Bayesian prior: Difference between revisions
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{{a|design|}}{{Sbf on bayesian priors}}{{dpn|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 (c) you are trying to estimate the likelihood that a specific Elizabethan playwright really was the best playwright in history and didn’t just somehow fluke it, then your knowledge that there were vastly fewer active playwrights in the Elizabethan period than have existed in all of dramatic history until now, you might conclude that the odds of that Elizabethan playwright really being the best are vanishingly low. | {{a|design|}}{{quote| | ||
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{{dpn|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 (c) you are trying to estimate the likelihood that a specific Elizabethan playwright really was the best playwright in history and didn’t just somehow fluke it, then your knowledge that there were vastly fewer active playwrights in the Elizabethan period than have existed in all of dramatic history until now, you might 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. | At the same time, everyone else will conclude that you have no idea about aesthetics, and a fairly shaky grasp even of Bayesian statistics. | ||
Revision as of 14:30, 18 November 2023
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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
Bayesian prior
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 (c) you are trying to estimate the likelihood that a specific Elizabethan playwright really was the best playwright in history and didn’t just somehow fluke it, then your knowledge that there were vastly fewer active playwrights in the Elizabethan period than have existed in all of dramatic history until now, you might 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.