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Probability
Marginal Probability: P(X)
Conditional Probability: P(X|Y)
Joint Probability: P(X,Y)
Total Probability:
Bayesian Rule:
Conditional Independence (i.i.d)
Example 1:
Example 2:
Example 3:
Random Variable

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Machine Learning

ML Notes

Bayesian Classifier

Terminology Review

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Last updated 3 years ago

Probability

Marginal Probability: P(X)

Conditional Probability: P(X|Y)

with Chain rule:

P(A,B|C)=P(A|B,C) P(B|C)

Joint Probability: P(X,Y)

Total Probability:

P(A)=P(A,B1)+P(A,B2)+...+P(A,Bn)= sum(P(A|Bk)P(Bk))

Bayesian Rule:

Conditional Independence (i.i.d)

Example 1:

1. 공장에서 A,B,C 종류의 기계를 사용하는데 각 생산하는 제품의 양은 50%, 30%, 20%이다. 각 기계종류별 불량률은 1%, 2%, 3%임. 임의의 제품 1개가 불량품일 활률은?

2. 그 불량 제품이 기계A 에서 생산한 물건일 확률은?

Solution

Example 2:

Example 3:

Random Variable