# Resources

Resources
covered subject areas

Expand your knowledge through our resources. Try our online tools and start reading our articles about probabilistic modeling, machine learning, risk assessment, uncertainty quantification and other fundamental topics. Here are the resources you need to get started dealing with uncertainties that arise in your day-to-day projects.

Sensitivity analysis

## What is variance-based sensitivity analysis?

Variance-based sensitivity analysis is a form of global sensitivity analysis based on a decomposition of the variance of the model output.

Reliability analysis

## What is the failure domain in a reliability analysis?

The failure domain consists of all samples for which the limit-state function becomes smaller or equal than zero.

Sensitivity analysis

## What is factor fixing (in sensitivity analysis)?

In factor fixing, the model parameters that have the least influence on the model output are identified – often with the purpose of simplifying the model.

Sensitivity analysis

## What are the total-effect sensitivity indices?

A total-effect sensitivity index quantifies the effect of the associated model parameter and all its interactions with other parameters on the model output. The total-effect sensitivity indices are well-suited for factor fixing.

Sensitivity analysis

## How to evaluate the first-order sensitivity indices numerically?

Based on samples from a Monte Carlo simulation, the first-order sensitivity indices can be estimated without additional model calls. This requires the numerical approximation of a one-dimensional conditional expectation.

Sensitivity analysis

## What is factor prioritization (in sensitivity analysis)?

In factor prioritization, the input parameters are ranked according to their importance on the model output. The first-order sensitivity indices are a commonly applied sensitivity measure for that purpose.

Sensitivity analysis

## What is an additive model?

For additive models, the variance of the model output can be decomposed as the sum of the first-order effects of the model input.

Sensitivity analysis

## What are the first-order sensitivity indices?

A first-order sensitivity index quantifies the effect of varying the associated model input parameter alone. The first-order sensitivity indices are well-suited for factor prioritization.

Sensitivity analysis

## How can we decompose the variance of the model output?

We can decompose the variance of the model output in terms of a series expansion of combined effects if the model input parameters are statistically independent.