discrete random variables

Discrete Random Variables

Discrete random variables

For a discrete random variable X the probability

distribution is described by the probability function,

p(x), which has the following properties :

10 .1 xp

1 .2 x

xpbXaP .3

Comment:

• For a discrete random variable the number of

possible values (i.e. x such that p(x) > 0) is

either finite or countably infinite (in a 1-1

correspondence with positive integers.)

Recall

p(x) = P[X = x] = the probability function of X.

This can be defined for any random variable X.

For a continuous random variable

p(x) = 0 for all values of X.

Let SX ={x| p(x) > 0}. This set is countable (i. e. it can

be put into a 1-1 correspondence with the integers}

SX ={x| p(x) > 0}= {x1, x2, x3, x4, …}

Thus let

p x p x

Proof: (that the set SX ={x| p(x) > 0} is countable)

(i. e. can be put into a 1-1 correspondence with the integers}

SX = S1 S2 S3 S3 …

1iS x p x

11 Note: 2

2S x p x n S

1 1 Note: 3

3 2S x p x n S

1 1 Note: 4

4 3S x p x n S

Thus the number of elements of 1 (is finite)i iS n S i

Thus the elements of SX = S1 S2 S3 S3 …

can be arranged {x1, x2, x3, x4, … }

by choosing the first elements to be the elements of S1 ,

the next elements to be the elements of S2 ,

This allows us to write

p x p x

A Discrete Random Variable

A random variable X is called discrete if

That is all the probability is accounted for by values, x,

such that p(x) > 0.

p x p x

Discrete Random Variables

For a discrete random variable X the probability

distribution is described by the probability

function p(x), which has the following properties

p x p x

1. 0 1p x

3. a x b

P a x b p x

Graph: Discrete Random Variable

P a x b p x

Some Important Discrete

distributions

The Bernoulli distribution

Suppose that we have a experiment that has two outcomes

1. Success (S)

2. Failure (F)

These terms are used in reliability testing.

Suppose that p is the probability of success (S) and

q = 1 – p is the probability of failure (F)

This experiment is sometimes called a Bernoulli Trial

Let 0 if the outcome is F

1 if the outcome is SX

Then 0

q xp x P X x

The probability distribution with probability function

is called the Bernoulli distribution

q xp x P X x

q = 1- p

The Binomial distribution

Suppose that we have a experiment that has two outcomes (A Bernoulli trial)

1. Success (S)

2. Failure (F)

Suppose that p is the probability of success (S) and

q = 1 – p is the probability of failure (F)

Now assume that the Bernoulli trial is repeated independently n times.

the number of successes occuring in th trialsX n

Note: the possible values of X are {0, 1, 2, …, n}

For n = 5 the outcomes together with the values of X and the probabilities of each outcome are given in the table below:

For n = 5 the following table gives the different possible values of X, x, and p(x) = P[X = x]

x 0 1 2 3 4 5

p(x) = P[X = x] q5 5pq4 10p3q2 10p2q3 5p4q p5

For general n, the outcome of the sequence of n Bernoulli trails is a sequence of S’s and F’s of length n.

SSFSFFSFFF…FSSSFFSFSFFS

• The value of X for such a sequence is k = the number of S’s in the sequence.

• The probability of such a sequence is pkqn – k ( a p for each S and a q for each F)

• There are such sequences containing exactly k S’s

• is the number of ways of selecting the k positions for the S’s. (the remaining n – k positions are for the F’s

0,1,2,3, , 1,k n kn

p k P X k p q k n nk

These are the terms in the expansion of (p + q)n using the Binomial Theorem

0 1 1 2 2 0

n n n n nn n n n

p q p q p q p q p qn

For this reason the probability function

0,1,2, ,x n xn

p x P X x p q x nx

is called the probability function for the Binomial distribution

Summary

We observe a Bernoulli trial (S,F) n times.

0,1,2, ,x n xn

p x P X x p q x nx

Let X denote the number of successes in the n trials.

Then X has a binomial distribution, i. e.

1. p = the probability of success (S), and

2. q = 1 – p = the probability of failure (F)

Example

A coin is tossed n= 7 times.

