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 wit
h 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 methods general implementation along with a toy Monte-Carlo study of its properties and limitations.
