When we analyze uncertain things, we usually do not use the routine of getting the features first and then analyzing them, because that is hard, that is kind of clustering, when the dimention is more than 2, no human can do a good job. So we always use the routine of getting some features first, coming out with some proposition, and looking for evidence to prove our proposition is right or wrong. And in that routine features used to get the proposition is a start point with some probability. The evidence is Context into bearing.
Well it is intuitive, but it is hard to use in AI, Actually we are not thinking so accurately, so we find it is hard to define the amount of information of evidence, the relationship of evidence and the indepence of all the evidence. And the start point come to our head randomly, which make it a little hard to be copied.
HMM, Bayesian, Fuzzy logic, Evidence Theory, Possibility Theory and all the Fusion Theory and Filter Theory are used to handle this problem, HMM is a simplification of Bayesian. And Bayesian seem to lie in the theory and mathematical realm. Filter Theory ignores the give the relationship a fixed outline in time view, gives a obscure model. Fuzzy logic, Evidence Theory and Possibility Theory are all trying to decline the responsibility by just simulating the human. Fusion is just another way to look at all this things.
In the hello-world example, we are trying to say how possible the rain will stop Peter to go shopping, some say 0.8. some say 0.5. All this stuff make yourself not so confident and seriously doubt the correctness of your decision.
I guess that is why we are trying to train our guessing machines. and that is usually a task of ANN, which in its birth getting the knowledge from data, which is similar to humans. Finnaly this leads to the so-called research of Maching Learning, which is trying to extend the training idea, and trying to not so human-like.
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