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Mathematics · Ch 14 — Probability

Summary

Summary

  • Random experiment & sample space: A random experiment has multiple possible outcomes; the set of all outcomes is the sample space SS. An event is any subset of SS.
  • Types of events: Impossible event (∅\emptyset), sure event (SS), simple event (exactly one outcome), compound event (more than one outcome).
  • Algebra of events: For events AA and BB: complement A′A' or Aˉ\bar{A}, union A∪BA \cup B, intersection A∩BA \cap B, difference A−B=A∩B′A - B = A \cap B'. Events are mutually exclusive if A∩B=∅A \cap B = \emptyset; exhaustive if A∪B=SA \cup B = S.
  • Axiomatic probability: For each event AA, P(A)≥0P(A) \ge 0; P(S)=1P(S) = 1; for mutually exclusive events A1,A2,…A_1, A_2, \dots, P(⋃Ai)=∑P(Ai)P(\bigcup A_i) = \sum P(A_i).
  • Key formulas:
    • 0≤P(A)≤10 \le P(A) \le 1
    • P(∅)=0P(\emptyset) = 0
    • P(A′)=1−P(A)P(A') = 1 - P(A)
    • P(A∪B)=P(A)+P(B)−P(A∩B)P(A \cup B) = P(A) + P(B) - P(A \cap B)
  • Equally likely outcomes: If SS has nn equally likely outcomes, P(A)=number of outcomes in AnP(A) = \frac{\text{number of outcomes in } A}{n}.
  • Conditional probability: P(A∣B)=P(A∩B)P(B)P(A|B) = \frac{P(A \cap B)}{P(B)}, provided P(B)≠0P(B) \neq 0.
  • Multiplication theorem: P(A∩B)=P(A)⋅P(B∣A)=P(B)⋅P(A∣B)P(A \cap B) = P(A) \cdot P(B|A) = P(B) \cdot P(A|B).
  • Independent events: AA and BB are independent iff P(A∩B)=P(A)⋅P(B)P(A \cap B) = P(A) \cdot P(B). Independence implies P(A∣B)=P(A)P(A|B) = P(A) and P(B∣A)=P(B)P(B|A) = P(B).
  • Partition & total probability: If events E1,E2,…,EnE_1, E_2, \dots, E_n partition SS (mutually exclusive and exhaustive), then for any event AA: P(A)=∑i=1nP(Ei)⋅P(A∣Ei)P(A) = \sum_{i=1}^n P(E_i) \cdot P(A|E_i).
  • Bayes' theorem: For the same partition, P(Ek∣A)=P(Ek)⋅P(A∣Ek)∑i=1nP(Ei)⋅P(A∣Ei)P(E_k|A) = \frac{P(E_k) \cdot P(A|E_k)}{\sum_{i=1}^n P(E_i) \cdot P(A|E_i)}.
  • Random variable & probability distribution: A random variable XX assigns a real number to each outcome. Its probability distribution lists P(X=xi)P(X = x_i) for each value xix_i, with ∑P(X=xi)=1\sum P(X = x_i) = 1.
  • Mean & variance of a random variable:
    • Mean: μ=E(X)=∑xi⋅P(X=xi)\mu = E(X) = \sum x_i \cdot P(X = x_i) …