DiscreteProbability

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

Info iconThis preview shows pages 1–68. Sign up to view the full content.

View Full Document Right Arrow Icon
Basic Probability (most slides borrowed with  permission from Andrew Moore of  CMU and Google) http://www.cs.cmu.edu/~awm/tutorials
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Background image of page 2
Background image of page 3

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Background image of page 4
Background image of page 5

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Background image of page 6
Background image of page 7

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
A
Background image of page 8
A B
Background image of page 9

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
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 
Background image of page 10
Background image of page 11

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
k k
Background image of page 12
k k
Background image of page 13

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
k k
Background image of page 14
Background image of page 15

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Background image of page 16
Background image of page 17

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Background image of page 18
Background image of page 19

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
R Q S P(H|F) = R/(Q+R) P(F|H) = R/(S+R)
Background image of page 20
Background image of page 21

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Background image of page 22
Background image of page 23

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Background image of page 24
Background image of page 25

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Background image of page 26
Background image of page 27

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Background image of page 28
Background image of page 29

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Background image of page 30
This is the end of the preview. Sign up to access the rest of the document.

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....
View Full Document

This note was uploaded on 10/24/2010 for the course CSCI 4202 at Colorado.

Page1 / 68

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

This preview shows document pages 1 - 68. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online