Re: Bayesian Networks and Belief Functions

Geoffrey Rutledge (rutledge@healtheon.com)
Mon, 07 Jun 1999 07:17:47 -0700

I thought that only the mode of the distribution on a prior has any
effect on a decision. In the example David gives below, at first the
prior of 0.5 is uncertain (has a wide distribution), whereas after 1M
trials, the distribution on the prior of 0.5 is quite narrow -- but any
decision to be made that depends on the prior is unchanged in the two
cases.

Isn't it provable that for expected utility decision making, uncertainty
on the prior is irrelevant? Can someone point to that proof?

Geoff

---
Geoffrey Rutledge, MD, PhD
Director of Clinical Informatics
Healtheon Corporation
---------------------------------

David Poole wrote: > > Rolf Haenni wrote: > > But knowing that P(Q)=0.5 is > > clearly not the same as to know nothing about Q. > > This isn't so clear. Here is some reasoning I have used to convince > myself that these are indeed the same: > > Suppose we have a button, and when we press the button, there is one of > two outcomes, either a H or a T (just to make it look familiar). Let the > event Q be the outcome of pressing the button. > > Before we press any buttons, I ask you what will be the first outcome. > You, of course, say "I don't know". > > Suppose we press the button 4 times and observe a H,T,T,H. And then I > ask you what will be the outcome of the 5th press. You then would also > say "I don't know". > > Now suppose that we were to press the button 1,000,000 times and we > observe that half of them resulted in H and half resulted in T (and we > could not detect a pattern in the sequence). If I then ask you to say > what the outcome of the 1,000,001st press will be, you would also say "I > don't know". > > The first "I don't know" was the result of knowing nothing about Q. > The last "I don't know" was the result of knowing the P(Q) is 0.5. > > But it seems to me that these "I don't knows" are not of different > types: you have no idea whether the outcome will be H or the outcome > will be T initially or for the 1,000,001st press. [Of course you have > very different knowledge about the probability of Q, but no one would > claim any differently.] Parsimony would suggest that we don't need to > distinguish these "I don't know"s (i.e,., the prediction from ignorance > and the prediction from knowing the probability is 0.5). > > For those people who would like to distinguish ignorance for the outcome > of a binary variables and probability 0.5, I would like to know how many > different meanings are there to "I don't know" (for a binary random > variables)? (If there are a finite number (such as two) different > meanings, when in the above sequence of making predictions and pressing > the button do the meanings switch? Is the "I don't know" prediction for > the 5th toss have the same meaning as the first "I don't know" or the > same meaning as the "I don't know" for the prediction of the 1,000,001st > press? (Or isn't 1,000,000 presses enough data to warrant drawing a > conclusion about the probability?). > > I would really like to know. There has been lots of research based on > the distinction between ignorance and probability, yet this is some > reasoning I have used to convince myself that the Bayesians are right. > > Thanks, > David > > -- > David Poole, Office: +1 (604) 822-6254 > Department of Computer Science, poole@cs.ubc.ca > University of British Columbia, http://www.cs.ubc.ca/spider/poole