  A method to approximate continuous multi-dimensional probability density
functions (PDFs) using their projections and correlations is described. The
method is particularly useful for event classification when estimates of
systematic uncertainties are required and for the application of an unbinned
maximum likelihood analysis when an analytic model is not available. A simple
goodness of fit test of the approximation can be used, and simulated event
samples that follow the approximate PDFs can be efficiently generated. The
source code for a FORTRAN-77 implementation of this method is available.
