On Wed, 20 Feb 2008 18:27:09 +0100, cdrick wrote:
...
I think all this is premature optimization for me :) as I'm only
building an early prototype (I'm doing a start of Dempster Shafer
Theory [1] implementation (actually Transferable Belief Model)... and
it's won't reach a big size for a while. It allows to have an
imprecise, incomplete even uncertain value for a proposition (sort of
multi-valued attribute with confidence...).
Waaah, belief and plausibility as sum over numbers; shudder;
political-systems-failure through machine calculations; market-meltdown
through machine calculations; poverty-for-everyone through machine
calculations :( Anyways, have you compared to Pei Wang's NARS (or perhaps
his "The limitation of Bayesianism"), that would be interesting [OT].
Tried to convince him that fractions are sufficient for him but he liked
floats more (his early J* prototype had no system support for fractions
...).
Do you have calculations of your model's epsilon on which you base your
"imprecise", "uncertain", etc ? Or do you at present (for the prototype)
just stab in the dark.
I use it to get expert
opinion on values, it's a known technique for different captor data
fusion, but in my case, it doesn't demand too much performance as the
combination is not that important (compared to sensor data fusion) ;)
...
Cédrick
[1] http://en.wikipedia.org/wiki/Dempster-Shafer_theory
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