  In this paper we present a new approach to control variates for improving
computational efficiency of Ensemble Monte Carlo. We present the approach using
simulation of paths of a time-dependent nonlinear stochastic equation. The core
idea is to extract information at one or more nominal model parameters and use
this information to gain estimation efficiency at neighboring parameters. This
idea is the basis of a general strategy, called DataBase Monte Carlo (DBMC),
for improving efficiency of Monte Carlo. In this paper we describe how this
strategy can be implemented using the variance reduction technique of Control
Variates (CV). We show that, once an initial setup cost for extracting
information is incurred, this approach can lead to significant gains in
computational efficiency. The initial setup cost is justified in projects that
require a large number of estimations or in those that are to be performed
under real-time constraints.
