**R**andal Douc, Florian Maire, and Jimmy Olsson recently arXived a paper on the use of Markov chain Monte Carlo methods for the sampling of mixture models, which contains the recourse to Carlin and Chib (1995) pseudo-priors to simulate from a mixture distribution* (and not from the posterior distribution associated with a mixture sampling model)*. As reported earlier, I was in the thesis defence of Florian Maire and this approach had already puzzled me at the time. In short, a mixture structure

gives rises to as many auxiliary variables as there are components, minus one: namely, if a simulation **z** is generated from a given component *i* of the mixture, one can create pseudo-simulations *u* from all the other components, using pseudo-priors à la Carlin and Chib. A Gibbs sampler based on this augmented state-space can then be implemented: (a) simulate a new component index **m** given (*z,u*); (b) simulate a new value of (*z,u*) given *m*. One version (MCC) of the algorithm simulates *z* given m from the proper conditional posterior by a Metropolis step, while another one (FCC) only simulate the *u*‘s. The paper shows that MCC has a smaller asymptotic variance than FCC. I however fail to understand why a Carlin and Chib is necessary in a mixture context: it seems (from the introduction) that the motivation is that a regular Gibbs sampler [simulating *z* by a Metropolis-Hastings proposal then *m*] has difficulties moving between components when those components are well-separated. This is correct but slightly moot, as each component of the mixture can be simulated separately and in advance in *z*, which leads to a natural construction of (a) the pseudo-priors used in the paper, (b) approximations to the weights of the mixture, and (c) a global mixture independent proposal, which can be used in an independent Metropolis-Hastings mixture proposal that [seems to me to] alleviate(s) the need to simulate the component index* m*. Both examples used in the paper, a toy two-component two-dimensional Gaussian mixture and another toy two-component one-dimensional Gaussian mixture observed with noise (and in absolute value), do not help in perceiving the definitive need for this Carlin and Chib version. Especially when considering the construction of the pseudo-priors.