Kinda, but to make it more explicit: with floating point numbers you don't even need the explicit log distribution because it's kinda baked in already, with the

Kinda, but to make it more explicit: with floating point numbers you don't even need the explicit log distribution because it's kinda baked in already, with the number split between an absolute and an exponential part. So floats capture an exponential scale of precision naturally, allowing you to write 1e-200 and 1 and 1e200 just as easily, with a common relative precision around each. You use the log scale as a mental model and then just keep floating point issues in mind while you work. The handy functions are more about making sure that cancellations don't happen by providing a few primitive operations that cover lots of the uses. Like the handy log1p which computes log(1+x). If you did this with the log function, you'll compute log(1+1e-200)=log(1)=0. If you use log1p, you get log1p(1e-200)=1e-200. You avoid the loss of info that comes with adding something close to zero to something that isn't close to zero, which is the real trick. And then if you need something close to one, you probable just use the converse: instead of using p=1-eps, you just work with eps itself.