# Confusing slice sampler

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

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**M**ost embarrassingly, Liaosa Xu from Virginia Tech sent the following email almost a month ago and I forgot to reply:

I have a question regarding your example 7.11 in your book

. To further decompose the uniform simulation by sampling a and b step by step, how you determine the upper bound for sampling of a? I don’t know why, for all y(i)=0, we need a+bx(i)>- log(u(i)/(1-u(i))). It seems that for y(i)=0, we get 0>log(u(i)/(1-u(i))). Thanks a lot for your clarification.Introducing Monte Carlo Methods with R

**T**here is nothing wrong with our resolution of the logit simulation problem but I acknowledge the way we wrote it is most confusing! Especially when switching from to in the middle of the example….

**S**tarting with the likelihood/posterior

we use slice sampling to replace each logistic expression with an indicator involving a uniform auxiliary variable

[which is the first formula at the top of page 220.] Now, when considering the joint distribution of

,

we only get a product of indicators. Either indicators that

or of ,

depending on whether *y _{i}=1* or

*y*. The first case produces the equivalent condition

_{i}=0This is how we derive both uniform distributions in and $beta$.

**W**hat is both a typo and potentially confusing is the second formula in page 220, where we mention the uniform over the set.

instead of the . It should be

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