Well , let me pick a random one
I haven't heard that before.
and say , uh  I don't know , it could be , like   This isn't the way it really is , but let me say  that , suppose someone mentioned , uh , admission fees Ah , it takes too long. Try  let me just say " Landmark ". If the thing is a landmark , you know ,  um  then there 's another thing that says if   um  if it 's closed or not , at the moment. Alright , so you have nodes. Right ? And the , uh , problem that we were having was that , you know , given N - nodes , there 's " two to the N " Given N - nodes , and furthermore , the fact that there 's three things here , we need to specify " three times " , uh , " two to the N " probabilities. Right ? That 's assuming these are all binary , which f they may not be. For example , they could be " time of day " , in which case we could , uh , say , you know , " Morning , afternoon , evening , night ". So , this could be more So , it 's a lot , anyway. And , that 's a lot of probabilities to put here , which is kind of a pain. So  Noisy - ORs are a way to , uh ,  sort of deal with this. Um Where should I put this ? So , the idea is that , um ,  Let 's call these , uh , C - one , C - two , C - three , and C - four , and E , for Cause and Effect , I guess. The idea is to have these intermediate nodes. Right. Well , actually , the idea , first of all , is that each of these things has a  quote - unquote distinguished state , which means that this is  the state in which we don't really know anything about it. So  right ? So , for example , if we don't really know  if the thing is a landmark or not , Or , i if that just doesn't seem relevant , then that would be th sort of the Disting - the Distinguish state. It 's a really , you know ,  if there is something for the person talking about the admission fee , you know , if they didn't talk about it , that would be the Distinguish state.
S so , this is a fanciful way of saying " default " ?