Technical overview of the ratings system: Difference between revisions
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0 [label="0, P=50%"] |
0 [label="0, P=50%"] |
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1 [label="1, P= |
1 [label="1, P=60%"] |
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2 [label="2, P= |
2 [label="2, P=80%"] |
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0 -> 1 [label="T=1.0",dir="both"]; |
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0 -> 2 [label="T=1.0",dir="both"]; |
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0 -> 1 [label="T=0.0",dir="both"]; |
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0 -> 2 [label="T=0.0",dir="both"]; |
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0 -> 3 [label="T=0.0",dir="both"]; |
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0 -> 4 [label="T=1.0",dir="both"]; |
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Revision as of 20:56, 26 August 2024
Let's take a very simple situation. You want to know whether it is going to rain tomorrow. You don't know so you ask two knowledgeable sources this question. One of them believes it will rain with a probability of 60%. The other believes it will rain with a probability of 80%. We can sketch this situation as follows:
You combine these probabilities using some aggregation technique. One such technique is Bayes' equation:
Pcomb = 0.6*0.8/(0.6*0.8 + 0.4*0.2) = 0.857
Another technique is a straight average of the two answers:
Pcomb = (0.6 + 0.8) / 2 = 0.7