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...

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speff an hour ago | parent | next [-]

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

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senkora 3 hours ago | parent | prev | next [-]

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.

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kqr 3 hours ago | parent | prev [-]

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

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riedel 2 hours ago | parent [-]

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).

	▲	tgv 2 hours ago \| parent [-]
		It is indeed a great tool for visualizing Bayesian relations. You can even "feel" the sensitivity.