  Bayesian inference of gravitational wave signals is subject to systematic
error due to modelling uncertainty in waveform signal models, coined
approximants. A growing collection of approximants are available which use
different approaches and make different assumptions to ease the process of
model development. We provide a method to marginalize over the uncertainty in a
set of waveform approximants by constructing a mixture-model multi-waveform
likelihood. This method fits into existing workflows by determining the mixture
parameters from the per-waveform evidences, enabling the production of
marginalized combined sample sets from independent runs.
