  We present several applications of the bias-variance decomposition, beginning
with straightforward Monte Carlo estimation of integrals, but progressing to
the more complex problem of Monte Carlo Optimization (MCO), which involves
finding a set of parameters that optimize a parameterized integral. We present
the similarity of this application to that of Parametric Learning (PL).
Algorithms in this field use a particular interpretation of the bias-variance
trade to improve performance. This interpretation also applies to MCO, and
should therefore improve performance. We verify that this is indeed the case
for a particular MCO problem related to adaptive importance sampling.
