I have some data generated using MCMC methods and in particular Gibbs sampling. I computed the autocorrelation but I'm unsure how to determine how many samples to skip.


I'd like to determine that quantity from the autocorrelation function that I computed and not just use a "reasonable" value. Any documents to help me? Direct help is also welcome.



Gibbs sampling step size
To expand a bit on Arthur B.'s comment that you can use samples even if they are dependent, consider this quote from Andrew Gelman and Kenneth Shirley:



>  The purpose of thinning (i.e. setting n
> to some integer greater than 1) is computational, not statistical. If we have a model with
> 2000 parameters and we are running three chains with a million iterations each, we do not
> want to be carrying around 6 billion numbers in our simulation. The key is to realize that,
> if we really needed a million iterations, they must be so highly autocorrelated that little is
> gained by saving them all. In practice, we ﬁnd it is generally more than enough to save
> 1000 iterations in total, and so we thin accordingly. But ultimately this will depend on the
> size of the model and computational constraints. 




The [full article](http://www.stat.columbia.edu/~gelman/research/published/GelmanMCMCchapter4.pdf) is full of great practical recommendations for using MCMC for inference. 


