(This article was originally published at R – Xi'an's Og, and syndicated at StatsBlogs.)

**Q**uentin F. Gronau, Henrik Singmann and Eric-Jan Wagenmakers have arXived a detailed documentation about their * bridgesampling* R package. (No wonder that researchers from Amsterdam favour bridge sampling!)

*[The package relates to a [52 pages] tutorial on bridge sampling by Gronau et al. that I will hopefully comment soon.]*The bridge sampling methodology for marginal likelihood approximation requires

*two*Monte Carlo samples for a ratio of

*two*integrals. A nice twist in this approach is to use a dummy integral that is already available, with respect to a probability density that is an approximation to the exact posterior. This means avoiding the difficulties with bridge sampling of bridging two different parameter spaces, in possibly different dimensions, with potentially very little overlap between the posterior distributions. The substitute probability density is chosen as Normal or warped Normal, rather than a t which would provide more stability in my opinion. The

*package also provides an error evaluation for the approximation, although based on spectral estimates derived from the*

**bridgesampling****package. The remainder of the document exhibits how the package can be used in conjunction with either JAGS or Stan. And concludes with the following words of caution:**

*coda*

“It should also be kept in mind that there may be cases in which the bridge sampling procedure may not be the ideal choice for conducting Bayesian model comparisons. For instance, when the models are nested it might be faster and easier to use the Savage-Dickey density ratio (Dickey and Lientz 1970; Wagenmakers et al. 2010). Another example is when the comparison of interest concerns a very large model space, and a separate bridge sampling based computation of marginal likelihoods may take too much time. In this scenario, Reversible Jump MCMC (Green 1995) may be more appropriate.”

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