parametervariaties

Parametervariaties refers to the variations in parameter values within a model or system and the resulting changes in model outputs. These variations can arise from natural heterogeneity in the real world, measurement error, or uncertainty about the correct model form. Studying parametervariaties helps to understand how sensitive a system is to its parameters, to assess robustness, and to support calibration and risk assessment.

Methods used to study parametervariaties include parameter sweeps, which systematically vary parameters across predefined ranges; local sensitivity analysis, which examines how small changes in parameters near a nominal point affect outputs; and global sensitivity analysis, which explores the influence of parameters across the entire plausible space, often using Monte Carlo simulations or sampling techniques such as Latin hypercube. Uncertainty quantification combines parameter variability with probabilistic models to produce distributions of possible outcomes. Parameter estimation or calibration uses data to constrain parameter values, while identifiability concerns address whether unique parameter values can be inferred from the available data.

Applications span engineering, physics, environmental and climate modelling, economics, and epidemiology. For example, in pharmacokinetic models, varying absorption and clearance rates illustrates how concentration profiles respond; in climate models, changes to a climate-sensitivity parameter influence projected warming.

Challenges include high dimensionality, correlations among parameters, nonlinearity, and computational cost. Best practices emphasize clear documentation of assumptions, reporting of sensitivity and uncertainty metrics, and robust validation of model predictions.

See also: sensitivity analysis; uncertainty quantification; model calibration; design of experiments.