Jan. 14 1.2 Properties of Probability Jan

Jan. 14 1.2 Properties of Probability Jan. 14 1.2 Properties of Probability

Event: A subset of the sample space (technical restrictions Algebra of sets and Venn diagrams unimportant for now) Null event and full sample space: ∅ and S Subset notation: A ⊂ B or, alternatively, B ⊃ A or A implies B Often use early-alphabet capital letters (e.g., A, B, C) for events. Union: A ∪ B Difference between an outcome and an event? Intersection: A ∩ B 0 c After an experiment, event A has occurred if . . . Complement: A or, alternatively, A

Special random variable: The indicator IA or I(A) or 1A

Jan. 14 1.2 Properties of Probability Jan. 14 1.2 Properties of Probability

Mutually exclusive events: Have empty pairwise intersection An example: Roll two dice, so S has 36 elements or outcomes. Exhaustive events: Have union equal to S Let A = {s ∈ S : sum of dice results in s is even} Partition: A group of mutually exclusive and exhaustive events B = {first die is even} (using abbreviated notation) C = {second die is ≥ 5} D = {sum is prime} What is A ∩ D? (B ∪ C0 ∪ A)0? (A0 ∩ B) ∪ (A ∩ B)? Jan. 14 Appendix A.1 Algebra of sets Jan. 14 Appendix A.1 Algebra of sets

Commutative property: A ∪ B = B ∪ A Commutative property: A ∩ B = B ∩ A Associative property: (A ∪ B) ∪ C = A ∪ (B ∪ C) Associative property: (A ∩ B) ∩ C = A ∩ (B ∩ C) Distributive property: A ∪ (B ∩ C) = (A ∪ B) ∩ (A ∪ C) Distributive property: A ∩ (B ∪ C) = (A ∩ B) ∪ (A ∩ C) DeMorgan’s Law: (A ∪ B)0 = A0 ∩ B0 or, more generally, DeMorgan’s Law: (A ∩ B)0 = A0 ∪ B0 or, more generally,

n !0 n n !0 n [ \ 0 \ [ 0 Ai = Ai Ai = Ai i=1 i=1 i=1 i=1

Jan. 14 1.2 Properties of Probability Jan. 14 1.2 Properties of Probability

Probability: Real-valued set function, P, satisfying: Some theorems: 0 P(A) ≥ 0 (Nonnegativity) (1) For any A, P(A) = 1 − P(A ). (2) P(∅) = 0. If A is the whole sample space S then P(A) = 1 (3) If A implies B (i.e., A ⊂ B), then P(A) ≤ P(B). Whenever A1, A2,... are mutually exclusive, (4) For any A, P(A) ≤ 1. ∞ ! ∞ (5) For any A and B, [ X P Ai = P(Ai ) (Countable additivity) P(A ∪ B) = P(A) + P(B) − P(A ∩ B) i=1 i=1 (5∗) For any A and B and C, P(A ∪ B ∪ C) = P(A) + P(B) + P(C) −P(A ∩ B) − P(A ∩ C) − P(B ∩ C) +P(A ∩ B ∩ C) ∗∗ (5 ) For any A1,..., An, n ! n [ X XX P Ai = P(Ai ) − P(Ai ∩ Aj ) + ··· i=1 i=1 i

Students will be able to: apply set notation to simplify expressions involving events; apply Venn diagrams to the same; prove simple facts based on the three axioms of probability;