prob (1)

# prob (1) - Probability Theory Richard F. Bass These notes...

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Unformatted text preview: Probability Theory Richard F. Bass These notes are c 1998 by Richard F. Bass. They may be used for personal or classroom purposes, but not for commercial purposes. Revised 2001. 1. Basic notions. A probability or probability measure is a measure whose total mass is one. Because the origins of probability are in statistics rather than analysis, some of the terminology is different. For example, instead of denoting a measure space by ( X, A ,μ ), probabilists use (Ω , F , P ). So here Ω is a set, F is called a σ-field (which is the same thing as a σ-algebra), and P is a measure with P (Ω) = 1. Elements of F are called events . Elements of Ω are denoted ω . Instead of saying a property occurs almost everywhere, we talk about properties occurring almost surely , written a.s. . Real-valued measurable functions from Ω to R are called random variables and are usually denoted by X or Y or other capital letters. We often abbreviate ”random variable” by r.v. We let A c = ( ω ∈ Ω : ω / ∈ A ) (called the complement of A ) and B- A = B ∩ A c . Integration (in the sense of Lebesgue) is called expectation or expected value , and we write E X for R Xd P . The notation E [ X ; A ] is often used for R A Xd P . The random variable 1 A is the function that is one if ω ∈ A and zero otherwise. It is called the indicator of A (the name characteristic function in probability refers to the Fourier transform). Events such as ( ω : X ( ω ) > a ) are almost always abbreviated by ( X > a ). Given a random variable X , we can define a probability on R by P X ( A ) = P ( X ∈ A ) , A ⊂ R . (1 . 1) The probability P X is called the law of X or the distribution of X . We define F X : R → [0 , 1] by F X ( x ) = P X ((-∞ ,x ]) = P ( X ≤ x ) . (1 . 2) The function F X is called the distribution function of X . As an example, let Ω = { H,T } , F all subsets of Ω (there are 4 of them), P ( H ) = P ( T ) = 1 2 . Let X ( H ) = 1 and X ( T ) = 0. Then P X = 1 2 δ + 1 2 δ 1 , where δ x is point mass at x , that is, δ x ( A ) = 1 if x ∈ A and 0 otherwise. F X ( a ) = 0 if a < 0, 1 2 if 0 ≤ a < 1, and 1 if a ≥ 1. Proposition 1.1. The distribution function F X of a random variable X satisfies: (a) F X is nondecreasing; (b) F X is right continuous with left limits; (c) lim x →∞ F X ( x ) = 1 and lim x →-∞ F X ( x ) = 0 . Proof. We prove the first part of (b) and leave the others to the reader. If x n ↓ x , then ( X ≤ x n ) ↓ ( X ≤ x ), and so P ( X ≤ x n ) ↓ P ( X ≤ x ) since P is a measure. Note that if x n ↑ x , then ( X ≤ x n ) ↑ ( X < x ), and so F X ( x n ) ↑ P ( X < x ). Any function F : R → [0 , 1] satisfying (a)-(c) of Proposition 1.1 is called a distribution function, whether or not it comes from a random variable....
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## This note was uploaded on 01/02/2012 for the course FINANCE 347 taught by Professor Bayou during the Fall '11 term at NYU.

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prob (1) - Probability Theory Richard F. Bass These notes...

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