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Applying resampling schemes

Resampling with schemes

For some uncertain collections and datasets, special resampling types are available to make resampling easier.

Constrained resampling

# UncertainData.Resampling.resample — Method.

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resample(x::AbstractUncertainValueDataset, resampling::ConstrainedValueResampling)

Resample x by first constraining the supports of the distributions/populations furnishing the uncertain values, then drawing samples from the limited supports.

Sampling is done without assuming any sequential dependence between the elements of x, such no that no dependence is introduced in the draws beyond what is potentially already present in the collection of values.

Example

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# Some example data 
N = 50
x_uncertain = [UncertainValue(Normal, x, rand(Uniform(0.1, 0.8))) for x in rand(N)]
y_uncertain = [UncertainValue(Normal, y, rand(Uniform(0.1, 0.8))) for y in rand(N)]
x = UncertainValueDataset(x_uncertain)
y = UncertainValueDataset(y_uncertain)

# Resample with different constraints
resample(x, ConstrainedValueResampling(TruncateStd(1.5))
resample(y, ConstrainedValueResampling(TruncateStd(0.5))
resample(y, ConstrainedValueResampling(TruncateQuantiles(0.2, 0.8))

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