0,1,2, ,x n xn

p x P X x p q x nx

Let X denote the number of heads (H) in the n = 7 trials.

Then X has a binomial distribution, with p = ½ and n = 7.

1 12 2

7 0,1,2, ,7

7 0,1,2, ,7x

0 1 2 3 4 5 6 7

x 0 1 2 3 4 5 6 7

p(x) 1/128 7/128

21/128 35/128

35/128 21/128

7/128 1/128

Example If a surgeon performs “eye surgery” the chance of “success” is 85%. Suppose that the surgery is perfomed n = 20 times

0,1,2, ,x n xn

p x P X x p q x nx

Let X denote the number of successful surgeries in the n = 20 trials.

Then X has a binomial distribution, with p = 0.85 and n = 20.

.85 .15 0,1,2, ,20x x

0.0500

0.1000

0.1500

0.2000

0.2500

0.3000

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

x 0 1 2 3 4 5

p (x ) 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

x 6 7 8 9 10 11

p (x ) 0.0000 0.0000 0.0000 0.0000 0.0002 0.0011

x 12 13 14 15 16 17

p (x ) 0.0046 0.0160 0.0454 0.1028 0.1821 0.2428

x 18 19 20

p (x ) 0.2293 0.1368 0.0388

The probability that at least sixteen operations are

successful

= P[X ≥ 16]

= p(16) + p(17) + p(18) + p(19) + p(20)

= 0.1821 + 0.2428 + 0.2293 + 0.1368 + 0.0388

= 0.8298

Other discrete distributions

• Poisson distribution

• Geometric distribution

• Negative Binomial distribution

• Hypergeometric distribution

The Poisson distribution

• Suppose events are occurring randomly and

uniformly in time.

• Let X be the number of events occuring in a

fixed period of time. Then X will have a

Poisson distribution with parameter l.

0,1,2,3,4,!

p x e xx

Some properties of the probability function for

the Poisson distribution with parameter l.

p x ex

1e el l

1! 2! 3! 4!

l ll l l ll

using 12! 3! 4!

u u u ue u

2. If , 1n xx

np x p n p p

is the probability function for the Binomial

distribution with parameters n and p, and we

allow n → ∞ and p → 0 such that np = a

constant (= l say) then

lim ,!

Bin Poissonn p

p x p n p x ex

Proof: , 1n xx

np x p n p p

Suppose or np pn

!, , 1

Bin Bin

np x p n p x n

x n x n n

1 1! !

x n n x n n

1 11 1

x nx n n n x

x n n n n n

1 11 1 1 1 1

x nx x

x n n n n

lim ,Binn

p x nl

1 1lim 1 1 1 1

x n n n n

lim 1!

Now using the classic limit lim 1

lim , lim 1! !

Bin Poissonn n

p x n e p xx n x

ll l ll l

Graphical Illustration

Suppose a time interval is divided into n equal parts and

that one event may or may not occur in each subinterval.

time interval

n subintervals

- Event occurs

- Event does not occur

As n→∞ , events can occur over the continuous time

interval.

X = # of events is Bin(n,p)

X = # of events is Poisson(l)

Example

The number of Hurricanes over a period of a

year in the Caribbean is known to have a Poisson

distribution with l = 13.1

Determine the probability function of X.

Compute the probability that X is at most 8.

Compute the probability that X is at least 10.

Given that at least 10 hurricanes occur, what is

the probability that X is at most 15?

Solution

• X will have a Poisson distribution with

parameter l = 13.1, i.e.

0,1,2,3,4,!

p x e xx

13.113.1 0,1,2,3,4,

Table of p(x)

x p (x ) x p (x )

0 0.000002 10 0.083887

1 0.000027 11 0.099901

2 0.000175 12 0.109059

3 0.000766 13 0.109898

4 0.002510 14 0.102833

5 0.006575 15 0.089807

6 0.014356 16 0.073530

7 0.026866 17 0.056661

8 0.043994 18 0.041237

9 0.064036 19 0.028432

at most 8 8P P X

0 1 8 .09527p p p

at least 10 10 1 9P P X P X

1 0 1 9 .8400p p p

at most 15 at least 10 15 10P P X X

15 10 10 15

P X X P X

P X P X

10 11 150.708

The Geometric distribution

Suppose a Bernoulli trial (S,F) is repeated until a

success occurs.

