stats 

interesting paper by Gelman and Shalizi on the philosophy of Bayesian methods: stat.columbia.edu/~gelman/rese

they argue that the common interpretation of Bayes as updating levels of certainty is flawed, and it’s better seen as Popperian hypothesis generation and testing. crucial to practice is that posteriors should be routinely checked and models constantly changed/replaced, not just updated.

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stats 

also: choice quote: a lil burn on something that always bothered me:

> statistical folklore says that the chi-squared statistic is ultimately a measure of sample size (cf. Lindsay & Liu, 2009) [→ arxiv.org/abs/1010.0304 ]

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