**R**eza Seirafi from Virginia Tech sent me the following email about **Bayesian Core**, which alas is pointing out a real typo in the reversible jump acceptance probability for the mixture model:

With respect to the expression provided on page 178 for the acceptance probability of the split move, I was wondering if the omission of the density of the auxiliary parameters u_{1}, u_{2}, and u_{3} — specially u_{2}, since its density is not necessarily equal to 1 contrary to the other two — is a case of typo.

**T**his is truly a typo, the acceptance probability at the bottom of page 178 should be

since *u*_{2} is indeed distributed from a non-uniform density. The physical reason for this (unacceptable!) typo is that I cut-and-pasted the LaTeX code from **Monte Carlo Statistical Methods**: (page 439) where *u*_{2} is a uniform variate! (Incidentally, there is a minor typo a few lines above: when defining

,

it should be

,

another side casualty of cut-and-paste!) Obviously, this could also affect the associated R code but I checked it and found the line

jacob=(kprop-2)*log(1-propp[kprop]) - singleprior(propmix,kprop)

which I think is correcting for the normal proposal.

**T**his typo will obviously be corrected in the next printing of** Bayesian Core** as well as in the new edition we plan to write in July. It also illustrates the folk theorem “*One can never write the reversible jump acceptance probability right*” that make me avoid using reversible jump each time I am facing a small enough collection of models to consider the exhaustive list of those models.

Filed under: Books, R, Statistics Tagged: Bayesian Core, mixture estimation, mixtures, Monte Carlo Statistical Methods, reversible jump, typos

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**Tags:** Bayesian Core, Books, mixture estimation, mixtures, Monte Carlo Statistical Methods, R, reversible jump, statistics, typos