Stephen Senn very kindly answered a post I wrote on p-values (Unsignificant Statistics: Or Die P-Value, Die Die Die) by sending me his “You May Believe You Are a Bayesian But You Are Probably Wrong” (in Rationality, Markets and Morals).
Since I will be teaching at Cornell these two weeks, and the topics are the same, I will use part of this time to answer his paper in depth.
It would be best to start here Subjective , since that series explains matters in greater detail. Versus Objective Bayes (Versus Frequentism): Part I
Senn went wrong before he even began, with his title: “You May Believe You Are a Bayesian But You Are Probably Wrong.” If you are only “probably wrong” about your belief then you also might be right. And if you were certainly wrong, then we would have a proof which says so. A proof is a string of deductions, i.e. a valid and sound argument, which begins with obviously true premises (agreed to by all) and ends at a proposition we must believe—even if we don’t want to.
Senn does not have, nor does he claim to have, a proof which shows being a Bayesian is certainly wrong. It is only his best guess that this philosophy is wrong. Probably wrong. So here we are, already at probability. What could Senn mean by his probabilistic statement “probably wrong”? (Besides the pun, I mean.) It can’t be any kind of frequentist statement, as in “I’ve collected a ‘random sample’ of Bayesian philosophies, itself embedded in an infinite sequence of such philosophies, and the mean of this sample (considering errors in theory equal to zero) tends towards zero.” That makes no kind of sense, as I’m sure Senn would agree, but it would have to if probability was frequentism.
Bayesian philosophy, at best, comes in a finite number of flavors. It could be that some of these are false (I agree subjectivism, as it is usually understood, is), but in no way can we imagine any individual theory as being embedded in an infinite sequence of theories, which is required for frequentist theory to hold. No: either we can prove each theory true or false, or our evidence is not (yet?) sufficient, and thus we are only probably sure each theory is true or false. This sounds like a Bayesian statement, no? (If so, do we fail because of self-reference? Well, no, because we can build this theory from simpler propositions.)
It could be that Senn took a subjective Bayesian tack when he formed his title, or perhaps he took a logical probability, or objective, Bayesian one. (Incidentally, I’ll call this latter theory LPB for short.) Or he could have meant some as yet unknown (or at least unidentified) theory. Whatever it was, it couldn’t have been frequentism, as shown.
His leading candidate is eclecticism (Senn is not frequentist), which is one of two things. One is no belief at all. It means “I’ll do whatever I want whenever it seems good to me.” There is no theory here to disprove, nor prove. To say “I’m an eclectic” this way means “I don’t want to argue for anything, just against things.” Since we go nowhere engaging with this “theory”, we pass on to number two. This is to say, “I’ll take a little of that, some of this, and some of the other.” Here we have several sub-theories. As such, this kind of eclecticism is actually a whole theory (the compilation of sub-theories) which might be true or false. Thus Senn might have used Bayes for his title and he might use frequentism for (say) dice tosses.
Senn recalls that Fisher himself was “skeptical” of attempts to unify probability. Hacking, another Big Cheese, in line with other well-aged curds, is of the same opinion. Why should we have a theory? Why not many? The obvious answer to this is that there is that which is true and that which is false and we should seek the truth. If it turns out a theory of probability works for all kinds of uncertainty, we’re stuck with it. If it must be that several theories are true, then we must accept them all. But it’s wrong to use desire or suspicion as proof there are many and not one theory.
Senn himself proved that frequentism is out (and forever) as a complete theory of probability because it cannot handle propositions like his “probably wrong.” But this isn’t proof that Bayes everywhere right; not yet. Senn’s later examples might be sufficient to show all versions of Bayes are wrong, in which case some other theory must be true.
But we’ll have to see next time, because we’re already out of space, and because next topic isn’t simple.