  In this paper, we develop a general approach for probabilistic estimation and
optimization. An explicit formula and a computational approach are established
for controlling the reliability of probabilistic estimation based on a mixed
criterion of absolute and relative errors. By employing the Chernoff-Hoeffding
bound and the concept of sampling, the minimization of a probabilistic function
is transformed into an optimization problem amenable for gradient descendent
algorithms.
