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Transformation Techniques
In Probability theory various transformation techniques
are used to simplify the solutions for various moment
calculations. We will discuss here 4 of those functions.
1.
2.
3.
4.
Probability Generating Function
Moment Generating Function
Characteristic Function
Laplace Transformation of probability density function
1
Probability Generating Function
Tool that simplifies computations of integer valued discrete random variable problems
X: non-negative integer valued Random Number
P(X=k) =pk, then define the Probability Generating Function (PGF) of X by
GX(z) = E[z X] = S pk z k
= p0 + p1z + p2 z2 + ……. pk z k +……
z is a complex number z < 1
G(z) is nothing more than z-transform of pk.
Gx(1) = 1 = S pk
2
Generating Functions
K : Non-negative integer valued random variable with probability distribution pj
where,
pj = Prob[K =j] for all j = 0,1,2,……
g(z) : p0 + p1 z+ p2 z2+ p3 z3+ …….
g(z) is a power series of probability pj with coefficient zj is the probability generating
Function of random variable K
Few properties
g(1) = 1
as S pj = 1
and z is a complex number and converged to Absolute Value Mod[z] < 1
Expected Value E[K] = S j pj for j: 0,1,2…..
(d/dz)g(z) = S j pj zj-1 at z =1 for j : 1,2,…..
E[K] = g(1)(1)
Similarly V[K] = g(2)(1) + g(1)(1) – [g(1)(1)]2
Reference: Introduction to Queuing Theory, Robert Coop
3
Moment Generating Function
mg(t) : Moment Generating Function: Expected Value of
function etX, where ‘t’ is a real variable and X is the
random variable
mg(t) = E[etX] =  Xi Rx p(Xi). etXi
= ∫Rx f(x). etXidx
If mg(t) exists for all real values of t, in some small
interval –d, d : d > 0 about the origin, it can be shown
that the probability distribution function can be obtained
from mg(t). We assume mg(t) exists at a small region t
about origin.
4
Moment Generating Function-2
etX= 1 + tx + t2X2/2! + t3X3/3!+ 
Assume X is a continuous Random Variable
mg(t) = E[etX] =  Xi Rx p(Xei).tXetXi
= ∫Rx f(x). etXidx

= ∫Rx i=0 tiXi/i! f(X)dx
= ∫Rx
ti/i!

i=0
Xi

f(X)dx = i=0 ti/i!∫Rx Xi f(X)dx

= i=0 ti/i!E[Xi] = E[X0] + tE[X1] + t2/2!E[X2] + …
5
Moment Generating Function-3
mg(t) = E[X0] + tE[X1] + t2/2!E[X2] + …
m (1)g(t) = E[X1] + tE[X2] + t2/2!E[X3] + …
m (2)g(t) = E[X2] + tE[X3] + t2/2!E[X4] + …
At t = 0
m (1)g(t) = E[X1]
m (2)g(t) = E[X2]
Var[X] = E[X2] – [E[X]]2 = m (2)g(t) - [m (1)g(t)] 2
6
Characteristic Function
The Characteristic Function of Random Variable X
fX(u) = E[e juX] = ∫- e juXfx(x)dx
where j = -1 and u is an arbitrary real variable
Note: Except for the sign of exponent, Characteristic function is
the Fourier Transform of the pdf of X.
fX(u) = ∫- fx(x)dx[1 + jux +(jux)2/2! + (jux)3/3! + ……..]dx
= 1 + juE[X] + (ju)2/2!E[X2] + (ju)3/3!E[X3] + …..
Let u=0
Then fX(0) = 1
f(1)X(0) = dfX(u)/duu=0 = jE[X]
f(2)X(0) = d2fX(u)/du2u=0 = j2E[X2]
7
Laplace Transform
Let CDF of traffic arrival process is defined as A(x), where X is the random variable for
inter arrival time between two customers.
A(x) = P[X < x]
The pdf (probability density function) is denoted by a(x)
Laplace Transform of a(x) is denoted by A*(s) and is given by
A*(s) = E[e –sX]

= ∫ - e –sx axdx
Since most random variable deals with non negative numbers, we can make the
transform as

