  Clarke and Barron analysed the relative entropy between an i.i.d. source and
a Bayesian mixture over a continuous class containing that source. In this
paper a comparable result is obtained when the source is permitted to be both
non-stationary and dependent. The main theorem shows that Bayesian methods
perform well for both compression and sequence prediction even in this most
general setting with only mild technical assumptions.
