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*From*: Brian Blais <bblais@bryant.edu>*Date*: Mon, 2 Sep 2013 19:07:19 +0000

On Sep 1, 2013, at 17:40 PM, John Denker wrote:

You can avoid both sets of problems by using the modern set-theoretic

definition of probability.

Hello John,

I've seen you make the references before, and was wondering what you think of E. T. Jaynes' approach to Bayesian inference. He does not make use of set-theoretic definitions, but in my reading of him, he does seem to admit that these have identical consequences in applications.

1) do you agree?

2) do you find some *quantitative* improvement using the set-theoretic definitions. I mean, is there an actual problem where one method works and the other not.

3) is there some *practical* improvement using the set-theoretic definitions. I mean, are there problems that are much easier to solve, even if both methods yield the same result in the end

4) am I missing any other difference which you consider important?

thanks,

Brian Blais

--

Brian Blais

bblais@bryant.edu

http://web.bryant.edu/~bblais

http://brianblais.wordpress.com/

**Follow-Ups**:**Re: [Phys-L] Bayesian statisitics***From:*John Denker <jsd@av8n.com>

**References**:**[Phys-L] Bayesian statisitics***From:*Dan Crowe <Dan.Crowe@lcps.org>

**Re: [Phys-L] Bayesian statisitics***From:*John Denker <jsd@av8n.com>

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