This preview shows pages 1–3. Sign up to view the full content.
This preview has intentionally blurred sections. Sign up to view the full version.View Full Document
Unformatted text preview: O.H. Probability and Markov Chains – MATH 2561/2571 E09 Construction of the Lebesgue integral (sketch) The material of this section is optional and thus for fun, not for exam! A.1 Measures and measure spaces Let E be a (fixed) set. We start by recalling the following definitions. Definition A.1. Let A be a collection of subsets of E . We shall call A a field if it has the following properties: 1. ∅ ∈ A ; 2. if A 1 , A 2 ∈ A , then A 1 ∪ A 2 ∈ A ; 3. if A ∈ A , then A c ∈ A . Remark A.1.1. Obviously, every field is closed w.r.t. taking finite unions or inter- sections. Definition A.2. Let A be a collection of subsets of E . We shall call A a σ-field if it has the following properties: 1. ∅ ∈ A ; 2. if A 1 , A 2 , ··· ∈ A , then S ∞ k =1 A k ∈ A ; 3. if A ∈ A , then A c ∈ A . Remark A.2.1. Obviously, property 2 above can be replaced by the equivalent con- dition T ∞ k =1 A k ∈ A . Definition A.3. Let C be a collection of subsets of E . The intersection of all σ-fields containing C shall be called the σ-field generated by C and denoted σ ( C ) . In other words, every collection C of subsets of E can be completed to a σ-field in a unique way. An important example . Let C be the collection of all open intervals in R 1 , C = n ( a,b ) : −∞ < a < b < ∞ o . Definition A.4. The Borel σ-field in R 1 is B 1 = σ ( C ) . Denote C 1 = n [ a,b ) : −∞ < a < b < ∞ o , C 2 = n [ a, ∞ ) : −∞ < a < ∞ o , C 3 = n ( a, ∞ ) : −∞ < a < ∞ o . Exercise A.5. Show that σ ( C 1 ) = σ ( C 2 ) = σ ( C 3 ) = B 1 . Definition A.6. Let C be a collection of subsets of E . Any set function μ : C → R + ≡ [0 , ∞ ] is called σ-additive or a measure , if 1. μ ( ∅ ) = 0 ; 1 O.H. Probability and Markov Chains – MATH 2561/2571 E09 2. every sequence A k , k ∈ N , of disjoint sets A k ∈ C ( k ∈ N ) satisfying ∪ ∞ k =1 A k ∈ C has the following property μ “ ∞ [ k =1 A k ” = ∞ X k =1 μ ` A k ´ . Recall that a probability measure P on a measurable space (Ω , F ) is a σ-additive set function P : F → [0 , 1] such that P (Ω) = 1. Remark A.6.1. Observe that in general the condition ∪ ∞ k =1 F k ∈ C cannot be dropped. Theorem A.7 (Carath´ eodory) . Let A be a field of subsets of E and let μ be a σ-additive map μ : A → [0 , ∞ ] . If D = σ ( A ) , there exists a measure ¯ μ on D such that A ∈ A = ⇒ ¯ μ ( A ) ≡ μ ( A ) . If μ ( E ) < ∞ , then ¯ μ is unique. In particular, if A is a field of subsets of Ω and μ : A → [0 , 1] is a σ-additive set function such that μ (Ω) = 1, then there exists a unique extension P of μ to the generated σ-field F = σ ( A ). Using the Carath´ eodory theorem one can construct various probability spaces (Ω , F , P )....
View Full Document
- Spring '10