Yes, a perfect Bayesian making perfect updates is perfect, we all know that :-)
My point is that I can remember easily that things are false, or that they are true. But to remember that they are somewhere in between is much harder, unless it’s things I really care about. You have to keep track of the data, and compare it with new results.
But to remember that they are somewhere in between is much harder, unless it’s things I really care about.
It isn’t in between. Your knowledge of the question is in between. You would like it to be closer to one end or the other. You can apply a whole lot of heuristics without messing this part up.
Yes. And that’s what’s harder to remember. I “know” that Lincoln was assassinated, and I “know” that Charles de Gaulle didn’t die in Burma. But trying to remember what my estimate is as to whether it’s good or bad for overweight people to go on a diet… that’s a lot harder.
Well, a Bayesian learner should eventually converge on the truth if the prior supports it, so that can be viewed as an “attractor” too...
Yes, a perfect Bayesian making perfect updates is perfect, we all know that :-)
My point is that I can remember easily that things are false, or that they are true. But to remember that they are somewhere in between is much harder, unless it’s things I really care about. You have to keep track of the data, and compare it with new results.
It isn’t in between. Your knowledge of the question is in between. You would like it to be closer to one end or the other. You can apply a whole lot of heuristics without messing this part up.
Yes. And that’s what’s harder to remember. I “know” that Lincoln was assassinated, and I “know” that Charles de Gaulle didn’t die in Burma. But trying to remember what my estimate is as to whether it’s good or bad for overweight people to go on a diet… that’s a lot harder.