# Example 7.3: what a mess!

**R – Xi'an's Og**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

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**A** rather obscure question on Metropolis-Hastings algorithms on X Validated ended up being about our first illustration in Introducing Monte Carlo methods with R. And exposing some inconsistencies in the following example… Example 7.2 is based on a [toy] joint Beta x Binomial target, which leads to a basic Gibbs sampler. We thought this was straightforward, but it may confuse readers who think of using Gibbs sampling for posterior simulation as, in this case, there is neither observation nor posterior, but simply a (joint) target in (x,θ).

And then it indeed came out that we had incorrectly written Example 7.3 on the [toy] Normal posterior, using at times a Normal mean prior with a [prior] variance scaled by the sampling variance and at times a Normal mean prior with a [prior] variance unscaled by the sampling variance. I am rather amazed that this did not show up earlier. Although there were already typos listed about that example.

Filed under: Books, Kids, R, Statistics, University life Tagged: beta distribution, cross validated, George Casella, Gibbs sampling, Introducing Monte Carlo Methods with R, Metropolis-Hastings algorithm, typos

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