sensitivity.sensitivity_sampling

sensitivity.sensitivity_sampling

Sampling-based sensitivity and uncertainty analysis.

This module implements a sampling-based sensitivity analysis in which model parameters are varied simultaneously according to predefined bounds and probability assumptions, and the resulting distribution of model outputs is analyzed statistically.

The primary purpose of this approach is to quantify output uncertainty and variability induced by parameter uncertainty, rather than to compute variance-decomposition sensitivity indices. It is therefore complementary to local (derivative-based) and global (variance-based) sensitivity analyses.

Parameter samples are generated using Latin Hypercube Sampling (LHS), a stratified Monte Carlo method that ensures efficient coverage of the multidimensional parameter space. In the current implementation, parameters are sampled independently assuming uniform distributions within their bounds.

For each analysis group and output variable, descriptive statistics are computed from the simulated sample ensemble, including: - mean and median - standard deviation and coefficient of variation - minimum and maximum - selected quantiles (5% and 95%)

These statistics provide a compact summary of output uncertainty and enable comparisons across model outputs and experimental or physiological conditions.

The module is intended for: - uncertainty propagation analyses - robustness and variability assessments - exploratory model analysis and screening - reporting uncertainty ranges in computational modeling studies

It integrates with the sbmlsim sensitivity framework and supports result caching, tabular export, and visualization of output distributions.

Notes

This method does not compute sensitivity indices in the strict variance-based sense (e.g., Sobol indices). Instead, it characterizes how uncertainty in parameters propagates to uncertainty in model outputs via sampling.

Typical workflows combine this approach with local or global sensitivity analysis to obtain both quantitative sensitivity measures and uncertainty estimates.

Classes

Name	Description
SamplingSensitivityAnalysis	Sensitivity/uncertainty analysis based on sampling.

SamplingSensitivityAnalysis

sensitivity.sensitivity_sampling.SamplingSensitivityAnalysis(
    sensitivity_simulation,
    parameters,
    groups,
    results_path,
    N,
    seed=None,
    n_cores=None,
    cache_results=False,
)

Sensitivity/uncertainty analysis based on sampling.

FIXME: more control on sampling cv: float = 0.1, distribution: DistributionType = DistributionType.NORMAL_DISTRIBUTION,

Methods

Name	Description
calculate_sensitivity	Calculate the sensitivity matrices for sampling sensitivity.
create_samples	Create LHS samples.
plot	Boxplots for the Sampling sensitivity.
plot_data	Boxplots for the sampled output.

calculate_sensitivity

sensitivity.sensitivity_sampling.SamplingSensitivityAnalysis.calculate_sensitivity(
    cache_filename=None,
    cache=False,
)

Calculate the sensitivity matrices for sampling sensitivity.

create_samples

sensitivity.sensitivity_sampling.SamplingSensitivityAnalysis.create_samples()

Create LHS samples.

Latin hypercube sampling (LHS) is a stratified sampling method used to generate near‑random samples from a multidimensional distribution for Monte Carlo simulations and computer experiments.

Assuming uniform distributions within the provided bounds.

Use LHS sampling of parameters.

plot

sensitivity.sensitivity_sampling.SamplingSensitivityAnalysis.plot(**kwargs)

Boxplots for the Sampling sensitivity.

plot_data

sensitivity.sensitivity_sampling.SamplingSensitivityAnalysis.plot_data(
    type,
    show_jitter=True,
    show_violin=True,
    **kwargs,
)

Boxplots for the sampled output.