DiscreteProbability

# DiscreteProbability - y n . • Assume there are m input...

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Basic Probability (most slides borrowed with  permission from Andrew Moore of  CMU and Google) http://www.cs.cmu.edu/~awm/tutorials

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A
A B

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Notation Digression P(A) is shorthand for P(A=true) P(~A) is shorthand for P(A=false) Same notation applies to other binary RVs: P(Gender=M), P(Gender=F) Same notation applies to  multivalued  RVs: P(Major=history), P(Age=19), P(Q=c) Note: upper case letters/names for  variables , lower case letters/names for  values For multivalued RVs, P(Q) is shorthand for

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k k
k k

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k k

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R Q S P(H|F) = R/(Q+R) P(F|H) = R/(S+R)

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Unformatted text preview: y n . • Assume there are m input attributes called X 1 , X 2 , X 3 , … X m . • For each of n values y i , build a density estimator, D i , that estimates P(X 1 , X 2 , X 3 , … X m |Y=y i ) • Y predict = argmax y P(Y=y| X 1 , X 2 , X 3 , … X m ) = argmax y P(X 1 , X 2 , X 3 , … X m | Y=y) Machine Learning Vs. Cognitive Science • For machine learning, density estimation is required to do classification, prediction, etc. • For cognitive science, density estimation under a particular model is a theory about what is going on in the brain when an individual learns....
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## This note was uploaded on 10/24/2010 for the course CSCI 4202 at Colorado.

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DiscreteProbability - y n . • Assume there are m input...

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