érzékenységelemzések

érzékenységelemzések, commonly translated as sensitivity analysis, is a branch of research that explores how the uncertainty in the output of a mathematical model or computer simulation can be attributed to different sources of uncertainty in its inputs. In essence, it aims to understand which input parameters have the most significant impact on the model's results. This is crucial for model validation, parameter calibration, and for identifying areas where further data collection or refinement of understanding might be most beneficial.

The primary goal of sensitivity analysis is to quantify the influence of each input variable on the model's output. This can be achieved through various methods, broadly categorized into local and global approaches. Local sensitivity analysis examines the change in output resulting from small perturbations around a specific point in the input space, often employing derivatives. Global sensitivity analysis, on the other hand, considers the entire range of input variability and assesses the contribution of each input to the total output variance.

Applications of sensitivity analysis are widespread across numerous disciplines, including environmental science, engineering, economics, and medicine. For example, in climate modeling, it helps determine which factors most influence projected temperature changes. In engineering, it can identify critical design parameters that affect system performance. By understanding these sensitivities, researchers and practitioners can make more informed decisions, allocate resources effectively, and build more robust and reliable models.