20e-lecture20 - Lecture 20 - Friday May 15th...

Info iconThis preview shows pages 1–2. Sign up to view the full content.

View Full Document Right Arrow Icon
Lecture 20 - Friday May 15th jacques@ucsd.edu Key words: Probability distribution function, Gaussian distribution Key concepts: Know how to show that f ( x ) = 1 2 π e - x 2 / 2 is a pdf. 20.1 Probability and Expectation Recall a function f : R 2 R defines a probability distribution function on R n if f ( x ) 0 for all x and Z Z ··· Z R n fdV = 1 . Underlying the probability distribution function is a probability measure defined by some experiment which gives an outcome to each point in R n . If X is the outcome of the experiment, then the probability distribution function gives P ( X λ ) = Z Z ··· Z E fdV where E = { x R n : X λ } – so E is the set of points in R n which witness the outcome of the experiment being at least λ . A basic example of a probability distribution function is the uniform distribution . Here we define f ( x,y ) = 1 for ( x,y ) D and D = [0 , 1] × [0 , 1] and f ( x,y ) = 0 otherwise. Then f ( x,y ) 0 for all ( x,y ) R 2 and RR D fdA = 1, so f is indeed a probability distribution function. Now suppose the experiment we perform is to measure the distance from a random point in D to the origin. Let X be the outcome of this experiment, so that X ( x,y ) = p x 2 + y 2 . Now consider the event X 1. The points which witness this outcome are the points (
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Image of page 2
This is the end of the preview. Sign up to access the rest of the document.

This note was uploaded on 02/12/2010 for the course MATH 20E taught by Professor Enright during the Spring '07 term at UCSD.

Page1 / 3

20e-lecture20 - Lecture 20 - Friday May 15th...

This preview shows document pages 1 - 2. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online