Let X = the trial on which the first success (S) occurs.

Find the probability distribution of X.

Note: the possible values of X are {1, 2, 3, 4, 5, … }

The sample space for the experiment (repeating a

Bernoulli trial until a success occurs is:

S = {S, FS, FFS, FFFS, FFFFS, … , FFF…FFFS, …}

p(x) =P[X = x] = P[{FFF…FFFS}] = (1 – p)x – 1p

(x – 1) F’s

Thus the probability function of X is:

P[X = x] = p(x) = p(1 – p)x – 1 = pqx – 1

A random variable X that has this distribution is said to

have the Geometric distribution.

Reason p(1) = p, p(2) = pq, p(3) = pq2 , p(4) = pq3 , …

forms a geometric series

1 + 2 + 3 + x

p x p p p

p pp pq pq pq

The Negative Binomial distribution

Suppose a Bernoulli trial (S,F) is repeated until k

successes occur.

Let X = the trial on which the kth success (S) occurs.

Note: the possible values of X are

{k, k + 1, k + 2, k + 3, 4, 5, … }

Bernoulli trial until k successes occurs) consists of

sequences of S’s and F’s having the following properties:

1. each sequence will contain k S’s

2. The last outcome in the sequence will be an S.

SFSFSFFFFS FFFSF … FFFFFFS

A sequence of length x

containing exactly k S’s

The last

outcome

is an S

The # of S’s in

the first x – 1

trials is k – 1.

, 1, 2,1

k x kx

p x P X x p q x k k kk

The # of ways of

choosing from the

first x – 1 trials, the

positions for the

first k – 1 S’s.

The probability

of a sequence

containing k S’s

and x – k F’s.

The Hypergeometric distribution

Suppose we have a population containing N objects.

Suppose the elements of the population are partitioned into two groups. Let a = the number of elements in group A and let b = the number of elements in the other group (group B). Note N = a + b.

Now suppose that n elements are selected from the population at random. Let X denote the elements from group A. (n – X will be the number of elements from group B.)

Find the probability distribution of X.\

Population

Group A (a elements)

GroupB (b elements)

sample (n elements)

x N - x

A random variable X that has this distribution is said to have the

Hypergeometric distribution.

The total number of

ways n elements can be

chosen from N = a + b

elements

x n xp x P X x

The number of ways

n - x elements can be

chosen Group B .

The number of ways

x elements can be

chosen Group A .

The possible values of X are integer values that range from

max(0,n – b) to min(n,a)

Discrete distributions

q p xp x P X x

1 Bernoulli trial =

0 Bernoulli trial = X

0,1,2, ,x n xn

p x P X x p q x nx

0.0500

0.1000

0.1500

0.2000

0.2500

0.3000

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

X = the number of successes in n repetitions of

a Bernoulli trial

p = the probability of success

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40

Events are occurring randomly and uniformly in time.

X = the number of events occuring in a fixed period of time.

0,1,2,3,4,!

p x e xx

The Geometric Distribution

The Negative Binomial Distribution

The Binomial distribution, the Geometric distribution and the Negative Binomial distribution each arise when repeating independently Bernoulli trials

the Bernoulli trials are repeated independently a fixed number of times n and X = the numbers of successes

the Bernoulli trials are repeated independently until a fixed number, k, of successes has occurred and X = the trial on which the kth success occurred.

the Bernoulli trials are repeated independently the first success occurs (,k = 1) and X = the trial on which the 1st success occurred.

Suppose a Bernoulli trial (S,F) is repeated until a

success occurs.

Let X = the trial on which the first success (S) occurs.

Note: the possible values of X are {1, 2, 3, 4, 5, … }

Bernoulli trial until a success occurs is:

S = {S, FS, FFS, FFFS, FFFFS, … , FFF…FFFS, …}

p(x) =P[X = x] = P[{FFF…FFFS}] = (1 – p)x – 1p

(x – 1) F’s

P[X = x] = p(x) = p(1 – p)x – 1 = pqx – 1

A random variable X that has this distribution is said to

have the Geometric distribution.

