  Algorithmic statistics considers the following problem: given a binary string
$x$ (e.g., some experimental data), find a "good" explanation of this data. It
uses algorithmic information theory to define formally what is a good
explanation. In this paper we extend this framework in two directions.
  First, the explanations are not only interesting in themselves but also used
for prediction: we want to know what kind of data we may reasonably expect in
similar situations (repeating the same experiment). We show that some kind of
hierarchy can be constructed both in terms of algorithmic statistics and using
the notion of a priori probability, and these two approaches turn out to be
equivalent.
  Second, a more realistic approach that goes back to machine learning theory,
assumes that we have not a single data string $x$ but some set of "positive
examples" $x_1,\ldots,x_l$ that all belong to some unknown set $A$, a property
that we want to learn. We want this set $A$ to contain all positive examples
and to be as small and simple as possible. We show how algorithmic statistic
can be extended to cover this situation.
