  Learning joint probability distributions on n random variables requires
exponential sample size in the generic case. Here we consider the case that a
temporal (or causal) order of the variables is known and that the (unknown)
graph of causal dependencies has bounded in-degree Delta. Then the joint
measure is uniquely determined by the probabilities of all (2 Delta+1)-tuples.
Upper bounds on the sample size required for estimating their probabilities can
be given in terms of the VC-dimension of the set of corresponding cylinder
sets. The sample size grows less than linearly with n.
