19.3 Component Collapsing in Mixture Models
It is possible for two mixture components in a mixture model to collapse to the same values during sampling or optimization. For example, a mixture of \(K\) normals might devolve to have \(\mu_i = \mu_j\) and \(\sigma_i = \sigma_j\) for \(i \neq j\).
This will typically happen early in sampling due to initialization in MCMC or optimization or arise from random movement during MCMC. Once the parameters match for a given draw \((m)\), it can become hard to escape because there can be a trough of low-density mass between the current parameter values and the ones without collapsed components.
It may help to use a smaller step size during warmup, a stronger prior on each mixture component’s membership responsibility. A more extreme measure is to include additional mixture components to deal with the possibility that some of them may collapse.
In general, it is difficult to recover exactly the right \(K\) mixture components in a mixture model as \(K\) increases beyond one (yes, even a two-component mixture can have this problem).