Original Post
In this old thread (I'd post there and revive it, but it doesn't work, probably because of the forum upgrade) I got help on how to estimate imprecise probabilities. When I help someone with something I always think it's kind of annoying not to find out what the final solution turned out to be so heres my update :) This was done for my Master's thesis "Optimal occlusion testing" and I was trying to optimise occlusion culling by only performing the occlusion test for objects likely to be hidden. Actually it was a little more complicated since there was a utility function involved but that was the basic idea. I ended up using a Markov model due to it's computational efficiency and decent theoretical grounding in this particular case. To my (and my supervisors ;) great joy this technique did in fact offer a speed up compared to other heuristics i tested. I learned lots about imprecise probabilities along the way, coherent lower probabilities and interval probabilities among other things, but in the end they actually weren't suitable for my particualr problem. Anyway, thanks for the help, especially Timkin that got me exploring using a Markov model. If you're interested (though it's really more graphics than AI related) the thesis is available online here Optimal occlusion testing.