TE
TechEcho
Home24h TopNewestBestAskShowJobs
GitHubTwitter
Home

TechEcho

A tech news platform built with Next.js, providing global tech news and discussions.

GitHubTwitter

Home

HomeNewestBestAskShowJobs

Resources

HackerNews APIOriginal HackerNewsNext.js

© 2025 TechEcho. All rights reserved.

An alternative construction of Shannon entropy

126 pointsby rkp80006 months ago

8 comments

IdealeZahlen6 months ago
Calling this 'alternative' construction seems like coming full circle since this line of combinatorial argument is how Boltzmann came up with his H-function in the first place, which inspired Shannon's entropy.
评论 #42175985 未加载
kgwgk6 months ago
Seems similar to <a href="https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Principle_of_maximum_entropy#The_Wallis_derivation" rel="nofollow">https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Principle_of_maximum_entropy#T...</a>
mturmon6 months ago
This is what I learned as the “theory of types” from Cover and Thomas, chapter 11, from original work by Imre Csiszar. See just under “theorem 6” in<p><a href="https:&#x2F;&#x2F;web.stanford.edu&#x2F;class&#x2F;ee376a&#x2F;files&#x2F;2017-18&#x2F;lecture_13.pdf" rel="nofollow">https:&#x2F;&#x2F;web.stanford.edu&#x2F;class&#x2F;ee376a&#x2F;files&#x2F;2017-18&#x2F;lecture_...</a><p>The key (which is not in OP) is not the construction of E log(p), but in being able to prove that the “typical set” exists (with arbitrarily high probability), and that the entropy is its size.
xigoi6 months ago
The site is unreadable on mobile because it disables overflow on the equations (which it shows as images, even though it’s 2024 and all modern browsers support MathML).
评论 #42168286 未加载
ivan_ah6 months ago
Nice!<p>The key step of the derivation is counting the &quot;number of ways&quot; to get the histogram with bar heights L1, L2, ... Ln for a total of L observations.<p>I had to think a bit why the provided formula is true:<p><pre><code> choose(L,L1) * choose(L-L1,L2) * ... * choose(Ln,Ln) </code></pre> The story I came up with for the first term, is that in the sequence of lenght L, you need to choose L1 locations that will get the symbol x1, so there are choose(L,L1) ways to do that. Next you have L-L1 remaining spots to fill, and L2 of those need to have the symbol x2, hence the choose(L-L1,L2) term, etc.
Maro6 months ago
In Physics, the log part comes in when you use the Stirling approximation for large N.<p>Ideal gas: <a href="https:&#x2F;&#x2F;bytepawn.com&#x2F;entropy-of-an-ideal-gas-with-coarse-graining.html" rel="nofollow">https:&#x2F;&#x2F;bytepawn.com&#x2F;entropy-of-an-ideal-gas-with-coarse-gra...</a><p>Physical gas: <a href="https:&#x2F;&#x2F;bytepawn.com&#x2F;the-physical-sackur-tetrode-entropy-of-an-ideal-gas.html" rel="nofollow">https:&#x2F;&#x2F;bytepawn.com&#x2F;the-physical-sackur-tetrode-entropy-of-...</a>
canjobear6 months ago
Interestingly this has a rather frequentist flavor. The probabilities end up coming from frequency ratios in very large samples.
评论 #42172905 未加载
ziofill6 months ago
Eh ok, but the trick is then taking the limit for L-&gt;infty and use Stirling’s approx which is what Shannon did