Poisson distribution function, Civil Engineering

Poisson distribution function:

Let XI, X2, ..., X,& b e n independently and identically distributed random variables each having the same cdf F ( x ). What is the pdf of the largest of the xi'?


Let Y = maximum (XI, X2, ..., Xn, )

Since Y ≤ y implies Xl ≤ y, X2 ≤y, ..., Xn ≤ y, we have

Fy(y )= P(Y ≤ y) = P(XI ≤ y,X2 ≤ y, ..., Xn ≤ y )

= P(X1 ≤ y) P(X2 ≤ y) ... P(Xn ≤ y),

since XI, X2, ..., Xn, are independent

= {F(y)}n, since the cdf of each Xi is F(x).

 Hence the pdf of Y is

fy (y) = F'(y) = n{F(y)}n-1 f(y),

where f ( y ) = F'( y ) is the pdf of Xi.

6.2.2 The Method of Probability Density Function (Approach 2)

For a univariate continuous random variable x having the pdf' fx ( x ) and the cdf Fx (x), we have

F'x(x)= (d/dx ) dFx(x) or dFx(x) = fx (x) dx

In other words, differential dFx (x) represents the element of probability that X assumes a value in an infinitesimal interval of width dx in the neighbourhood of X = x.

For a one-to-one transformation y = g ( x ), there exists an inverse transformation x = g - 1 ( y ), so that under the transformation as x changes to y, dx changes to dg-1(y)/dy and

dF (x) = f(x) dx = fx (g-1(y))¦dg-1(y)/dy¦ dy

The absolute value of dg-1(y)/dy is taken because may be negative and fx ( x ) and d Fx ( x ) are always positive. As X, lying in an interval of width dx in the neighbourhood of X = x, changes to y, that lies in the corresponding interval of width dy in the neighbourhood of Y = y, the element of probability dFx ( x ) and dFy ( y ) remain the same where Fy ( y ) is 1 cdf of Y. Hence

dFy(y) = dFx(x) = fx(g-1(y)) ¦ dg-1(y)/dy¦ dy


fy(y) = d/dy Fy(y) = fx (g-1 (y)) ¦ dg-1(y)/dy¦                                         (6.2)

Equation (6.2) may be used to find the pdf of a one to one function of a random variable. The method could be generalized to the multivariate case to obtain the result that gives the joint pdf of transformed vector random variable Y under the one to one transformation Y - G ( X ) , in terms of the joint pdf of X The generalized result is stated below

fy(y) = fx(G-1 (y))1/¦J¦

where the usual notations and conventions for the Jacobian J = ¦ð y/ð x¦ are assumed


This technique is applicable hust to continuous random variables and only if the functions of random variable Y = G (X) define a one to one transformation of the region where the pdf of X is non zero.

Posted Date: 1/30/2013 7:04:30 AM | Location : United States

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