> Isn't there some way, if a "full curve" is too computationally > exensive, some way of expressing, say, 2 sigmas (standard deviations) > or whatever? E.g. 74% will fall within 1 standard dev. of optimum X?
We tried that, but generally, after a few inference iterations, the confidence intervals tend to become meaningless. What we do when we want pdf truth values is to use polynomials (so that, e.g. we can look at truth value distributions fitted by 10'th degree polynomials). Polynomials are nice in that they allow rapid algebraic manipulation. > Finally, isn't there some precise equation or set of equations you are > approximating? Sure, and I worked that out in detail, but it's not computationally feasible. You can take a possible worlds approach. In the case where the premises are consistent, for instance, you can look at all possible worlds consistent with the given premises. Then to find an unknown probability P(A|B) you average the value this would take in each of the possible worlds. If you have a priori knowledge about which possible worlds are more likely, you can use it too. This is a "correct" approach, but only computationally feasible for small problem cases. The math proofs of the Novamente inference rules explicitly involve an approximation to this approach. -- Ben ------- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/?[EMAIL PROTECTED]
