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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 eff
On Tue, 12 Jul 2005 [EMAIL PROTECTED] wrote:
> Consider the following line of reasoning. Let p be the proposition
> "Ronald was born in New York." From p, we can infer q: Ronald was born
> in the United States.
> From q, we can infer r: It is possible that Ronald
> was born in New Jersey.
That's
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 unknown
Hi Marcus,
Indeed it is not a novel line of thought - you will find many related
ideas in the work of Jaynes, which proposes a form of (objective)
probability theory without the concept of randomness. I have also seen
arguments for interpretations of quantum theory without the concept of
rando
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
uot;, the former
encapsulates it while the latter does not - perhaps you can convince me
otherwise).
Regards,
Konrad
----
Dr Konrad Scheffler
Computer Science Division
Dept of Mathematical Sciences
University of Stellenbosch
+
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
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
ca
Dear Paul,
Bayesian inference is still appropriate for both problems. There are two
issues here:
1) the subjectivist Bayesian viewpoint is confusing because it does not
make it explicit on which information you are conditioning when setting
up your prior - it becomes much clearer if you
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 differe
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