Variance-based methods are statistical techniques used to analyze the sensitivity of a model’s output with respect to its input parameters by examining how variations in input values affect the variance of the output. These methods help identify which parameters have the most significant impact on the uncertainty in the model results, providing insights into model behavior and reliability. By quantifying the influence of each input variable, variance-based methods facilitate better decision-making and more efficient resource allocation in complex systems.
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