On Mon, 23 Feb 2009, Francisco Javier Diez wrote:
Konrad Scheffler wrote:
I agree this is problematic - the notion of calibration (i.e. that you can
say P(S|70%) = .7) does not really make sense in the subjective Bayesian
framework where different individuals are working with different
Konrad Scheffler wrote:
I agree this is problematic - the notion of calibration (i.e. that you can
say P(S|70%) = .7) does not really make sense in the subjective Bayesian
framework where different individuals are working with different priors,
because different individuals will have different
, is it
also inappropriate here? Is my advice bad?
Paul
From: uai-boun...@engr.orst.edu [mailto:uai-boun...@engr.orst.edu]
On Behalf Of Lehner, Paul E.
Sent: Monday, February 16, 2009 11:40 AM
To: uai@ENGR.ORST.EDU
Subject: Re: [UAI] A perplexing problem - Version 2
UAI members
Thank you for your
ary 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. You
I agree this is problematic - the notion of calibration (i.e. that you can
say P(S|70%) = .7) does not really make sense in the subjective Bayesian
framework where different individuals are working with different priors,
because different individuals will have different posteriors and they
this bother anyone else?
paull
From: uai-boun...@engr.orst.edu [mailto:uai-boun...@engr.orst.edu]
On Behalf Of Lehner, Paul E.
Sent: Friday, February 13, 2009 4:29 PM
To: uai@ENGR.ORST.EDU
Subject: [UAI] A perplexing problem
I was working on a set of instructions to teach simple two-
hypothesis
is inappropriate for the TWC problem, is it also
inappropriate here? Is my advice bad?
Paul
From: uai-boun...@engr.orst.edu [mailto:uai-boun...@engr.orst.edu] On Behalf Of
Lehner, Paul E.
Sent: Monday, February 16, 2009 11:40 AM
To: uai@ENGR.ORST.EDU
Subject: Re: [UAI] A perplexing problem - Version 2
Of Lehner, Paul E.
Sent: Friday, February 13, 2009 4:29 PM
To: uai@ENGR.ORST.EDU
Subject: [UAI] A perplexing problem
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
Dear Paul,
if you consider TWC prediction as a part of the probabilistic model,
you get 4 probabilities for modelling a model which needs 3
probabilities to be specified.
(the model is given by the 2-way table given by (Snow/not snow and
snow prediction of 70%/not snow prediction of 70%).
:29 PM
To: uai@ENGR.ORST.EDU
Subject: [UAI] A perplexing problem
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
] 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
Peter Szolovits wrote:
If TWC is really calibrated, then your conditions 5 and 6 are false, no?
I agree with Peter's solution. If I build a model for this problem, it
must contain at least two variables: Snow and TWC_report. According with
my model, the TWC forecasts are calibrated if and
1. Note that you haven't really used the 70% at all. You could
restate the problem with any other statement you liked in there.
2. Your basic reasoning is correct. However, your modelling choice
seems poor. I would try replacing TWC forecasts 70% chance of
snow with TWC
Paul,
I'm not aware of this being discussed anywhere but my observation is
that the information given makes TWC quite lousy -- the probability of
the forecast 70% chance of snow is much too high when there is no
snow. It is a very specific piece of forecast and I would expect this
Hi Paul,
Your calculation is correct, but the numbers in the example are odd. If
TWC really only manage to predict snow 10% of the time (90% false negative
rate), you would be right not to assign much value to their predictions
(you do assign _some_, hence the seven-fold increase from your
Hi Paul,
Your calculations are correct (although I note you really mean
P(70%|not S) = 0.01 in the calc below).
^^^
Sometimes it helps to think about what the numbers actually
mean. First 0.05 prob of snow is quite a low prior.
You need to have quite certain evidence to move that up
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
17 matches
Mail list logo
