So , this is the Heckerman paper you 're working with ? Good.
Yeah. So  The idea is that , each of these EI  is   represents what this would be  if all the other ones were in the distinguish state. Right ? So , for example , suppose that the person  I mean , suppose the thing that they talked about is a landmark. But none of the other   sort of cues really apply. Then ,  this would be  W The  this would just represent the probability distribution of this , assuming that this cue is turned on and the other ones just didn't apply ? So , you know , if it is a landmark , and no none of the other things really ap applicable , then  this would represent the probability distribution. So maybe in this case   Maybe we just t k Maybe we decide that , if the thing 's a landmark and we don't know anything else , then we 're gonna conclude that , um   They want to view it with probability , you know , point four. They want to enter it with probability , uh  with probability point five and they want to approach it probability point one , say  Right ? So we come up with these l little tables for each of those OK. And the final thing is that , um    this is a deterministic function of these , so we don't need to specify any probabilities. We just have to , um , say what function this is , right ? So we can let this be , um   G of E - one comma E - two. E - three , E - four. Right ? and our example G would be , um ,  a majority vote ? Right ?
Well. OK , so th so the important point  is  W not what the G function is. The important point is  that  Um  There is a  a  a general kind of idea of shortcutting the full CPT. Th - c the full conditional probability table  with some function. OK ? Which y w you choose appropriately for each case. So , depending on  what your situation is , there are different functions which are most appropriate. And  So I gave  eh  Bhaskara a copy of this , eh  sort of " ninety - two "  paper. D and you got one , Robert.
Mm - hmm.