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V. Conditional Probability. Stochastic Independence

Sergei Fomin Probability Theory and It's Applications - W. Feller English 6 cards

Cards in this deck (6)

  1. 1P(A|H), where P(H) > 0P(A|H) = P(AH)/P(H)
  2. 2Law of total probabilitiesLet Hi be a set of mutually exclusive events of which one necessarily occurs. Let A be any event. Then:
    P(A) = Sum [ P(A|Hi)*P(Hi) ]
  3. 3Bayes's rule: P(Hk|A)P(Hk|A) = P(A|Hk)*P(Hk) / (Sum[P(A|Hi)*P(Hi)])
  4. 4Stocahstic independence of events A and HA and H are called stocahstically independent if:
    P(AH) = P(A)*P(H)
  5. 5Mutual independence if events A1, A2, ..., AnThe events A1, ..., An are called mutually independent it:
    P(AiAj) = P(Ai)*P(Aj)
    P(AiAjAk) = P(Ai)*P(Aj)*P(Ak)
    ...
    P(A1A2...An) = P(A1)*P(A2)*...*P(An)
  6. 6Combinatorial product of two sample spacesLet Q be a sample space consisting of events Xi with probabilities pi, and M be a sample space of events Yi with probabilities qi. Then G is a combinatorial product of Q and M if it consists of events (Xi, Yj) and P{(Xi, Yj)} = pi*qj

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Probability Theory and It's Applications - W. Feller Flashcards on V. Conditional Probability. Stochastic Independence, created by Sergei Fomin on 22/08/2017.
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