On Mon, Feb 25, 2008 at 2:51 PM, Ed Porter <[EMAIL PROTECTED]> wrote: > But that does stop people from modeling systems in a simplified manner by > acting as if these limitations were met. Naïve Bayesian methods are > commonly used. I have read multiple papers saying that in many cases it > proves surprisingly accurate (considering what a gross hack it is) and, of > course, it greatly simplifies computation.
Admittedly, I do not have a quantitative grasp of Bayesian methods (naive or otherwise) but if I understand qualitatively it is about attempting to reach a conclusion based on complete knowledge based on a confidence of available knowledge to the unknown. If I'm already wrong, please school me. While walking the dog tonight I was considering the application of knowledge across different domains. In this light, I considered the unknown (or unknowable) part of the problem to be similar to some amount of chaos in a system that displays a gross-level order. Increasing the precision of the measurement of the ordered part can increase the instability of the chaotic part. Is it possible that a different kind of math is required to model the chaotic part of a complex system like this? Something as fundamental as the discovery of irrational numbers perhaps? This would have been yet another fleeting thought if I hadn't returned to this thread about Bayesian (thinking?) and I was curious what insight the list could offer... ------------------------------------------- agi Archives: http://www.listbox.com/member/archive/303/=now RSS Feed: http://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: http://www.listbox.com/member/?member_id=8660244&id_secret=95818715-a78a9b Powered by Listbox: http://www.listbox.com
