15 338995 2 p ln 12 so we obtain s 000405 170455

Info iconThis preview shows page 1. Sign up to view the full content.

View Full Document Right Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: ma We further know, f A a forallu fU (u ) da du Using this equation, we get the density function as, a a 1 fU f A (a) fU m 2 ma m 1 a exp for a 0 2ma 2m Similarly, for B f B (b) 1 b exp 2mb 2m for b 0 These are Chi-square distribution with one degree of freedom. Now, taking b k a , k a 1 1 a 2m 1 2m e e da a 2m 0 k a k fK ( k ) 1 k 2m 1 / 2 1 / 2 e a k a da 2m 0 k Then taking r a / k i.e. da kdr 1 fK ( k ) 1 k 2m 1 / 2 1 / 2 e r 1 r dr 2m 0 The integral part of this equation is the Beta function B 1 ,1 . 22 1 since , 2 Hence f K (k ) B 1 ,1 22 1 1 2 2 1 1 k 2m e is a Chi-square distribution with two degrees of freedom. 2m 7. Concluding Remarks In this lecture, different statistical properties of one function of two random variables, viz. sum and difference of independent Normal variates, maximum, minimum product and quotient) are discussed. It is important to remember that the sum of two independent Gaussian random variates also possesses a normal density function with mean and variance derived a sum and square of the sum of individual mean and variance respectively. The same is true for the difference of two independent Gaussian random variates. Also to note that the expression for quotient of two random variables can be derived using the same principles followed in the case of product of two random variables. Two functions of two random variables will be discussed in next lecture, to explain the theories further....
View Full Document

This note was uploaded on 03/18/2014 for the course CE 5730 taught by Professor Dr.rajibmaity during the Spring '13 term at Indian Institute of Technology, Kharagpur.

Ask a homework question - tutors are online