arXiv:1405.2061cs.IT
Shannon's metric of "Entropy" of information is a foundational concept of information theory. This article is a primer for novices that presents an intuitive way of understanding, remembering, and/or reconstructing Shannon's Entropy metric for information.
Linked by 2 identifiable commenters in 2 comments across 2 threads, from Jan 2021 to Oct 2021. Found in 1 HN submission from Oct 2021 to Oct 2021. Open submission discussions.
It does indeed sound like it’s written the wrong way around. Random information = high entropy. A common word is therefore low entropy. Here’s a good analogy I found: > Informally, the amount of information in an email is proportional to the amount of “surprise” its reading…
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I read your arxiv.org article on Shannon Entropy: https://arxiv.org/abs/1405.2061 What I am amazed at is that arxiv accepted this. I submitted exactly the same argument to them on 27 Jan 2009. They rejected it. Here it is on my home page: http://dsw.users.sonic.net/entropy.html…
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