Quickstart

Sobol’ indices for the Ishigami function in five minutes.

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The Ishigami function is the standard test problem for sensitivity analysis methods. It has three inputs, nonlinear interactions, and closed-form Sobol’ indices — so you know immediately whether your estimates are right.

The model

The Ishigami function maps three independent uniform inputs x_i \sim \mathcal{U}(-\pi, \pi) to a scalar:

f(\mathbf{x}) = \sin(x_1) + 7\sin^2(x_2) + 0.1\, x_3^4 \sin(x_1)

The x_3^4 \sin(x_1) interaction term makes this a hard test: x_3 contributes nothing to first-order variance but has a large total-effect index through its interaction with x_1.

Closed-form indices

With a = 7 and b = 0.1, the analytic Sobol’ indices are:

Factor	S_i (first-order)	S_{Ti} (total-effect)
x_1	0.3139	0.5576
x_2	0.4424	0.4424
x_3	0.0000	0.2437

x_3 has zero first-order effect but 24% total effect — all of it through the x_1 x_3 interaction. Any estimator that handles this correctly is working.

Dependencies

[dependencies]
salib = "0.1"

This pulls in salib-core, salib-samplers, and salib-estimators by default.

Code

use std::f64::consts::PI;
use salib::*;
use salib::samplers::{SobolSampler, build_saltelli_matrix};
use salib::estimators::estimate_saltelli2010;

fn main() {
    // 1. Define the problem: three uniform factors on [-π, π].
    let problem = ProblemBuilder::new()
        .factor("x1", Distribution::Uniform { lo: -PI, hi: PI })
        .factor("x2", Distribution::Uniform { lo: -PI, hi: PI })
        .factor("x3", Distribution::Uniform { lo: -PI, hi: PI })
        .build()
        .unwrap();

    // 2. Generate quasi-random samples.
    //    Sobol' sequence, 8192 base samples, Saltelli cross-matrix design.
    let mut rng = RngState::from_seed([0u8; 32]);
    let sampler = SobolSampler::minimal(2 * problem.dim());
    let saltelli = build_saltelli_matrix(
        &sampler, 8192, false, &mut rng
    ).unwrap();

    // 3. Evaluate the model and estimate indices.
    let indices = estimate_saltelli2010(&saltelli, |x| {
        x[0].sin()
            + 7.0 * x[1].sin().powi(2)
            + 0.1 * x[2].powi(4) * x[0].sin()
    });

    println!("{indices}");
}

Output

Sobol' Indices (Saltelli 2010)
──────────────────────────────────────────
Factor          S1        ST
x1          0.3143    0.5559
x2          0.4427    0.4420
x3         -0.0042    0.2455

Verify: Compare against the closed-form values above. S_1 estimates are within 0.005 of the analytic values at N = 8192. S_T estimates are within 0.002. S_1 for x_3 is slightly negative — expected Monte Carlo noise around the true value of zero.

What just happened

Three things, each handled by a different crate.

Sampling (salib-samplers): A Sobol’ quasi-random sequence generated N = 8192 base samples in the unit hypercube, then the Saltelli cross-matrix design produced the A, B, and A_B^{(i)} matrices needed for the variance decomposition — (d + 2) \times N = 40{,}960 model evaluations total.

Evaluation: The closure ran the Ishigami function on each row of the combined matrix. salib does not own your model — any Fn(&[f64]) -> f64 works.

Estimation (salib-estimators): The Saltelli 2010 estimator computed first-order indices S_i and total-effect indices S_{Ti} from the A, B, A_B^{(i)} output vectors using the formulas in Saltelli et al. (2010).

Next

• Choosing a method — when to use Morris screening vs. Sobol’ indices vs. distribution-based measures.
• Variance-based methods — the full theory behind the Saltelli, Jansen, Janon, and Owen estimators.
• Internals — how salib guarantees identical results regardless of thread count.