P Floding;180545 Wrote: 
> In fact, you have no safe way of estimating plausability when you have
> incomplete knowledge. And if you have complete knowledge you know if it
> is POSSIBLE or not. So much for that distinction.

That isn't entirely true, actually. Substitute probability of being
true for plausibility, and you've got something to work with. It's a
pretty hot research topic these days. That's what Bayesian statistics
is all about, and what other "machine learning" stuff is generally
about (this is what I do for a living, so I feel on pretty solid ground
on this one), and is also, AFAIK, the main thrust of "decision theory".


If you're interested, here are some classic references:

Judea Pearl: Probabilistic Reasoning in Intelligent Systems
http://www.amazon.com/Probabilistic-Reasoning-Intelligent-Systems-Plausible/dp/1558604790/sr=8-1/qid=1171477877/ref=sr_1_1/104-5904837-3949567?ie=UTF8&s=books

Pearl's new book on causation
http://bayes.cs.ucla.edu/BOOK-2K/why.html

Hastie and Tibshirani: Elements of Statistical Learning
http://www.amazon.com/Elements-Statistical-Learning-T-Hastie/dp/0387952845/sr=1-1/qid=1171477972/ref=pd_bbs_sr_1/104-5904837-3949567?ie=UTF8&s=books

Haven't read the new one on causality, but the other two are great: you
basically need undergrad level engineering math to get through them.


-- 
totoro

squeezebox 3 -> mccormack dna .5 -> audio physic tempo 4
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