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
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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
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Call For Participation
What: IPSN '09 Extreme Sensing Competition
When: April
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
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
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