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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 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.

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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.

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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.

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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.

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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.

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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.

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Sensitivity analysis

## What is derivative-based sensitivity analysis?

Derivative-based SA provides a local sensitivity measure that is commonly used to assess deterministic model parameters. By normalizing the derivatives with standard deviations, the measure can also be applied to uncertain parameters (of linear models).

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Sensitivity analysis

## Can scatter plots be used for sensitivity analysis?

Scatter plots of the input against the output are one of the simplest and most versatile tools for qualitative global sensitivity analysis.

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Reliability analysis

## What is FORM?

FORM is a structural reliability analysis method that approximates the probability of failure by linearizing the limit state function around the design point.

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Probabilistic modeling

## What is the underlying standard Normal space?

The uncertain model parameters are transformed into independent standard Normal random variables. For the performance and stability of numerical algorithms, performing such a transformation step is sometimes preferable.

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