# Delta Moment-Independent Method

GlobalSensitivity.DeltaMomentType
DeltaMoment(; nboot = 500, conf_level = 0.95, Ygrid_length = 2048,
num_classes = nothing)
• nboot: number of bootstrap repetions. Defaults to 500.
• conf_level: the level used for confidence interval calculation with bootstrap. Default value of 0.95.
• Ygrid_length: number of quadrature points to consider when performing the kernel density estimation and the integration steps. Should be a power of 2 for efficient FFT in kernel density estimates. Defaults to 2048.
• num_classes: Determine how many classes to split each factor into to when generating distributions of model output conditioned on class.

Method Details

The Delta moment-independent method relies on new estimators for density-based statistics. It allows for the estimation of both distribution-based sensitivity measures and of sensitivity measures that look at contributions to a specific moment. One of the primary advantage of this method is the independence of computation cost from the number of parameters.

Note

DeltaMoment only works for scalar output.

API

gsa(f, method::DeltaMoment, p_range; samples, batch = false,
rng::AbstractRNG = Random.default_rng())
gsa(X, Y, method::DeltaMoment; rng::AbstractRNG = Random.default_rng())

Example

using GlobalSensitivity, Test

function ishi(X)
A= 7
B= 0.1
sin(X) + A*sin(X)^2+ B*X^4 *sin(X)
end

lb = -ones(3)*π
ub = ones(3)*π

m = gsa(ishi,DeltaMoment(),fill([lb, ub], 3), samples=1000)

samples = 1000
X = QuasiMonteCarlo.sample(samples, lb, ub, QuasiMonteCarlo.SobolSample())
Y = ishi.(@view X[:, i] for i in 1:samples)

m = gsa(X, Y, DeltaMoment())
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