This model does however still have a number of unknown free parameters which can be estimated using system identification.

An estimator attempts to approximate the unknown parameters using the measurements.

It considers an inaccurate model but without any unknown parameter.

It considers a model that fully describes the underlying physics but with one or more unknown parameters.

Apart from the current available data, a prior distribution of unknown parameters should be assigned.

Larger sample sizes generally lead to increased precision when estimating unknown parameters.

Note that under either hypothesis, the distribution of the data is fully specified; there are no unknown parameters to estimate.

Risk functions are chosen depending on how one measures the distance between the estimate and the unknown parameter.

Assume that the 's have a common prior which depends on unknown parameters.

Let θ be a vector consisting of n 3 unknown parameters.