Stochastic Processes Chapter 1 Introduction to Probability Theory 0. Reading: • Ross Chap 1 (main reading) • Rosenthal Chap 1 (a discussion on why we need measure theory, and the existence of a nonmeasurable set) • Shreve §1.1 (a deeper discussion on σ-algebra and countable additivity) 1. Formal definition of probability space (Ω, F, P) • Definition: The set of all possible outcomes of an experiment is called the sample space and is denoted by Ω. An outcome (sample point) is denoted by ω ∈ Ω. • Definition: A collection F of subsets of Ω is called a σ-filed (σ-algebra, filtration) if it satisfies the following conditions: (a) ∅ ∈ F; (b) if A1 , A2 , · · · ∈ F, then S∞ i=1 Ai ∈ F; (c) if A ∈ F, then Ac ∈ F. • Definition: A probability measure P on (Ω, F) is a function P : F → [0, 1] satisfying (a) 0 ≤ P(A) ≤ 1; (b) P(Ω) = 1; (c) if A1 , A2 , · · · is a collection of disjoint (mutually exclusive) members of F, in that Ai ∩ Aj = ∅ for all pairs i, j satisfying i 6= j, then P( ∞ [ Ai ) = i=1 • ∞ X P(Ai ). i=1 Discussion: (a) Why not “finitely additive” but “countably additive”? (b) What’s wrong with “uncountably additive”? (c) What is a “measure”? What is an event? What does “measurable” mean? 1 (d) How do you define a “random variable”? (usually denoted as X(ω)) 2. Unions and intersections • P(A ∪ B) = P(A) + P(B) − P(A ∩ B) if A and B are mutually exclusive, this becomes P(A ∪ B) = P(A) + P(B) • P(A ∩ B) = P(A)P(B|A) if A and B are independent, this becomes P(A ∩ B) = P(A)P(B) • conditional probability: P(B|A) = P(A∩B) P(A) if A and B are independent, then P(B|A) = P(B) • 3-event version: P(A ∪B ∪ C) = P(A)+P(B)+P(C)−P(A∩B)−P(B∩C)−P(A∩C)+P(A∩B∩C) P(A ∩ B ∩ C) = P(A)P(B|A) P(C|A ∩ B) | {z } P(A∩B) • general version: P(A1 ∪ A2 ∪ · · · ∪ An ) =⇒ inclusion-exclusion identity, see p.6, (1.4) P if mutually exclusive, then P(A1 ∪ A2 ∪ · · · ∪ An ) = ni=1 P(Ai ) (axiom 3) P(A1 ∩ A2 ∩ · · · ∩ An ) =⇒ see p.17, Exercise 23 Q if independent, then P(A1 ∩ A2 ∩ · · · ∩ An ) = ni=1 P(Ai ) (see p.11, first formula) 3. Bayes’ Formula S • Partition Ω = ni=1 Bi (B1 , · · · , Bn are mutually exclusive events) S • A = ni=1 (A ∩ Bi ) P P • P(A) = ni=1 P(A ∩ Bi ) = ni=1 P(Bi )P(A|Bi ) • P(Bj |A) = P(A ∩ Bj ) P(Bi )P(A|Bj ) = Pn P(A) i=1 P(Bi )P(A|Bi ) 2