Hello everyone!

I have recently read about mixture models, especially the cases in which we consider the number of components, *k*, as a random variables with posterior distribution.

I have read in different articles that the sampling of such posterior is pretty complicated, partly because the posterior’s dimensionality is changing with *k*.

Some authors proposed other sampling stategies, like reversible jumps Monte-Carlo, or the Fu and Wang’s algorithm, exploring the distribution of parameters conditional on *K* (2002,2007). Unfortunatly, I do not understand enough the different algorithms to recognize their similarities or dissimilarities with the ones implemented with STAN.

So I will ask the question very directly : is stan able to deal with those kind of problems, i.e. exploring a posterior distribution of changing dimension, given a random integer variable *k*?

Thank you very much!

Best regards,

Lucas