  Probability Density Estimation (PDE) is a multivariate discrimination
technique based on sampling signal and background densities defined by event
samples from data or Monte-Carlo (MC) simulations in a multi-dimensional phase
space. In this paper, we present a modification of the PDE method that uses a
self-adapting binning method to divide the multi-dimensional phase space in a
finite number of hyper-rectangles (cells). The binning algorithm adjusts the
size and position of a predefined number of cells inside the multi-dimensional
phase space, minimising the variance of the signal and background densities
inside the cells. The implementation of the binning algorithm PDE-Foam is based
on the MC event-generation package Foam. We present performance results for
representative examples (toy models) and discuss the dependence of the obtained
results on the choice of parameters. The new PDE-Foam shows improved
classification capability for small training samples and reduced classification
time compared to the original PDE method based on range searching.
