Resampling Algorithms

As it is neccessary to perform resampling, both for determining how many offspring each bin should have, and, having determined that, resampling the particles within each bin, we have provided several choices here. These methods, and their properties, can be found in [4]. The provided selection! functions default to using WeightedEnsemble.systematic across bins and WeightedEnsemble.multinomial within bins.

All of these resampling methods take as their arguments, n, the number of trials, and ω, a vector of probabilities. What is returned is a vector, Nvals, the same size as ω, containing the number allocated to each of the states. These will satisfy the properties that sum(Nvals)=n, and the mean value of Nvals[i] (from repeated independent trials), is n * ω[i] for each i.

residual(n,ω)

stratified(n,ω)

systematic(n,ω)

multinomial(n, ω)