Bayesian reasoning
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The design of organisations and products
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I could go on and on about the failings of Shakespeare ... but really I shouldn’t need to: the Bayesian priors are pretty damning. About half of the people born since 1600 have been born in the past 100 years, but it gets much worse than that. When Shakespeare wrote, almost all of Europeans were busy farming, and very few people attended university; few people were even literate—probably as low as about ten million people. By contrast, there are now upwards of a billion literate people in the Western sphere. What are the odds that the greatest writer would have been born in 1564?
- —Chauncey Gardiner’s “sophomore college blog”, quoted in Michael Lewis’ Going Infinite
You ever seen the dude from FTX? The one that went to prison? That dude shouldn’t be talking about Shakespeare.
- —Mike Tyson
Bayesian reasoning
beɪzˈiːən ˈpraɪə (n.)
A way to incorporate existing knowledge or beliefs about a parameter into statistical analysis. For example, if you believe that
- (a) all playwrights can be objectively ranked according to independent, observable criteria;
- (b) the quality of those playwrights in a given sample will be normally distributed;
and you think the best way of assessing the quality of dramas is by statistical analysis, then
- (i) you have already made several category errors, should not be talking about art, and if you are, no-one should be listening; but
- (ii) if, nonetheless, you are, and they are, and you are trying to estimate the statistical likelihood of a specific Elizabethan playwright being the best in history, then your knowledge that there were vastly fewer playwrights active in the Elizabethan period than have existed in all of history until now — which is a Bayesian prior distribution — might help you conclude that the odds of that Elizabethan playwright really being the best are vanishingly low.
At the same time, everyone else will conclude that you have no idea about aesthetics and a fairly shaky grasp even of Bayesian statistics.