(This article was first published on

**Probability and statistics blog » r**, and kindly contributed to R-bloggers)Sample once from the Uniform(0,1) distribution. Call the resulting value . Multiply this result by some constant . Repeat the process, this time sampling from Uniform(0, ). What happens when the multiplier is 2? How big does the multiplier have to be to force divergence. Try it and see:

iters = 200 locations = rep(0,iters) top = 1 multiplier = 2 for(i in 1:iters) { locations[i] = runif(1,0,top) top = locations[i] * multiplier } windows() plot(locations[1:i],1:i,pch=20,col="blue",xlim=c(0,max(locations)),ylim=c(0,iters),xlab="Location",ylab="Iteration") # Optional save as movie, not a good idea for more than a few hundred iterations. I warned you! # library("animation") # saveMovie(for (i in 1:iters) plot(locations[1:i],1:i,pch=20,col="blue",xlim=c(0,max(locations)),ylim=c(0,iters),xlab="Location",ylab="Iteration"),loop=1,interval=.1)

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