parametersE

ParametersE is a term used in computational modeling to denote a designated collection of external or environmental parameters that influence a model’s behavior. The name reflects a division sometimes made between internal model parameters and external inputs. In practice, parametersE can include quantities that are not estimated from the model data within a given run but are provided to drive scenarios or reflect ambient conditions.

Formal description: Let E = {e1, ..., en} be a set of external parameters, each with a type, unit, and valid range. They may be constants or time-dependent functions ei(t). In many modeling frameworks, parametersE are treated as inputs rather than free parameters to be inferred; however, some approaches allow Bayesian or optimization-based calibration of E alongside internal parameters.

Applications: In climate or ecological models, parametersE can include temperature, rainfall, humidity, and solar radiation; in economics or social simulations, policy levers or external shocks. They enable scenario analysis, sensitivity studies, and stress-testing by varying external conditions while keeping internal structure fixed.

Estimation and calibration: Techniques include sensitivity analysis, Monte Carlo sampling, and Bayesian updating to assess how changes in the external parameters affect outputs. Recognizing identifiability issues is important when external inputs are correlated with model structure or with each other.

See also: Exogenous variables, external inputs, parameter sensitivity.