Tying together recent threads on indefinite probabilities and prior
distributions (PI, maxent, Occam), I thought I'd make a note on the
relation between the two topics.
In the indefinite probability approach, one assigns a statement S a
truth value L,U,b,k denoting one's attachment of
gts wrote:
On Sat, 10 Feb 2007 13:41:33 -0500, Richard Loosemore
[EMAIL PROTECTED] wrote:
The meat of this argument is all in what exact type of AGI you claim
is the best, of the two suggested above.
The best AGI in this context would be one capable of avoiding the
conjunction fallacy, of
Benjamin Goertzel wrote:
Tying together recent threads on indefinite probabilities and prior
distributions (PI, maxent, Occam), I thought I'd make a note on the
relation between the two topics.
In the indefinite probability approach, one assigns a statement S a
truth value L,U,b,k denoting
Eliezer,
Ben, is the indefinite probability approach compatible with local
propagation in graphical models?
Hmmm... I haven't thought about this before, but on first blush, I don't
see any reason why you couldn't locally propagate indefinite
probabilities through a Bayes net...
We
Yesterday I received from amazon.com a copy of Cox's book _The Algebra of
Probable Inference_. (Thanks for the recommendation, Ben.)
In his preface Cox expresses his indebtedness to Keynes, and Keynes'
influence is obvious throughout. For this reason I was expecting to find
somewhere