Reason p(1) = p, p(2) = pq, p(3) = pq2 , p(4) = pq3 , …

forms a geometric series

1 + 2 + 3 + x

p x p p p

p pp pq pq pq

Example

Suppose a die is rolled until a six occurs

Success = S = {six} , p = 1/6.

Failure = F = {no six} q = 1 – p = 5/6.

1. What is the probability that it took at most 5 rolls of a die to

roll a six?

2. What is the probability that it took at least 10 rolls of a die to

roll a six?

3. What is the probability that the “first six” occurred on an

even number toss?

4. What is the probability that the “first six” occurred on a toss

divisible by 3 given that the “first six” occurred on an even

number toss?

Solution

Let X denote the toss on which the first head occurs.

Then X has a geometric distribution with p = 1/6.. q = 1 – p = 5/6.

1. P[X ≤ 5]?

2. P[X ≥ 10]?

3. P[X is divisible by 2]?

4. P[X is divisible by 3| X is divisible by 2]?

6 6 1,2,3,

xxP X x p x pq x

1. P[X ≤ 5]?

5 1 2 3 4 5P X p p p p p

0 1 2 3 4

5 5 5 5 51 1 1 1 16 6 6 6 6 6 6 6 6 6

+ + + +

1 2 3 4

5 5 5 516 6 6 6 6

1+ + + +

a ar ar ar ar

556 51

1a ar ar a

Note also

10 10 11 12P X p p p

2. P[X ≥ 10]?

9 10 11

5 5 51 1 16 6 6 6 6 6

5 5 516 6 6 6

1+ + +

5 516 6 65

Using 2 1

1a ar ar a

is divisible by 2 2 4 6P X p p p

3. P[X is divisible by 2]?

5 5 51 1 16 6 6 6 6 6

2 45 5 5 51 1

6 6 6 6 6 6 256

11+ + +

5 5 536 36 25 1125

4. P[X is divisible by 3| X is divisible by 2]?

5 11 17

5 5 51 1 16 6 6 6 6 6

5 6 12

5 5 516 6 6 6

1+ + +

is divisible by 3 is divisible by 2P X X

is divisible by 3 is divisible by 2

is divisible by 2

is divisible by 6

is divisible by 2

is divisible by 6 6 12 18P X p p p

516 6 6 6 6

1 5 3125

6 5 435311-

is divisible by 3 is divisible by 2P X X

is divisible by 6

is divisible by 2

6875435315 43531

Suppose a Bernoulli trial (S,F) is repeated until k

successes occur.

Let X = the trial on which the kth success (S) occurs.

Note: the possible values of X are

{k, k + 1, k + 2, k + 3, 4, 5, … }

Bernoulli trial until k successes occurs) consists of

sequences of S’s and F’s having the following properties:

1. each sequence will contain k S’s

2. The last outcome in the sequence will be an S.

SFSFSFFFFS FFFSF … FFFFFFS

A sequence of length x

containing exactly k S’s

The last

outcome

is an S

The # of S’s in

the first x – 1

trials is k – 1.

, 1, 2,1

k x kx

The # of ways of

choosing from the

first x – 1 trials, the

positions for the

first k – 1 S’s.

The probability

of a sequence

containing k S’s

and x – k F’s.

Example

Suppose the chance of winning any prize in a lottery is

3%. Suppose that I play the lottery until I have won k =

5 times.

Let X denote the number of times that I play the lottery.

Find the probability function, p(x), of X

, 1, 2,1

k x kx

0.03 0.91 5,6,7,4

0 100 200 300 400 500 600

Graph of p(x)

Suppose the elements of the population are partitioned into two groups. Let a = the number of elements in group A and let b = the number of elements in the other group (group B). Note N = a + b.

Now suppose that n elements are selected from the population at random. Let X denote the elements from group A. (n – X will be the number of elements from group B.)

Find the probability distribution of X.\

Population

Group A (a elements)

GroupB (b elements)

sample (n elements)

x n - x

A random variable X that has this distribution is said to have the

Hypergeometric distribution.

The total number of

ways n elements can be

chosen from N = a + b

elements

x n xp x P X x

The number of ways

n - x elements can be

chosen Group B .

