Re:Benford's Law
by hyperbovine on Sunday February 16, 2020 @02:37AM
(#22340404)
The max entropy distribution over (-oo,+oo) is usually taken to be either Gaussian or Laplace (two sided exponential), depending on moment conditions. There is no such thing as a uniform distribution over the real line.
[ Parent
| on HN ]
Re:Benford's Law
by BenoitEssiambre on Sunday February 16, 2020 @08:35AM
(#22341469)
Of course there is. It's an improper prior (doesn't sum up to one), see link above. Theoretically the Gaussian is only maximum entropy given a mean and stddev. An infinite uniform is maxent without any knowledge of mean and stddev.
In practice though you can use a wide Gaussian if it is more mathematically convenient and get about the same results.
Re:Benford's Law
by hyperbovine on Sunday February 16, 2020 @03:57PM
(#22343709)
The entropy of any improper prior diverges. There is no point in arguing over which one maximizes entropy; they all do, vacuously. Max entropy is not useful for discriminating amongst probability distributions unless you require them to actually be probability distributions.
Side note: great how you spell infinity. Never saw this before. So simple and smart
Re:Benford's Law
by polyphonicist on Sunday February 16, 2020 @04:31AM
(#22340727)
Came here to say the same thing. Certainly haven't seen such depiction of infinity earlier. But this is a nice way. I am going to use it myself.