In what order shall we proceed ?
OK.
Maybe you have your on - line
Uh , yeah , sure. Um , so , I 've be just been looking at , um , Ack ! What are you doing ? Yeah. OK. Um , I 've been looking at the PRM stuff. Um , so , this is , sort of like the latest thing I have on it , and I sorta constructed a couple of classes. Like , a user class , a site class , and  and you know , a time , a route , and then  and a query class. And I tried to simplify it down a little bit , so that I can actually um , look at it more. It 's the same paper that I gave to Jerry last time. Um , so basically I took out a lot of stuff , a lot of the decision nodes , and then tried to  The red lines on the , um , graph are the um , relations between the different um , classes. Like , a user has like , a query , and then , also has , you know um , reference slots to its preferences , um , the special needs and , you know , money , and the user interest. And so this is more or less similar to the flat Bayes - net that I have , you know , with the input nodes and all that. And  So I tried to construct the dependency models , and a lot of these stuff I got from the flat Bayes - net , and what they depend on , and it turns out , you know , the CPT 's are really big , if I do that , so I tried to see how I can do , um  put in the computational nodes in between. And what that would look like in a PRM. And so I ended up making several classes  Actually , you know , a class of  with different attributes that are the intermediate nodes , and one of them is like , time affordability money affordability , site availability , and the travel compatibility. And so some of these classes are  s some of these attributes only depend on stuff from , say , the user , or s f just from , I don't know , like the site. S like , um , these here , it 's only like , user , but , if you look at travel compatibility for each of these factors , you need to look at a pair of , you know , what the um , preference of the user is versus , you know , what type of an event it is , or you know , which form of transportation the user has and whether , you know , the onsite parking matters to the user , in that case. And that makes the scenario a little different in a PRM , because , um , then you have one - user objects and potentially you can have many different sites in  in mind. And so for each of the site you 'll come up with this rating , of travel compatibility. And , they all depend on the same users , but different sites , and that makes a  I 'm tr I w I wa have been trying to see whether the PRM would make it more efficient if we do inferencing like that. And so , I guess you end up having fewer number of nodes than in a flat Bayes - net , cuz otherwise you would  c well , it 's probably the same. But um , No , you would definitely have  be able to re - use , like ,  um , all the user stuff , and not  not having to recompute a lot of the stuff , because it 's all from the user side. So if you changed sites , you  you can , you know , save some work on that. But , you know , in the case where , it depends on both the user and the site , then I 'm still having a hard time trying to see how um , using the PRM will help. Um , so anyhow , using those intermediate nodes then , this  this would be the class that represent the intermediate nodes. And that would  basically it 's just another class in the model , with , you know , references to the user and the site and the time. And then , after you group them together this  no the dependencies would  of the queries would be reduced to this. And so , you know , it 's easier to specify the CPT and all. Um , so I think that 's about as far as I 've gone on the PRM stuff.