The number of ways

x elements can be

chosen Group A .

The possible values of X are integer values that range from

max(0,n – b) to min(n,a)

• Suppose that N (unknown) is the size of a wildlife

population.

• To estimate N, T animals are caught, tagged and

replaced in the population. (T is known)

• A second sample of n animals are caught and the

number, t, of tagged animals is noted. (n is known

and t is the observation that will be used to estimate

Example: Estimating the size of a

wildlife population

• The observation, t, will have a hypergeometric

distribution

t n tf t N L N

ˆTo Estimate we find the value , that maximizesN N

t n tL N

To determine when

t n tL N

is maximized compute and determine when the ratio

is greater than 1 and less than 1.

T N T T N T

L N t n t t n t

N NL N

1 ! ! ! 1 !

! 1 ! 1 ! !

N T N T n t N N n

N T N n

N T n t N

N T N n

N T n t N

1 1 1 1N T N n N T n t N or

1 ( ) 1N n T N nT

1 1N t n T N

1nT t N or

greatest integer less than or equal to xx

Thus ˆ nTN

greatest integer less than or equal to nT

ˆ ˆIf is an integer then 1 or nT nT nT

N Nt t t

Example: Hyper-geometric distribution

Suppose that N = 10 automobiles have just come off the

production line. Also assume that a = 3 are defective

(have serious defects). Thus b = 7 are defect-free.

A sample of n = 4 are selected and tested to see if they

are defective. Let X = the number in the sample that are

defective. Find the probability function of X.

From the above discussion X will have a hyper-

geometric distribution i.e.

4 0,1,2,3

x n x x xp x P X x x

Table and Graph of p(x)

x p (x )

0 0.1667

1 0.5000

2 0.3000

3 0.0333 0.0

0 1 2 3

Sampling with and without replacement

Suppose the elements of the population are partitioned

into two groups. Let a = the number of elements in group

A and let b = the number of elements in the other group

(group B). Note N = a + b.

Now suppose that n elements are selected from the

population at random. Let X denote the elements from

group A. (n – X will be the number of elements from

group B.)

1. If the sampling was done with replacement.

2. If the sampling was done without replacement

Solution:

1. If the sampling was done with replacement.

Then the distribution of X is the Binomial distn.

n a bi e p x P X x

with and 1a b

p q pN N

2. If the sampling was done without replacement.

Then the distribution of X is the hyper-geometric distn.

. . Hyper

x n xi e p x P X x

p x x n x n

p x n a b

! !! ! ( )! !

! ! ! ! ! !

x n xa b N n n N

x a x n x b n x n N a b

1 1 1 1

1 ( 1)

a a a x b b b n x N

a b N N N n

1 1 1 11 1 1 1

1 as , ,1 1

a a b bN a b

for large values of N, a and b

n a bp x

a b Np x

x n x n

Thus for large values of N, a and b sampling with

replacement is equivalent to sampling without

replacement.

Summary

q p xp x P X x

1 Bernoulli trial =

0 Bernoulli trial = X

0,1,2, ,x n xn

p x P X x p q x nx

0.0500

0.1000

0.1500

0.2000

0.2500

0.3000

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

X = the number of successes in n repetitions of

a Bernoulli trial

p = the probability of success

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40

Events are occurring randomly and uniformly in time.

X = the number of events occuring in a fixed period of time.

0,1,2,3,4,!

p x e xx

the Bernoulli trials are repeated independently until a fixed number, k, of successes has occurred and X = the trial on which the kth success occurred.

the Bernoulli trials are repeated independently the first success occurs (,k = 1) and X = the trial on which the 1st success occurred.

P[X = x] = p(x) = p(1 – p)x – 1 = pqx – 1

, 1, 2,1

k x kx

Geometric ≡ Negative Binomial with k = 1

The population are partitioned into two groups.

• a = the number of elements in group A

• b = the number of elements in the other group (group B).

Note N = a + b.

• n elements are selected from the population at random.

• X = the elements from group A. (n – X will be the number of

elements from group B.)

x n xp x P X x

discrete random variables - university of saskatchewanmath.usask.ca/~laverty/s241/s241 lectures...

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