label switching by optimal transport: Wasserstein to the rescue

A new arXival by Pierre Monteiller et al. on resolving label switching by optimal transport. To appear in NeurIPS 2019, next month (where I will be, but extra muros, as I have not registered for the conference). Among other things, the paper was inspired from an answer of mine on X validated, presumably a première […]

label switching in Bayesian mixture models

A referee of our paper on approximating evidence for mixture model with Jeong Eun Lee pointed out the recent paper by Carlos Rodríguez and Stephen Walker on label switching in Bayesian mixture models: deterministic relabelling strategies. Which appeared this year in JCGS and went beyond, below or above my radar. Label switching is an issue […]

My discussion on label switching

For the incoming conference on mixtures at ICMS in Edinburgh, Scotland, I have prepared a few slides as my discussion of the talks by Sylvia Früwirth-Schnatter and John Geweke on the controversial topic of label switching. I am afraid I am still in agreement with the radical breakup of our 2000 JASA paper, namely that […]

relabelling in Bayesian mixtures by pivotal units

Yet 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 […]

relabelling mixtures (#2)

Following the previous post, I went and had  a (long) look at Puolamäki and Kaski’s paper. I must acknowledge that, despite having several runs through the paper, I still have trouble with the approach… From what I understand, the authors use a Bernoulli mixture pseudo-model to reallocate the observations to components.  That is, given an […]