Seriously, though, there's one nomogram you (yes you) should know about and have it well-enough engraved in your mind's eye that you can use it with eyes closed. A nomogram for Bayes' theorem: https://www.ovid.com/journals/nejm/abstract/10.1056/nejm1975...
That is cool, although it took me awhile to understand it because the posterior probability is on the left and the prior probability is on the right, and because it uses D=Disease and T=Test when I am used to seeing D=Data.
I am fascinated with nomograms ever since I stumbled upon them.
I spent some time earlier this year creating one for two resistors in parallel. I had seen it in an old book [1] but it was of poor quality.
(I tried to get Gemini writing to write code to generate an SVG file—but it was pretty poor compared to the one that I had done by hand in Affinity Designer.)
If you like things like this I can recommend you check out the Chris Staecker youtube channel. He covers all sorts of tools people used to use to do math before computers and calculators, and there are a lot of them. Some of the things people came up with to do what today would be considered relatively simple math are pretty clever, pretty complex, or both.
Seriously, though, there's one nomogram you (yes you) should know about and have it well-enough engraved in your mind's eye that you can use it with eyes closed. A nomogram for Bayes' theorem: https://www.ovid.com/journals/nejm/abstract/10.1056/nejm1975...
That is cool, although it took me awhile to understand it because the posterior probability is on the left and the prior probability is on the right, and because it uses D=Disease and T=Test when I am used to seeing D=Data.
Neat. This is based on Bayes' rule in its odds form[1], or more specifically in log-odds form, where evidence is additive[2].
[1]: https://entropicthoughts.com/bayes-rule-odds-form
[2]: https://entropicthoughts.com/sensitivity-counts-against-you
Actually I find nomograms in log form really cool for making naive bayes classifiers 'explainable'. One can even add density for continuous values.
IMHO this is so much nicer than e.g. decisions tree visualizations (which everyone quotes for the most explainable AI models).
It is indeed a great tool for visualizing Bayesian relations. You can even "feel" the sensitivity.
That was a bit small on my screen. Found an interactive one here that's scalable - https://www.medcalc.org/en/calc/fagans-nomogram.php
Can you use actually use it eyes closed? Never heard of that level of precision in the mind's eye
Here’s an old python program to make pdf nomograms from almost any formula. The example of payment for a loan is one of my favorites.
https://github.com/lefakkomies/pynomo
I am fascinated with nomograms ever since I stumbled upon them.
I spent some time earlier this year creating one for two resistors in parallel. I had seen it in an old book [1] but it was of poor quality.
(I tried to get Gemini writing to write code to generate an SVG file—but it was pretty poor compared to the one that I had done by hand in Affinity Designer.)
[1] https://www.worldradiohistory.com/BOOKSHELF-ARH/Technology/T...
The Smith chart is the electrical engineer's favorite: https://en.wikipedia.org/wiki/Smith_chart
You either love it or hate it, depending on how well your electromagnetics class was taught.
If you like things like this I can recommend you check out the Chris Staecker youtube channel. He covers all sorts of tools people used to use to do math before computers and calculators, and there are a lot of them. Some of the things people came up with to do what today would be considered relatively simple math are pretty clever, pretty complex, or both.
https://www.youtube.com/@ChrisStaecker
I read the title as "Nonogram" (Picross) at first !
I read PyNomo as "Py no mo' " and was overcome by a feeling of loss.
video explaining what a Nomogram is and how to make them by hand https://www.youtube.com/watch?v=GCd9hANNLsw
The US Navy still uses nomograms for chemistry control on nuclear reactors!
There's an old paper about the mathematics of nomograms that I found interested when I stumbled across it: https://doi.org/10.1016/0001-8708(65)90042-3
I think the Numogram is more interesting, highly relevant today due to AI happenings