Link o' the week: Bayes' Theorem

I seem to be up to my armpits in Bayesian statistics. Between Thrun and Norvig's AI class (the AI classes I took back in the late '80's and early '90's primarily dealt with state-space search, neural networks, and predicate logic; there was nary a statistic to be found) and the very good textbook I ran across, Doing Bayesian Data Analysis: A Tutorial with R and BUGS by John K. Kruschke, this stuff seems to be appearing everywhere.

Then I ran across Eliezer Yudkowsky's post, An Intuitive Explanation of Bayes' Theorem. Now, intuition is a funny thing. It is not innate as most people seem to think, you have to train it. Yudkowsky does that, going over the same ground with many examples and good explanations. Better, his writing, like Kruschke's, is light and entertaining:

Fun Fact!
	Q.	How can I find the priors for a problem?
	A.	Many commonly used priors are listed in the Handbook of Chemistry and Physics.
	Q.	Where do priors originally come from?
	A.	Never ask that question.
	Q.	Uh huh. Then where do scientists get their priors?
	A.	Priors for scientific problems are established by annual vote of the AAAS. In recent years the vote has become fractious and controversial, with widespread acrimony, factional polarization, and several outright assassinations. This may be a front for infighting within the Bayes Council, or it may be that the disputants have too much spare time. No one is really sure.
	Q.	I see. And where does everyone else get their priors?
	A.	They download their priors from Kazaa.
	Q.	What if the priors I want aren't available on Kazaa?
	A.	There's a small, cluttered antique shop in a back alley of San Francisco's Chinatown. Don't ask about the bronze rat.

Bronze rat aside, I also like the Javascript calculators embedded in the article, allowing a reader to try to figure out the answers to Yudkowsky's examples without dredging up another app.