Re: [UAI] A perplexing problem

[Alexandre Saidi]

Dear All, We may not compare "70%" of TWC prediction with Paul's 34%. Simply beacause, as Paul assumed , TWC is right only at a ratio of 1/10 (their 70% "prediction" happens at 10%,  true positive compared to   1% "false positive") ! Given the uncertainty of TWC "70%" predictions,  the Paul's 34%  would have its own uncertainty (belief) that will be observable from the feedback. One is often surprized for the same reasons as Paul. That was my case  in the "Alarm/Earthquake.." problem from many sources, e.g. P. Norvig & S. Russel. regards. Alex Le 16/02/09 20:49, Agosta, John M a écrit : All - The "Bayes ratio" (or odds ratio) interpretation of Bayes rule is enlightening, since it reveals the strength of evidence in a way not clear from just looking at the probabilities. A 5% prior chance becomes odds of 1:19 against snow. With Paul's assigned sensitivity (probability of snow forecast given it will snow) of 10%, the evidence of a positive forcast has an odds ratio of 10:1 in favor of snow. Expressed, for instance in a scale suggested by Kass & Raftery this counts as not particularly strong positive evidence. Not surprisingly the combination of 1:19 prior against and a 10:1 odds for results in less than even odds for snow. ___ John Mark Agosta, Intel Research -----Original Message----- From: uai-boun...@engr.orst.edu [mailto:uai-boun...@engr.orst.edu] On Behalf Of Paul Snow Sent: Monday, February 16, 2009 3:24 AM To: uai@engr.orst.edu Subject: Re: [UAI] A perplexing problem Dear Paul, If the Weather Channel is Bayesian, then say they used that empricial prior that you did (5%), and they observed evidence E to arrive at their 70% for the snow S given E. Their Bayes' ratio is 44.3. Yours, effectively, is 10 (assuming that the event "They say 70%" coincides with "They observe evidence with a Bayes ratio in the forties" - that is, they agree with you about the empirical prior and are Bayesian). So, having effectively disagreed with them about the import of what they observed, you also disagreed with them about the conclusion. Hope that helps, Paul 2009/2/13 Lehner, Paul E. <pleh...@mitre.org>: I was working on a set of instructions to teach simple two-hypothesis/one-evidence Bayesian updating. I came across a problem that perplexed me. This can't be a new problem so I'm hoping someone will clear things up for me. The problem 1. Question: What is the chance that it will snow next Monday? 2. My prior: 5% (because it typically snows about 5% of the days during the winter) 3. Evidence: The Weather Channel (TWC) says there is a "70% chance of snow" on Monday. 4. TWC forecasts of snow are calibrated. My initial answer is to claim that this problem is underspecified. So I add 5. On winter days that it snows, TWC forecasts "70% chance of snow" about 10% of the time 6. On winter days that it does not snow, TWC forecasts "70% chance of snow" about 1% of the time. So now from P(S)=.05; P("70%"|S)=.10; and P("70%"|S)=.01 I apply Bayes rule and deduce my posterior probability to be P(S|"70%") = .3448. Now it seems particularly odd that I would conclude there is only a 34% chance of snow when TWC says there is a 70% chance. TWC knows so much more about weather forecasting than I do. What am I doing wrong? Paul E. Lehner, Ph.D. Consulting Scientist The MITRE Corporation (703) 983-7968 pleh...@mitre.org _______________________________________________ uai mailing list uai@ENGR.ORST.EDU https://secure.engr.oregonstate.edu/mailman/listinfo/uai _______________________________________________ uai mailing list uai@ENGR.ORST.EDU https://secure.engr.oregonstate.edu/mailman/listinfo/uai _______________________________________________ uai mailing list uai@ENGR.ORST.EDU https://secure.engr.oregonstate.edu/mailman/listinfo/uai -- Alexandre Saidi Maitre de Conférences Ecole Centrale de Lyon-Dép. MI Tél : 0472186530, Fax : 0472186443

begin:vcard
fn:A-S Saidi
n:A-S Saidi;A-S
org:Ecole Centrale de Lyon;LIRIS-UMR 5205 CNRS
adr;quoted-printable:;;36 Av. Guy de Collongue;Ecully;Rh=C3=B4ne;69134;France
email;internet:alexandre.sa...@liris.cnrs.fr
title;quoted-printable:Ma=C3=AEtre de Conf=C3=A9rences
tel;work:04 72 18 65 30
tel;fax:04 72 18 64 43
x-mozilla-html:TRUE
version:2.1
end:vcard


_______________________________________________
uai mailing list
uai@ENGR.ORST.EDU
https://secure.engr.oregonstate.edu/mailman/listinfo/uai