  The problem of extracting as much information as possible from a sequence of
observations of a stationary stochastic process $X_0,X_1,...X_n$ has been
considered by many authors from different points of view. It has long been
known through the work of D. Bailey that no universal estimator for
$\textbf{P}(X_{n+1}|X_0,X_1,...X_n)$ can be found which converges to the true
estimator almost surely. Despite this result, for restricted classes of
processes, or for sequences of estimators along stopping times, universal
estimators can be found. We present here a survey of some of the recent work
that has been done along these lines.
