E: It was a quick one , huh ?
B: That 's why they invented " pocket T 's " .
E: They have clips !
A: exactly
E: Huh .
D: Yeah .
D: So , um  Recall that , uh , we want to have this kind of structure in our Bayes - nets .
D: Namely , that , um   You have these nodes that have several bands , right ?
D: So D: Does I mean , they sort of D: the typical example is that , um , these are all a bunch of cues for something ,
D: and this is a certain effect that we 'd like to conclude .
D: So , uh D: Like , let 's just look at the case when , um , this is actually the the final action , right ?
D: So this is like , uh , you know , touch ,
C: Y
D: or D: Sorry .
C: E - EVA
D: Uh
D: Yeah ,
D: E - EVA , right ?
C: Yeah .
D: Enter , V View , Approach , right ?
F: W what was this ?
F: It i i i ehhh , <comment> i ehhh .
D: So , this is B: Wri - write it out for for D: Yeah .
F: I mean D: Enter ,
D: View ,
D: Approach .
F: OK . Right .
D: Right .
D: So , I mean , we 'd like to take all these various cues , right ?
F: Like the army .
D: So this one might be , say , uh F: Yeah .
E: New terminology ?
D: Well , let me pick a random one
C: Hmm ?
E: I haven't heard that before .
D: and say , uh D: I don't know ,
D: it could be , like  This isn't the way it really is ,
D: but let me say that , suppose someone mentioned , uh , admission fees
D: Ah , it takes too long .
D: Try let me just say " Landmark " .
D: If the thing is a landmark , you know , um then there 's another thing that says if  um D: if it 's closed or not , at the moment .
D: Alright , so you have nodes .
D: Right ?
D: And the , uh , problem that we were having was that , you know , given N - nodes , there 's " two to the N "
D: 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 .
D: Right ?
D: That 's assuming these are all binary , which f they may not be .
D: For example , they could be " time of day " ,
D: in which case we could , uh , say , you know , " Morning , afternoon , evening , night " .
D: So , this could be more
D: So , it 's a lot , anyway .
D: And , that 's a lot of probabilities to put here , which is kind of a pain .
D: So Noisy - ORs are a way to , uh , sort of deal with this .
D: Um
D: Where should I put this ?
D: 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 .
D: The idea is to have these intermediate nodes .
D: Right .
D: Well , actually , the idea , first of all , is that each of these things has a quote - unquote distinguished state ,
D: which means that this is the state in which we don't really know anything about it .
D: So D: right ?
D: 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 .
D: It 's a really , you know , if there is something for the person talking about the admission fee ,
D: you know , if they didn't talk about it , that would be the Distinguish state .
D: So C: S so , this is a fanciful way of saying " default " ?
D: Yeah , yeah .
C: OK .
D: That 's just what they the word they used in that paper .
C: Mm - hmm .
D: So , the idea is that , um , you have these intermediate nodes , right ?
D: E - one , E - two , E - three and E - four ?
B: So , this is the Heckerman paper you 're working with ?
D: Yeah .
B: Good .
D: 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 ?
D: So , for example , suppose that the person I mean , suppose the thing that they talked about is a landmark .
D: But none of the other  sort of cues really apply .
D: Then , this would be D: W The this would just represent the probability distribution of this ,
D: assuming that this cue is turned on and the other ones just didn't apply ?
D: 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 .
D: 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 .
D: They want to enter it with probability , uh with probability point five
D: and they want to approach it probability point one , say D: Right ?
D: So we come up with these l little tables for each of those
D: OK .
D: And the final thing is that , um   this is a deterministic function of these ,
D: so we don't need to specify any probabilities .
D: We just have to , um , say what function this is , right ?
D: So we can let this be , um  G of E - one comma E - two . E - three , E - four . Right ?
D: and our example G would be , um , a majority vote ?
D: Right ?
B: Well . OK , so th so the important point is W not what the G function is .
B: The important point is that Um There is a a a general kind of idea of shortcutting the full CPT .
B: Th - c the full conditional probability table with some function .
B: OK ?
B: Which y w you choose appropriately for each case .
B: So , depending on what your situation is , there are different functions which are most appropriate .
B: And B: So I gave eh Bhaskara a copy of this , eh sort of " ninety - two " <comment> paper .
D: Mm - hmm .
B: D and you got one , Robert .
B: I don't know who else has seen it .
D: There 's I mean D: yeah .
D: it 's Heckerman and Breese .
B: It 's short .
B: It 's short .
D: Yeah .
B: So , I u w Um , yo uh you Have you read it yet ?
D: Uh , you can Yeah , you should take a look at it , I guess .
B: OK , so you should take a look .
A: OK
B: Nancy , I 'm sure you read it at some point in life .
E: I E: yeah .
E: I I think so , yeah .
B: OK .
B: And so , you other guys can decide how interested E: Yeah , @ @ .
B: Anyway . So the paper isn't th isn't real hard .
F: OK .
B: And  Uh B: One of the questions just come at Bhaskara is , " How much of this does JavaBayes support ? "
D: Yeah , it 's a good question .
D: Um  <nonvocalsound> The so what we want , is basically JavaBayes to support deterministic , uh , functions .
B: Right .
D: And , um  In a sense it sup we can make it supported by , um , manually , uh , entering , you know , probabilities that are one and zeros , right ?
B: Right . So the little handout that The little thing that I sent B: I sent a message saying , uh , here is a way to take B: One thing you could do , which is kind of s in a way , stupid , is take this deterministic function , and use it to build the CPT .
B: So , if Ba - JavaBayes won't do it for you ,
C: Mmm .
B: that you can convert all that into what the CPT would be .
B: Um and , what I sent out about a week ago , was an idea of how to do that , for , um , evidence combination .
B: So one of one function that you could use as your " G function " is an e e Evidence - Combining .
B: So you just take the uh , if each of th if each of the ones has its own little table like that , then you could take the , uh , strength of each of those , times its little table ,
B: and you 'd add up the total evidence for " V " , " E " , and " A " .
D: Mmm .
D: I don't think you can do this ,
E: Mm - hmm .
D: because  G is a function from that to that .
B: Yep .
B: Right .
D: Right ?
D: So there 's no numbers .
D: There 's just quadruplets of well , N - duplets of , uh , E Vs .
B: I i i
B: No , no But I 'm saying is There   There is a w
B: I mean , if y if if you decide what 's what is appropriate , is probablistic evidence combination , you can write a function that does it .
B: It 's a pui it 's actually one of the examples he 's got in there .
B: But , anyway , s skipping skipping the question of exactly which functions B: now is it clear that you might like to be able to shortcut the whole conditional probability table .
C: I mean , in some it seems very plausible in some sense , where we will be likely to not be observe some of the stuff .
C: Cuz we don't have the a access to the information .
D: Oops , <comment> sorry .
B: Right .
B: That 's one of the problems , is , W Is is , Where would th Where would it all come from ?
C: Yeah .
C: So .
D: Is E: Mmm .
D: Oh , right .
D: W would not be ab able to observe
D: What ?
C: I if it 's a a a discar Discourse Initial Phrase , we will have nothing in the discourse history .
C: So , if if we ever want to wonder what was mention
D: Oh D: Oh . A are you saying that we 'll not be able to observe certain nodes ?
D: That 's fine .
D: That is sort of orthogonal thing .