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CALL FOR DEMOS AND POSTERS - RTAS 2009
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
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
Dear Paul,
Your numerical application of Bayes rule is correct. Thus given your
model, your estimate is accurate assuming the numbers you assigned to
your prior and conditional probabilities are accurate for your
location.
However, you model the information provided by TWC as a binary
variable (E
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Call For Participation
What: IPSN '09 Extreme Sensing Competition
When: April
If TWC is really calibrated, then your conditions 5 and 6 are false, no?
On Feb 13, 2009, at 4:28 PM, Lehner, Paul E. wrote:
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
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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 prio
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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 fore
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
probabil
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