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Konrad Scheffler
University of California, San Diego
http://id.ucsd.edu/faculty
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
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
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
not - perhaps you can convince me
otherwise).
Regards,
Konrad
Dr Konrad Scheffler
Computer Science Division
Dept of Mathematical Sciences
University of Stellenbosch
+27-21-808-4306
http://www.cs.sun.ac.za/~kscheffler/
On Mon, 21
CV (which should include information on your most advanced
computer programming project to date) and a covering letter to Dr Konrad
Scheffler ([EMAIL PROTECTED]). Alternatively, please get in touch by
e-mail or phone (021 808 4306) to request more information about the
project
Hmm, no takers on this one yet?
I'll rephrase the problem in a way that makes more sense to me (since
the original contains words I don't know the meaning of):
X and Y are unknown variables taking values in the set (1, 2, ..., n). The
entries in the joint probability matrix, P, are
Hi Rich,
In your analysis you present a frequentist and a Bayesian approach,
arguing that the paradox exists only for the frequentist case. Fair
enough. I would just like to point out that the frequentist approach
(orthodox hypothesis testing) is even more problematic than that, in that
it