Fix Hoeffding bound

The standard (ε,δ)-bound for the MonteCarlo SV approximation assumes a deterministic utility, but one never has this. Instead: make an additional (ε,δ)-assumption on the utility and nest the bounds.

However, sample bounds on the utility are valid for >n samples and during SV computation, most evaluations of the utility are on small subsets of the training set. This is generally a problem when SVs. Solutions?