Search results
Variance-based sensitivity analysis (often referred to as the Sobol’ method or Sobol’ indices, after Ilya M. Sobol’) is a form of global sensitivity analysis. Working within a probabilistic framework, it decomposes the variance of the output of the model or system into fractions which can be attributed to inputs or sets of inputs.
Sobol Indices to Measure Feature Importance. Understanding the model’s output plays a major role in business-driven projects, and Sobol can help. Valentin Catherine. Follow. Published in. Towards Data Science. 7 min read. Jun 20, 2022. 1. Listen. Share. Photo by Martin Sanchezon Unsplash. Introduction.
Sensitivity in high dimension . Learn how to use sparse PCE to calculate higher-order Sobol' indices with 100 input parameters. Learn how to obtain the Sobol' indices using either the sampling-based or the PCE/LRA-based methods.
1 de sept. de 2021 · This paper proposes an Innovative Algorithm (named IA estimator) to estimate the Sobol’ main effect indices, based on dynamic adaptive variances. The new estimator is tested on a broad set of test functions.
Sobol indices are the standard approach for performing a global sensitivity analysis. The indices are based on a variance decomposition of the model output. Using this decomposition allows us to assign the contribution of uncertain inputs to the variance of the model output.
Several different sensitivity measures exist, but Uncertainpy uses the commonly used Sobol sensitivity indices (Sobol, 1990). The Sobol sensitivity indices quantify how much of the variance in the model output each uncertain parameter is responsible for.
26 de feb. de 2015 · The Sobol sensitivity indices have several features, which can be categorized as following: The total-/first-/second-order sensitivity indices are positive values. Parameters with sensitivity indices greater than 0.05 are considered significant.
