A generalization of Gy's theory for the variance of the fundamental sampling
error is reviewed. Practical situations where the generalized model potentially
leads to more accurate variance estimates are identified as: clustering of
particles, differences in densities or sizes of the particles or repulsive
inter-particle forces. Two general approaches for estimating an input parameter
for the generalized model are discussed. The first approach consists of
modelling based on physical properties of particles such as size, density and
electrostatic forces between particles.