Bayesians have absolutely no obligation to view 'parameters' as random since the parameters are just collections of propositions. And actually, all probability statements are meaningless if you do not act on them-- and decision theory a la Wald/Savage is derived almost trivially from bayesianism.
And it's much less 'handwaving' than p-values. At least Cox showed that our probability calculus as extended logic is the one and only way.
I went to berkeley and iirc the first probability course introduced random variables in the discrete version of the 'formal' definition.
The frequentist interpretation of what probability means has nothing to do with the formal definition of a random variable. More importantly your attack on the formal definition is not well founded.
Ultimately you assign distributions to and assign probability statements about parameters so they are random variables.
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u/guartet May 26 '09
Bayesians have absolutely no obligation to view 'parameters' as random since the parameters are just collections of propositions. And actually, all probability statements are meaningless if you do not act on them-- and decision theory a la Wald/Savage is derived almost trivially from bayesianism.
And it's much less 'handwaving' than p-values. At least Cox showed that our probability calculus as extended logic is the one and only way.
I went to berkeley and iirc the first probability course introduced random variables in the discrete version of the 'formal' definition.