  Selection functions are vital for understanding the observational biases of
spectroscopic surveys. With the wide variety of multi-object spectrographs
currently in operation and becoming available soon, we require easily
generalisable methods for determining the selection functions of these surveys.
Previous work, however, has largely been focused on generating individual,
tailored selection functions for every data release of each survey. Moreover,
no methods for combining these selection functions to be used for joint
catalogues have been developed.
  We have developed a Poisson likelihood estimation method for calculating
selection functions in a Bayesian framework, which can be generalised to any
multi-object spectrograph. We include a robust treatment of overlapping fields
within a survey as well as selection functions for combined samples with
overlapping footprints. We also provide a method for transforming the selection
function that depends on the sky positions, colour, and apparent magnitude of a
star to one that depends on the galactic location, metallicity, mass, and age
of a star. This `intrinsic' selection function is invaluable for chemodynamical
models of the Milky Way. We demonstrate that our method is successful at
recreating synthetic spectroscopic samples selected from a mock galaxy
catalogue.
