  The label switching problem arises in the Bayesian analysis of models
containing multiple indistinguishable parameters with arbitrary ordering. Any
permutation of these parameters is equivalent, therefore models with many such
parameters have extremely multi-modal posterior distributions. It is difficult
to sample efficiently from such posteriors. This paper discusses a solution to
this problem which involves carefully mapping the input parameter space to a
high dimensional hypertriangle. It is demonstrated that this solution is
efficient even for large numbers of parameters and can be easily applied
alongside any stochastic sampling algorithm. This method is illustrated using
two example problems from the field of gravitational wave astronomy.
