input: Text: Grad A: Yeah, I think I got my mike on. OK. Let's see. Professor B: OK. Ami, do yours then we 'll open it and I think it 'll be enough. Grad A: Mmm <unk>disfmarker<unk> Doesn't, uh <unk>disfmarker<unk> It should be the other way. Yeah, now it's on. PhD F: Right. OK. Professor B: OK. So, we all switched on? Grad A: We are all switched on, yeah. Professor B: Alright. Anyway. So, uh, before we get started with the, uh, technical part, I just want to review what I think is happening with the <unk>disfmarker<unk> our data collection. PhD F: We are all switched on. Professor B: So <unk>disfmarker<unk> <unk>vocalsound<unk> Uh, probably after today, <unk>vocalsound<unk> that shouldn't come up in this meeting. Th - this <unk>disfmarker<unk> this is s should be im it isn't <unk>disfmarker<unk> There's another thing going on of gathering data, and that's pretty much independent of this. But, uh, I just want to make sure we're all together on this. What we think is gonna happen is that, uh, in parallel starting about now <unk>vocalsound<unk> we're gonna get Fey <unk>vocalsound<unk> to, where you're working with me and Robert, draft a note that we're gonna send out to various CogSci c and other classes saying, " here's an opportunity to be a subject. Contact Fey. " And then there 'll be a certain number of um, hours during the week which she will be available and we 'll bring in people. Uh, roughly how many, Robert? We
  Question: What was the advantage of using Noisy-ORs?
  Answer:
output: The actual number of the inputs in the Bayes-net can create a combinatorial explosion when setting the probabilities. Noisy-OR's can help avoid this by simplifying the probability tables and applying a deterministic function to produce their complete version.