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An in-depth exploration of probability theory, covering topics such as sample spaces, events, probability measures, and random variables. Learn about concepts like disjoint events, conditional probability, independence, discrete and continuous random variables, and more.
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Zahra Koochak and Jeremy Irvin
Sample Space Ω {HH, HT , TH, TT }
Sample Space Ω {HH, HT , TH, TT } Event A ⊆ Ω
Sample Space Ω {HH, HT , TH, TT } Event A ⊆ Ω {HH, HT }, Ω
Sample Space Ω {HH, HT , TH, TT } Event A ⊆ Ω {HH, HT }, Ω Event Space F
Sample Space Ω {HH, HT , TH, TT } Event A ⊆ Ω {HH, HT }, Ω Event Space F Probability Measure P : F → R P(A) ≥ 0 ∀A ∈ F
Sample Space Ω {HH, HT , TH, TT } Event A ⊆ Ω {HH, HT }, Ω Event Space F Probability Measure P : F → R P(A) ≥ 0 ∀A ∈ F P(Ω) = 1
Let B be any event such that P(B) 6 = 0.
Let B be any event such that P(B) 6 = 0. P(A|B) := P P(A(∩BB)) A ⊥ B if and only if P(A ∩ B) = P(A)P(B)
Let B be any event such that P(B) 6 = 0. P(A|B) := P P(A(∩BB)) A ⊥ B if and only if P(A ∩ B) = P(A)P(B) A ⊥ B if and only if P(A|B) = P(A∩B) P(B) =^ P(A)P(B) P(B) =^ P(A)
ω 0 = HHHTHTTHTT
ω 0 = HHHTHTTHTT A RV is X : Ω → R