Andrew Gelman (and Aleks Jakulin) from Statistica Sinica, posted over at his blog:
As a lifetime member of the International Chinese Statistical Association, I am pleased to introduce a volume of Bayesian articles. I remember that in graduate school, Xiao-Li Meng, now editor of this journal, told me they didn’t teach Bayesian statistics in China because the idea of a prior distribution was contrary to Mao’s quotation, “truth comes out of empirical/practical evidence.” I have no idea how Thomas Bayes would feel about this, but Pierre-Simon Laplace, who is often regarded as the first applied Bayesian, was active in politics during and after the French Revolution.
In the twentieth-century Anglo-American statistical tradition, Bayesianism has certainly been seen as radical. As statisticians, we are generally trained to respect conservatism, which can sometimes be defined mathematically (for example, nominal 95% intervals that contain the true value more than 95% of the time) and sometimes with reference to tradition (for example, deferring to least-squares or maximum-likelihood estimates). Statisticians are typically worried about messing with data, which perhaps is one reason that the Current Index to Statistics lists 131 articles with “conservative” in the title or keywords and only 46 with the words “liberal” or “radical.”
Like many political terms, the meaning of conservatism depends on its comparison point.