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<lavaflow>
if anybody here is familar with bayes theorem and willing to review my implementation of it, I would be very grateful: http://pasterack.org/pastes/47300
<lavaflow>
Specifically I'm trying to find the probability of A given a list of B's. To do this I apply bayes theorem between A and the first B in the list of Bs, then use that result as my next P(A) and apply bayes again recursively using the subsequent B's in the list of B's.
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<lavaflow>
I'm really not sure that's the right way to use bayes thereom though... it gives me unintuitive results, probabilities well over 1.0
<lavaflow>
the function that's giving me concern is the 10 line function 'bayes' at the top of the file. The rest of the file is basically just caching and plumbing, I'm pretty sure that part is fine.
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<nisstyre>
lavaflow: do you know the law of total probability?
<lavaflow>
nisstyre: I wasn't able to get the total probability rule generating the results I was looking for, but it occurred to me that my problem is very similar to using a naive bayes classifier to classify spam emails
<lavaflow>
instead of looking at each word in an email, I look at each tag on the file. I have one classifier trained for each possible tag, so I loop through all possible tags and see if the classifier for that tag returns a high probability for that file's existing tag-set.
<lavaflow>
(on the last page) I'm also going to try the other form which reputes to give better results though I'm skeptical. After I settle on one I think I'm going to try classifier chains (https://www.cs.waikato.ac.nz/~eibe/pubs/chains.pdf) to tackle label independance.
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