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# lectures2 - MATH 235A – Probability Theory Lecture Notes...

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Unformatted text preview: MATH 235A – Probability Theory Lecture Notes, Fall 2009 Part II: Laws of large numbers Dan Romik Department of Mathematics, UC Davis Draft version of 11/2/2009 (minor typos corrected 11/5/09) Lecture 7: Expected values 7.1 Construction of the expectation operator We wish to define the notion of the expected value , or expectation , of a random variable X , which will be denoted E X (or E ( X )). In measure theory this is denoted R XdP and is called the “Lebesgue integral”. It is one of the most important concepts in all of mathematical analysis! So time invested in understanding it is time well-spent. The idea is simple. For bounded random variables, we want the expectation to satisfy three properties: First, the expectation of an indicator variable 1 A , where A is an event, should be equal to P ( A ). Second, the expectation operator should be linear i.e., should satisfy E ( aX + bY ) = a E X + b E Y for real numbers a,b and r.v.’s X,Y . Third, it should be monotone, i.e., if X ≤ Y (meaning X ( ω ) ≤ Y ( ω ) for all ω ∈ Ω) then E X ≤ E Y . For unbounded random variables, we will also require some kind of continuity, but let’s treat the case of bounded case first. It turns out that these properties determine the expec- tation/Lebesgue integral operator uniquely. Different textbooks may have some variation in how they construct it, but the existence and uniqueness are really the essential facts. 1 Theorem 1. Let (Ω , F , P ) be a probability space. Let B Ω denote the class of bounded random variables. There exists a unique operator E that takes a r.v. X ∈ B Ω and returns a number in R , and satisfies: 1. If A ∈ F then E (1 A ) = P ( A ) . 2. If X,Y ∈ B Ω , a,b ∈ R then E ( aX + bY ) = a E ( X ) + b E ( Y ) . 3. If X,Y ∈ B Ω and X ≥ Y then E ( X ) ≥ E ( Y ) . Sketch of proof. Call X a simple function if it is of the form X = ∑ n i =1 a i 1 B i , where a 1 ,...,a n ∈ R and B 1 ,...,B n are disjoint events. For such r.v.’s define E ( X ) = ∑ a i P ( B i ). Show that the linearity and monotonicity properties hold, and so far uniqueness clearly holds since we had no choice in how to define E ( X ) for such functions if we wanted the properties above to hold. Now for a general bounded r.v. X with | X | ≤ M , for any > 0 it is pos- sible to approximate X from below and above by simple functions Y ≤ X ≤ Z such that E ( Z- Y ) < . This suggests defining E ( X ) = sup { E ( Y ) : Y is a simple function such that Y ≤ X } . (1) By approximation, the construction is shown to still satisfy the properties in the Theorem and to be unique, since E ( X ) is squeezed between E ( Y ) and E ( Z ), and these can be made arbitrarily close to each other....
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lectures2 - MATH 235A – Probability Theory Lecture Notes...

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