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On this page
  • 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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  1. Machine Learning
  2. ML Notes
  3. Bayesian Classifier

Terminology Review

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Last updated 3 years ago

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Probability

Marginal Probability: P(X)

Conditional Probability: P(X|Y)

with Chain rule:

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