Ultimately exchangeable mixture models are not well suited for Bayesian inference
I had hoped this wasn’t the case, and I wait with abated breath for your updatea on the mixture model write-up.
In my case I have a large data-set and an unknown K of Gaussian sub-populations. I can see a way through if I knew K. So if I just had some way of determining K, I could incrementally solve the rest.
Where a mixture model is the right choice, is there typically an alternative that would also be appropriate?