel Bayes' rule do not
hold in general.
