Null hypothesis and Bayes

Hello again!

I would like to start another thread on null hypothesis and Bayesian
statistics. Neyman-Pearson approach does not tell us what we are
usually intersted in i.e. P[H0|data], but it gives us P[data|H0], which
is not the same thing.

OK, but I am stumbled upon Bayesian approach with "noninformative"
priors i.e. Bayes theorem is

P[H0|data] = P[data|H0] P[H0] / P[data]

and if we use "noninformative" prior P[H0] = const and taking into
aknowledge proportionality on P[data] we get

P[H0|data] \propto P[data|H0]

So P[H0|data] is proportional to Neyman-Pearson result. Should one
interpret P[H0|data] as P[data|H0] or what? I get lost here.

Any comments are more tha welcome! Anon?

--
Lep pozdrav / With regards,
Gregor Gorjanc

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University of Ljubljana PhD student
Biotechnical Faculty
Zootechnical Department URI: http://www.bfro.uni-lj.si/MR/ggorjan
Groblje 3 mail: gregor.gorjanc <at> bfro.uni-lj.si

SI-1230 Domzale tel: +386 (0)1 72 17 861
Slovenia, Europe fax: +386 (0)1 72 17 888

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"One must learn by doing the thing; for though you think you know it,
you have no certainty until you try." Sophocles ~ 450 B.C.
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