This paper consists of an overview on universal prediction from an inf
ormation-theoretic perspective. Special attention is given to the noti
on of probability assignment under the self-information loss function,
which is directly related to the theory of universal data compression
. Both the probabilistic setting and the deterministic setting of the
universal prediction problem are described with emphasis on the analog
and the differences between results in the two settings.