  Many imaging systems are used to estimate a vector of parameters associated
with the object being imaged. In many cases there are other parameters in the
model for the imaging data that are not of interest for the task at hand. We
refer to these as nuisance parameters and use them to form the components of
the nuisance parameter vector. If we have a prior probability distribution
function (PDF) for the nuisance parameter vector, then we may mariginalize over
the nuisance parameters to produce a conditional PDF for the data that only
depends on the parameters of interest. We will examine this approach to develop
inequalities and approximations for the FIM when the data is affected by
nuisance parameters.
