In simple cases, all but one of these quantities is a nuisance parameter.

In many applications, the statistician is most concerned with a "parameter of interest" rather than with "nuisance parameters".

Doing this only makes sense if the dependency structure is a nuisance parameter with respect to the goals of the analysis.

For maximum likelihood estimations, a model may have a number of nuisance parameters.

Note that the distribution of and its observed value are both free of nuisance parameters.

In general, any parameter which intrudes on the analysis of another may be considered a nuisance parameter.

The general treatment of nuisance parameters can be broadly similar between frequentist and Bayesian approaches to theoretical statistics.

The partition allows frequentist theory to develop general estimation approaches in the presence of nuisance parameters.

In some special cases, it is possible to formulate methods that circumvent the presences of nuisance parameters.

However, this approach may not always be computationally efficient if some or all of the nuisance parameters can be eliminated on a theoretical basis.