P(E) can be broken down into P(E|A)P(A) + P(E|~A)P(~A). Our temptation, when looking at a model, is to treat P(E|~A)*P(~A) as smaller than it really is—the question is, “Is the number of worlds in which the hypothesis is false but the evidence exists anyway large or small?” Yvain is noting that, because we are crazy, we tend to forget about many (or most) of these worlds when looking at evidence. We should expect the number of these worlds to be much larger than the number of worlds in which our probabililty calculations are everywhere and always correct.
The math doesn’t work out to “round up” exactly. It’s situation-dependent. It’s entirely possible that the model is so ill-specified that every variable has the wrong sign. The math will usually work out to deviation towards priors, even if only slightly.
Here’s a post on the same problem in social sciences.
P(E) can be broken down into P(E|A)P(A) + P(E|~A)P(~A). Our temptation, when looking at a model, is to treat P(E|~A)*P(~A) as smaller than it really is—the question is, “Is the number of worlds in which the hypothesis is false but the evidence exists anyway large or small?” Yvain is noting that, because we are crazy, we tend to forget about many (or most) of these worlds when looking at evidence. We should expect the number of these worlds to be much larger than the number of worlds in which our probabililty calculations are everywhere and always correct.
The math doesn’t work out to “round up” exactly. It’s situation-dependent. It’s entirely possible that the model is so ill-specified that every variable has the wrong sign. The math will usually work out to deviation towards priors, even if only slightly.
Here’s a post on the same problem in social sciences.
What’s A?
“Deviation towards priors” sounds again like we are positing a bound on log(P(E|H)/P(E)). How can I estimate this bound?