John M. Drake & Pej Rohani

Model calibration requires *tuning* the parameters to “best” represent reality

Evaluation of this representation is by

- goodness-of-fit
- generalizability

Model calibration is *performed* by adjusting the parameters to minimize some *objective function* (aka *loss function*)

For instance, *least squares estimation* minimizes the *sum of squared errors*

\[ SSE = \sum_{i=1}^n (y_i - f(x_i))^2 \]

where \( y_i \) is the \( i^{th} \) observation, \( x_i \) are the inputs to the model, and \( f() \) is the model.

The “adjustment” of parameters is typically performed by numerical *optimization*