A*(s) = ∫
0
e –sx axdx
Similar techniques of Moment generating function or characteristic function can be used
to show that
A*(n) (0) = (-1)nE[Xn]
8
Example
For a continuous Random variable pdf is given as follows
le –lx
x>0
fx(x) =
0
x<0
Laplace Transform : A*(s) =
l
l+s
Characteristic Function: fx(u) =
l
l - ju
Moment Generating Function: mg(v) =
l
l-v
9
Expected Value
Laplace Transform : E[X] = (-1)A*(1) (0) = (-) d[l/(l + s)/dss=0
= (-) [(-)l/(l +s)2] s=0
= l/l2 = 1/l
Characteristic Function: E[X] = j-1 fx(1) (0)
= (j-1) d[l/(l - ju)/duu=0
= (j-1)[l.j/(l - ju)2u=0
= l/l2 = 1/l
Moment Generating Function E[X}= mX(1) (0)
= d[l/(l - v)/dvv=0
=[l/(l - v)2v=0
= l/l2 = 1/l
10
Variance
Laplace Transform : E[X2] = (-1)2A*(2) (0) = d2[l/(l + s)/ds2s=0
= [2l(l+s)/(l +s)3] s=0
= 2l2/l3 = 2/l
Var[X] = E[X2] – [E[X]]2 = 2/l – [1/l]2 = (2l –l)/l2 = 1/l
Characteristic Function: E[X2] = j-2 fx(2) (0)
= (j-2) d2[l/(l - ju)/du2u=0
= (j-2)[2l(l – ju).j2/(l - ju)3u=0
= 2l2/l3 = 2/l
Moment Generating Function E[X2}= mX(2) (0)
= d2[l/(l - v)/dv2v=0
=[2l(l - v )/(l - v)3v=0
= 2l2/l3 = 2/l
11
12
Sum of Random Variables
K1 and K2 are two independent random variables with GF g1(z) and g2(z)
Find the Probability distribution P{K=k} where K = K1 + K2
P{K =k} = P{k1 = j}.P{k2 = k-j}
g1(z) = S P{k1=j}zj for j: 0.1,2…….
g2(z) = S P{k2=j}zj for j: 0.1,2…….
g1(z)g2(z) =kS= 0{ Sj = P{k1=j}P{k2=k
- j}zk for k: 0.1,2……. and j : 0,1,2…k
0
If K has a generating function of g(z), then
g(z) = S P{K=k}zk for k: 0.1,2…….
= k=S0[Sj =P{k1
= j}.P{k2 = k-j}] for k: 0.1,2……. and j : 0,1,2…k
0
g(z) = g1(z)g2(z)
13
Example: Bernoulli Distribution
Bernoulli Distribution :
X =0 with probability q
X = 1 with probability p
p+q=1
g(z) = q + pz
g’(1) = p
g’’(I) = 0
E[X] = g’(1) = p
V[x] = g’’(1) + g’(1) – [g’(1)]2
= p – p2 = p(1 – p) = pq
A coin is tossed for n times, Xj = 0 if tail and Xj = 1 if head
probability to have k heads in n tosses.
Sn is the sum of n independent Bernoulli random variables
Sn = X1 +X2 +……….+ Xn
g(z) = GF of a toss = q + pz
GF of Sn = S P{Sn = k}zk
for k : 0,1,2……
= g(z).g(z)…….g(z) = [g(z)]n = (q + pz)n
= S nCk[pz] k q n-k
for k = 0…..n
P{Sn= k} = nCk[pz] k q n-k
for k = 0…..n
=0
for k > n
Binomial Distribution
14
Example Poisson Distribution
Poisson Distribution = [(lt)j/j!]e –lt
for j:0,1,2…..
Generating Function
g(z) = S [(lt)j/j!]e –lt zj
= e –ltS [(ltz)j/j!] for j: 0,1,2,….
= e –lt e ltz = e –lt(1-z)
Expectation P[N(t) =j]
g’(z) = lte –lt(1-z)
E[N(t) =j] = g’(1) = lt
Variance
g’’(z) = (lt)2e –lt(1-z)
g’’(1) = (lt)2
V[N(t)] = g’’(1) + g’(1) – {g’(1)}2 = lt
Sum of Poisson distribution of l1 and l2
g(z) = e –l1t(1-z) e –l2t(1-z) = +e –(l1+ l2)t(1-z)
l = l1 + l2
15
Use of GF for Probability
M/M/1 System Birth and Death Equation
0 = - ( l + m) pn + m pn+1 + l pn-1 (n>1)
0 = -l p0 + m p1
pn+1 = [( l + m)/ m] pn - [ l/ m] pn-1
p1 = [l/ m]p0
If r = l/ m
pn+1 = ( r + 1)pn - r pn-1 (n>1)
p1 = rp0
Use GF to solve this equation
zn pn+1 = ( r + 1) zn pn - r zn pn-1 (n>1)
z-1 pn+1 zn+1 = ( r + 1) zn pn - r z pn-1 zn-1



n+1 = ( r + 1) Szn p - r z Sp
n-1
z-1 S
p
z
z
n+1
n
n-1
n=1
n=1
n=1
16
GF for Prob


n=-1
n=0

z-1[Spn+1 zn+1 – p1 z – p0 ] = ( r + 1)[ Szn pn - p0] - r z Spn-1 zn-1

But
Spn+1
n=-1
zn+1 =

Spn
n=0
zn =
n=1

Spn-1 zn-1 = P(z)
n=1
z-1[P(z) – p1 z – p0 ] = ( r + 1)[ P(z) - p0] - r z P(z)
z-1[P(z) – rp0 z – p0 ] = ( r + 1)[ P(z) - p0] - r z P(z)
z-1P(z) – rp0 – z-1p0 = rP(z) - rp0 + P(z) - p0 - r z P(z)
P(z) = p0 /(1 –rz)
To Find p we use the boundary condition P(1) =1
P(1) = p0 /(1 –r) = 1
p0 = 1 –r
P(z) = (1 –r) /(1 –rz)
1 /(1 –rz) = 1 + zr +

P(z) = S(1-r)rnzn
n=0
zr2
+ …….
pn = (1-r)rn
17
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