  The joint likelihood is a simple extension of the standard likelihood
formalism that enables the estimation of common parameters across disjoint
datasets. Joining the likelihood, rather than the data itself, means nuisance
parameters can be dealt with independently. Application of this technique,
particularly to Fermi-LAT dwarf spheroidal analyses, has already been met with
great success. We present a description of the method's general implementation
along with a toy Monte-Carlo study of its properties and limitations.
