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Mar 21, 2017 at 23:51 comment added Deane Yang I meant differential entropy. You can check that if $f$ is the probability density for a random vector $X \in \mathbb{R}^n$, then $N[X] = \exp -\int f\log f$ scales like $N[tX] = t^nN[X]$. It has units of volume.
Mar 21, 2017 at 23:48 history edited Arthur B CC BY-SA 3.0
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Mar 21, 2017 at 23:47 comment added Arthur B Entropy is, differential entropy isn't.
Mar 21, 2017 at 23:43 comment added Deane Yang Entropy is in fact a meaningful physical or information quantity in the same sense that decibels are. Both are logarithmic quantities. The entropy power ($exp -\int f\log f$) scales quite nicely.
Mar 21, 2017 at 22:17 history edited Arthur B CC BY-SA 3.0
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Mar 21, 2017 at 19:06 comment added Henry.L If $f$ is the density of the measure w.r.t. the Lebesgue measure, then this seems pretty restrictive. Is there a necessary condition?
Mar 21, 2017 at 18:21 comment added Arthur B Also, by that definition, you can get negative entropy, even though the entropy is supposed to be the log of a number of configurations. Yes you can define it mathematically, but how meaningful is it?
Mar 21, 2017 at 18:12 comment added Arthur B It's a matter of philosophy. Accepting these priors give you probability paradoxes.
Mar 21, 2017 at 17:49 comment added Qiaochu Yuan The concept is perfectly meaningful. You can take the KL divergence of a measure, not necessarily a probability measure, with respect to another measure as long as the relevant Radon-Nikodym derivative is defined. The uniform distribution over the real line corresponds to Lebesgue measure, and can be used e.g. as an "improper prior" in Bayesian inference.
Mar 21, 2017 at 17:12 history edited Arthur B CC BY-SA 3.0
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Mar 21, 2017 at 17:06 history answered Arthur B CC BY-SA 3.0