"It’s a Bayesian thing. Part of Bayesian reasoning is to think like a Bayesian; another part is to assess other people’s conclusions as if they are Bayesians..."

"... and use this to deduce their priors. I’m not saying that other researchers are Bayesian—indeed I’m not always so Bayesian myself—rather, I’m arguing that looking at inferences from this implicit Bayesian perspective can be helpful, in the same way that economists can look at people’s decisions and deduce their implicit utilities. It’s a Neumann thing: again, you won’t learn people’s 'true priors' any more than you’ll learn their 'true utilities'—or, for that matter, any more than a test will reveal students’ 'true abilities'—but it’s a baseline."

From "Reverse-engineering priors in coronavirus discourse" by Andrew (at Statistical Modeling, Causal Inference, and Social Science,) via "What’s the Deal With Bayesian Statistics?" by Kevin Drum (at Mother Jones).

Both of these posts went up yesterday, that is, 2 days after I said, "Shouldn't we talk about Bayes theorem?" I'm not saying I caused that. I'm just saying maybe you should use Bayesian reasoning to figure out if I did. I will stand back and say, this is not my field. I'm only here to encourage it.

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