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"Frequentists vs. Bayesians"? The problem with this comic is that it paints statisticians as belonging to one of two opposing tribes, when really frequentist an

by adwn 11y ago

"Frequentists vs. Bayesians"? The problem with this comic is that it paints statisticians as belonging to one of two opposing tribes, when really frequentist and Baysian statistics are two different methods in the toolbox of a competent statistician – which of them is the appropriate one depends on the circumstances and the problem at hand. I'm not sure where this idea of an alleged divide comes from, but I think it might have to do with a certain small but vocal group on the Internet [1]. [1] cough Eliezer Yudkowsky cough

bonoboTP 11y ago
No, the philosophical debate exists and has a history. Also Bayesian is quite a buzzword that people use in paper titles just because it sounds like some seal of approval, even if it just means that they used Bayes' rule somewhere in the derivation of their formula. Anyway, using Bayes' formula is not the same as philosophical Bayesianism. Bayes' formula is simply a theorem of mathematics that follows from the axioms of probability theory. It's not controversial. The controversy comes from the applicability of Bayesian philosophy. In theory and ideally, it seems nice, but there are some problems with it. One well known problem is how to specifying your priors. A bigger issue that is often forgotten is that you can't calculate the conditional probability P(explanation | observation), without computing P(observation) = SUM_i [ P(observation | explanation_i) P(explanation_i) ] for the denominator of the Bayes formula. Now, this means that you have to consider all possible explanations. But systematically scanning through all possible hypotheses is really difficult. Usually it takes rare moments of random genious ideas to come up with a hypothesis that works well. You could say this is a "global optimization" problem in hypothesis space. Now, if you restrict the search for a particular class of hypotheses (a limited-flexibility model), then you can use computational methods, like Monte Carlo methods, to approximate this sum (integral) over a set of hypotheses. But you can never be sure if your modeling restrictions were valid or maybe the best explanation is outside "the box". When I hear Internet people (bloggers, commenters) saying they use Bayesianism in their everyday life it's almost always about pulling numbers out of their asses and then feeling great about how smart and rational they are. Everyday life is just too messy, there are many possibilities that are hard to capture in a model. Addendum: I also really hate how Lesswrongians hijacked and overmystify the term "Bayesian" (and even "rationalist") as if it were some panacea/silver bullet that magically turns you into some superhero. It's like a "one weird trick" BS just for smarter people.

by adwn 11y ago

bonoboTP 11y ago

No, the philosophical debate exists and has a history. Also Bayesian is quite a buzzword that people use in paper titles just because it sounds like some seal of approval, even if it just means that they used Bayes' rule somewhere in the derivation of their formula. Anyway, using Bayes' formula is not the same as philosophical Bayesianism. Bayes' formula is simply a theorem of mathematics that follows from the axioms of probability theory. It's not controversial. The controversy comes from the applicability of Bayesian philosophy. In theory and ideally, it seems nice, but there are some problems with it. One well known problem is how to specifying your priors. A bigger issue that is often forgotten is that you can't calculate the conditional probability P(explanation | observation), without computing P(observation) = SUM_i [ P(observation | explanation_i) P(explanation_i) ] for the denominator of the Bayes formula. Now, this means that you have to consider all possible explanations. But systematically scanning through all possible hypotheses is really difficult. Usually it takes rare moments of random genious ideas to come up with a hypothesis that works well. You could say this is a "global optimization" problem in hypothesis space. Now, if you restrict the search for a particular class of hypotheses (a limited-flexibility model), then you can use computational methods, like Monte Carlo methods, to approximate this sum (integral) over a set of hypotheses. But you can never be sure if your modeling restrictions were valid or maybe the best explanation is outside "the box". When I hear Internet people (bloggers, commenters) saying they use Bayesianism in their everyday life it's almost always about pulling numbers out of their asses and then feeling great about how smart and rational they are. Everyday life is just too messy, there are many possibilities that are hard to capture in a model. Addendum: I also really hate how Lesswrongians hijacked and overmystify the term "Bayesian" (and even "rationalist") as if it were some panacea/silver bullet that magically turns you into some superhero. It's like a "one weird trick" BS just for smarter people.