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 At the same time, everyone else will conclude that you have no idea about aesthetics and a fairly shaky grasp even of Bayesian statistics.
+====The Monty Hall problem ====
+The neatest illustration of how Bayesian priors are meant to work is the “Monty Hall” problem, named for the ghost of the gameshow Deal or No Deal and famously articulated in a letter to ''Parade'' Magazine as follows:
+{{quote|
+Suppose you're on a game show, and youʼre given the choice of three doors: Behind one door is a car; behind the others, goats. You pick a door, say No. 1, and the host, who knows whatʼs behind the doors, opens another door, say No. 3, which has a goat. He then says to you, "Do you want to pick door No. 2?" Is it to your advantage to switch your choice?}}
-Bayesian probabilities are a clever way of deducing, a priori, that we are all screwed. If you find yourself at or near the beginning of something, such as Civilisation, a bayesian model will tell you it will almost certainly end soon.
+If you have not encountered the problem before, the intuitive answer is to say, no: each door carried an equal probability, 1/3, of containing the car before the host opened a door, and each carries an equal probability, 1/2, afterward. To be sure the odds are better now than they were, but one should still be indifferent as to whether to switch.
+Bayes says no: since the host will never open the door you chose, nor the door concealing the car, this new information tells you something about the remaining choice. Your original choice keeps its original odds of 1/3; the odds as between the other two doors change, from 1/3 each to 0/3 for the open door, which definitely doesnʼt hold the car, 2/3 for the closed one, which still might. So you should switch doors. You exchange a 1/3 risk of being right for a 1/3 risk of being wrong.
+This proposal outrages some people, at first. Apparently, even statisticians. But it is true. It becomes more intuitive if you adjust the thought experiment so there are one ''thousand'' doors, not three, and after your 1/1000 choice the host reveals 998 of the other doors to reveal goats and leaves one shut. ''Now'' would you switch? Clearly, the other door now accounts for 999/1000 of the original options.
+Or you could just experiment.
+====Bayesian probabilies are probabilities ====
+Now all of this is well and good and unimpeachable if the nomological conditions for probabilities hold. There needs to be a static, finite sample space — 1000 doors —and a finite and known number of discrete outcomes — goat or car. It also works for coins dice, cards and games of chance. These are simple systems, easily reduced to nomological machines
+====The doomsday problem ====
+Bayesian probabilities are a clever way of deducing, [[a priori]] , that we are all screwed. If you find yourself at or near the beginning of something, such as Civilisation, a bayesian model will tell you it will almost certainly end soon.
 It works on elementary probability and can be illustrated simply.