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I need advice and some non-epistemic references:

I'm writing my new book on “computer networks performance analysis”. In the first and second chapters I had to discuss the concepts of Queuing theory, Birth and Death model and infinite variance stochastic process and I did of course! But after considerable research and thought on the probabilistic nature of some phenomena, I was suddenly dumbfounded by a question:

Where does “a priori knowledge” come from especially in statistical presuppositions and reasoning?

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Theoretical knowledge of the process and empirical observation? Am I missing something? – Emre Apr 28 '12 at 22:50
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Take your time when you write this part of the book ... and perhaps try to find better sources than stackexchange – user10525 Apr 28 '12 at 23:02
This was actually a question by a friend of mine who was looking for a conceptual reasoning about the a priori knowledge. I don't know what is not clear for him though. Thanks. – Isaac Apr 29 '12 at 12:57
What do you mean by "non-epistemic"? More generally, I think you will need to scope down your question a little if you want a good answer on this. – Peter Ellis Apr 30 '12 at 10:30

closed as not a real question by rolando2, gung, Macro, Peter Flom, steffen Oct 16 '12 at 11:15

It's difficult to tell what is being asked here. This question is ambiguous, vague, incomplete, overly broad, or rhetorical and cannot be reasonably answered in its current form. For help clarifying this question so that it can be reopened, see the FAQ.

1 Answer

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In some practical Bayesian problems this is called eliciting prior distributions. Instead of assuming a particular prior distribution some Bayesians interview experts to get their probability assessment for the random outcome. Usually there is a series of questions that do not directly ask "what is the probability that ...." but rather infer the probability based on the answer to the questions.

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