**Y**et another paper on relabelling for mixtures, when one would think everything and more has already be said and written on the topic… This one appeared in Statistics and Computing last August and I only became aware of it through ResearchGate which sent me an unsolicited email that this paper quoted one of my own papers. As well as Bayesian Essentials.

The current paper by Egidi, PappadÃ , Pauli and Torelli starts from the remark that the *similarity* matrix of the probabilities for pairs of observations to be in the same component is invariant to label switching. A property we also used in our 2000 JASA paper. But here the authors assume it is possible to find *pivots*, that is, as many observations as there are components such that any pair of them is never in the same component with posterior probability one. These pivots are then used for the relabelling, as they define a preferential relabelling at each iteration. Now, this is not always possible since there are presumably iterations with empty components and there is rarely a zero probability that enough pairs never meet. The resolution of this quandary is then to remove the iterations for which this happens, a subsampling that changes the nature of the MCMC chain and may jeopardise its Markovian validation. The authors however suggest using alternative and computationally cheaper solutions to identify the pivots. (Which confuses me as to which solution they adopt.)

The next part of the paper compares this approach with seven other solutions found in the literature, from Matthew Stephens’ (2000) to our permutation reordering. Which does pretty well in terms of MSE in the simulation study (see the massive Table 3) while being much cheaper to implement than the proposed pivotal relabelling (Table 4). And which, contrary to the authors’ objection, *does not require* the precise computation of the MAP since, as indicated in our paper, the relative maximum based on the MCMC iterations can be used as a proxy. I am thus less than convinced at the improvement brought by this alternative…

This entry was posted on September 14, 2017 at 12:17 am and is filed under Statistics with tags Bayesian Essentials with R, finite mixtures, label switching, relabelling, ResearchGate. You can follow any responses to this entry through the RSS 2.0 feed.
You can leave a response, or trackback from your own site.

## Leave a Reply