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
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
Paul, your restated problem reminds me of one I encountered in
medicine in the 1980's. When an internist sends a patient's sample to
a pathologist and the pathologist says 90% chance of cancer, how is
the internist supposed to interpret that answer in light of his own
priors?
This time, the probabilistic model is underspecified, since it has 2
probabilities,
but it is not important for answering the question since the answer to
question 1 is is propositions 3 et 4:
if TWC forecasts are calibrated then P(S/70%) = 70%, and prior 2 plays
no role.
You find this
UAI members
Thank you for your many responses. You've provided at least 5 distinct answers
which I summarize below.
(Answer 5 below is clearly correct, but leads me to a new quandary.)
Answer 1: 70% chance of snow is just a label and conceptually should be
treated as XYZ. In other words
