Didn’t realize you were here so let me be clear that I did overall find the paper so approachable that I could implement it with only a couple of outside pointers (also a little clever impl optimization around storing p if you’re curious). The above should be read more as “even this well-written simplified paper is not necessarily trivial to understand for practicians”. So more of a general point around academic obscurity.
> But yes, our theorems can be reworked to estimate the confidence/error rate
I think that’s useful for practical implications. Also, for practical use, how does one decide the tradeoff between delta and epsilon? Perhaps it’s covered elsewhere, but I have a hard time intuiting their relationship.
I fully agree with you and this is indeed one of my criticisms of modern academic writing -- we tend to write papers that are just very hard for anyone to read.
So delta refers to the confidence, i.e., how often are you willing to be wrong, and epsilon is tolerance with respect to the actual count.
We have found that in general, setting delta=0.1 and espilon=0.8 works fine for most practical applications.
> But yes, our theorems can be reworked to estimate the confidence/error rate
I think that’s useful for practical implications. Also, for practical use, how does one decide the tradeoff between delta and epsilon? Perhaps it’s covered elsewhere, but I have a hard time intuiting their relationship.