Eliezer_Yudkowsky comments on Bayes' rule =/= Bayesian inference - Less Wrong

37 Post author: neq1 16 September 2010 06:34AM

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Comment author: Perplexed 16 September 2010 05:17:58PM *  8 points [-]

Joseph Felsenstein is a pioneer in the use of maximum likelihood methods in evolutionary biology. In his book, "Inferring Phylogenies", he has a chapter on Bayesian methods, and a section in that chapter on controversies over Bayesian inference. He discusses a toy example of a space probe to Mars which looks for little green men and doesn't find them. He wonders whether a scientist whose prior for little green men involved odds of 1/4, and who, based on the evidence of the space probe, now assigns odds of 1/12, should publish those revised odds. He writes:

It might be argued that the correct thing to do in such a case is to publish the likelihood ratio 1/3 and let the reader provide their own prior. This is the likelihoodist position. A Bayesian is defined, not by using a prior, but by being willing to use a controversial prior.

Felsenstein apparently defines himself as a "likelihoodist" rather than a "frequentist" or "Bayesian".

Comment author: Eliezer_Yudkowsky 16 September 2010 09:11:37PM 3 points [-]

"Maximum likelihood" totally != "report likelihood ratios".

Comment author: Perplexed 16 September 2010 09:32:12PM 3 points [-]

Yes, I know, as I'm sure does Felsenstein. The book covered much more than maximum likelihood. The recommendation to report likelihood ratios came in the first of two chapters on Bayesian methods. The second involved hidden Markov models.

The book begins (as does the field) with a tree-building method called 'maximum parsimony'. Maximum likelihood is a step up in sophistication from that, and Felsenstein is largely responsible for that step forward. I'm not really sure why he is not an enthusiastic Bayesian. My guess would be that it is because he is a professional statistician and the whole discipline of statistics traditionally consists of ways of drawing totally objective conclusions from data.