Subsampling Parameter Ensembles

While the parametric_uq function returns a VirtualPopulation ensemble of parameter values which correspond to relatively good fits to the data, in many cases this return is referred to as a "plausible population", i.e. a set of potentially good parameters which may or may not reflect the statistical effects of the population.

For example, say that for the data series that is being fit, 50% of the population is known to be fast matabolizers and the other 50% is comprised of slow matabolizers (characterized by some parameter or measurement in the model). A virtual population method, vpop, will return a plausible population of N plausible parameters but there is no guarantee that it captured the distribution of fast and slow metabolizers from the actual population. The purpose of a subsample algorithm is to downsample from N to M to find a subpopulation which captures important statistical quantities better than the plausible population.

The subsample Function

function subsample(alg::AbstractSubsamplingAlgorithm, ps::AbstractParametricUncertaintyEnsemble,
                experiment::AbstractExperiment; kwargs)

Accessing the Sampler

Each subsampling algorithm internally uses a Sampler object to perform subsampling on a plausible population vp. One can call the subsample method multiple times to rerun the subsampling process and produce a different population each time. However, this might be computationally expensive, as methods like ARM need to solve an optimization problem before producing a subsampled population.

Users can access the internal Sampler object directly and call it multiple times to generate different populations. The Sampler is initialized with all the necessary information that the respective subsampling algorithm needs. Thus, by using this object, one can avoid the computational cost of running the entire subsample method multiple times, if multiple subsampled populations are required.

function get_sampler(alg::AbstractSubsamplingAlgorithm, ps::AbstractParametricUncertaintyEnsemble,
    experiment::AbstractExperiment; kwargs)

ps = parametric_uq(prob, alg; population_size = 100)
subsample_alg = WeightSampler()
sampler = get_sampler(subsample_alg, vp, experiment)
ps_subsampled = vp[sampler()]