Agriculture Reference
In-Depth Information
Statistical Models
Statistical models include issues such as statistical characterization of numerical
data, estimating the probabilistic future behavior of a system based on past behavior,
extrapolation or interpolation of data based on some best-fit, error estimates of
observations, or spectral analysis of data or model generated output. Statistical mod-
els are useful in helping identify patterns and underlying relationships between data
sets.
10.2.2.5 Static and Dynamic Model
Static Model
A model that analyzes variables in the system in a single point in time. Time is not
a variable.
Dynamic Model
A model (or system) that contains time as one of the variables. It captures important
changes in, and inter-relationships between parameters and variables through time;
i.e., output is projected as a function of time.
10.2.2.6 Mechanistic and Probabilistic Model
Mechanistic Model
When mathematical relationships based on physical, chemical, and/or biological
principles (whatever may applicable) can be combined to represent a cause-effect
process, the relationships can be referred to as a mechanistic model.
Mechanistic model is based on physical concepts and laws. Process-based math-
ematical model, which integrates the different processes (mechanisms) involve, is
termed as mechanistic model. Mechanistic representations allow descriptions of
why events happen to be integral parts of a mathematical model. Mechanistic repre-
sentations can be combined in logical associations to provide a simulation of all, or
portion of, large complex system.
Probabilistic Model
Model which is based on probability concepts and laws.
10.2.2.7 Deterministic and Stochastic Model
Deterministic Model
A model where all variables and parameters are known (non-random). In general, a
deterministic model comprises of two components:
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