In modelling, there are two main approaches regarding parameters.

1.     A process model can be split in: response variable, the mathematical function and the random errors.  The mathematical function involves predictor variables (the inputs) and parameters. The parameters are the values that will be assessed through the modeling process.

2.     In response functions or equations of a model we can include variables and parameters. The (input or output) variables are quantitative (or even qualitative) values which can be measured (assessed) independently. To build a relation between inputs and outputs we can use certain fixed constrains – usually constants that describe/define a property (e.g. type of material, the start or end date for the running model). These constrains represents the parameters.

INPUTS are the quantitative components of a model (numerical inputs, ranges or probability distributions). The inputs represent the initial data (variables) and the flow of data into the process. To broad the discussion we can use deterministic or random inputs …