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Discrete Probability






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Discrete Probability. CSC-2259 Discrete Structures. Introduction to Discrete Probability. Unbiased die. Sample Space:. All possible outcomes. Event:. any subset of sample space. Experiment:. procedure that yields events. Throw die. Probability of event :. Note that: . since.
Discrete Probability

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Slide 1

Discrete Probability

CSC-2259 Discrete Structures

Konstantin Busch - LSU

Slide 2

Introduction to Discrete Probability

Unbiased die

Sample Space:

All possible outcomes

Slide 3

Event:

any subset of sample space

Experiment:

procedure that yields events

Throw die

Konstantin Busch - LSU

Slide 4

Probability of event :

Note that:

since

Konstantin Busch - LSU

Slide 5

What is the probability that

a die brings 3?

Event Space:

Sample Space:

Probability:

Slide 6

What is the probability that

a die brings 2 or 5?

Event Space:

Sample Space:

Probability:

Slide 7

Two unbiased dice

Sample Space:

36 possible outcomes

First die

Second die

Ordered pair

Slide 8

What is the probability that

two dice bring (1,1)?

Event Space:

Sample Space:

Probability:

Slide 9

What is the probability that

two dice bring same numbers?

Event Space:

Sample Space:

Probability:

Slide 10

Game with unordered numbers

Game authority selects

a set of 6 winning numbers out of 40

Number choices: 1,2,3,…,40

i.e. winning numbers: 4,7,16,25,33,39

Player picks a set of 6 numbers

(order is irrelevant)

i.e. player numbers: 8,13,16,23,33,40

What is the probability that a player wins?

Konstantin Busch - LSU

Slide 11

Winning event:

a single set with the 6 winning numbers

Sample space:

Konstantin Busch - LSU

Slide 12

Probability that player wins:

Konstantin Busch - LSU

Slide 13

A card game

Deck has 52 cards

13 kinds of cards (2,3,4,5,6,7,8,9,10,a,k,q,j),

each kind has 4 suits (h,d,c,s)

Player is given hand with 4 cards

What is the probability that the cards

of the player are all of the same kind?

Konstantin Busch - LSU

Slide 14

Event:

each set of 4 cards is of

same kind

Sample Space:

Konstantin Busch - LSU

Slide 15

Probability that hand has 4 same kind cards:

Konstantin Busch - LSU

Slide 16

Game with ordered numbers

Game authority selects from a bin 5 balls

in some order labeled with numbers 1…50

Number choices: 1,2,3,…,50

i.e. winning numbers: 37,4,16,33,9

Player picks a set of 5 numbers

(order is important)

i.e. player numbers: 40,16,13,25,33

What is the probability that a player wins?

Konstantin Busch - LSU

Slide 17

Sampling without replacement:

After a ball is selected

it is not returned to bin

Sample space size:

5-permutations of 50 balls

Probability of success:

Konstantin Busch - LSU

Slide 18

Sampling with replacement:

After a ball is selected

it is returned to bin

Sample space size:

5-permutations of 50 balls

with repetition

Probability of success:

Konstantin Busch - LSU

Slide 19

Probability of Inverse:

Proof:

End of Proof

Konstantin Busch - LSU

Slide 20

Example:

What is the probability that

a binary string of 8 bits contains

at least one 0?

Konstantin Busch - LSU

Slide 21

Probability of Union:

Proof:

End of Proof

Konstantin Busch - LSU

Slide 22

Example:

What is the probability that

a binary string of 8 bits

starts with 0 or ends with 11?

Strings that start with 0:

(all binary strings with 7 bits 0xxxxxxx)

Strings that end with 11:

(all binary strings with 6 bits xxxxxx11)

Konstantin Busch - LSU

Slide 23

Strings that start with 0 and end with 11:

(all binary strings with 5 bits 0xxxxx11)

Strings that start with 0 or end with 11:

Konstantin Busch - LSU

Slide 24

Probability Theory

Sample space:

Probability distribution function :

Konstantin Busch - LSU

Slide 25

Notice that it can be:

Example:

Biased Coin

Heads (H) with probability 2/3

Tails (T) with probability 1/3

Sample space:

Konstantin Busch - LSU

Slide 26

Uniform probability distribution:

Sample space:

Example:

Unbiased Coin

Heads (H) or Tails (T) with probability 1/2

Konstantin Busch - LSU

Slide 27

Probability of event :

For uniform probability distribution:

Konstantin Busch - LSU

Slide 28

Example:

Biased die

What is the probability

that the die outcome is 2 or 6?

Konstantin Busch - LSU

Slide 29

Combinations of Events:

Complement:

Union:

Union of disjoint events:

Konstantin Busch - LSU

Slide 30

Conditional Probability

Three tosses of an unbiased coin

Tails

Heads

Tails

Condition:

first coin is Tails

Question:

What is the probability that

there is an odd number of Tails,

given that first coin is Tails?

Konstantin Busch - LSU

Slide 31

Sample space:

Restricted sample space given condition:

first coin is Tails

Konstantin Busch - LSU

Slide 32

Event without condition:

Odd number of Tails

Event with condition:

first coin is Tails

Konstantin Busch - LSU

Slide 33

Given condition,

the sample space changes to

(the coin is unbiased)

Konstantin Busch - LSU

Slide 34

Notation of event with condition:

event given

Konstantin Busch - LSU

Slide 35

Conditional probability definition:

(for arbitrary probability distribution)

Given sample space with

events and (where )

the conditional probability of given is:

Konstantin Busch - LSU

Slide 36

Example:

What is probability that a family

of two children has two boys

given that one child is a boy

Assume equal probability to have boy or girl

Sample space:

Condition:

one child is a boy

Konstantin Busch - LSU

Slide 37

Event:

both children are boys

Conditional probability of event:

Konstantin Busch - LSU

Slide 38

Independent Events

Events and are independent iff:

Equivalent definition (if ):

Konstantin Busch - LSU

Slide 39

Example:

4 bit uniformly random strings

: a string begins with 1

: a string contains even 1

Events and are independent

Konstantin Busch - LSU

Slide 40

Bernoulli trial:

Experiment with two outcomes:

success or failure

Success probability:

Failure probability:

Example:

Biased Coin

Success = Heads

Failure = Tails

Konstantin Busch - LSU

Slide 41

Independent Bernoulli trials:

the outcomes of successive Bernoulli trials

do not depend on each other

Example:

Successive coin tosses

Konstantin Busch - LSU

Slide 42

Throw the biased coin 5 times

What is the probability to have 3 heads?

Heads probability:

(success)

Tails probability:

(failure)

Konstantin Busch - LSU

Slide 43

HHHTT

HTHHT

HTHTH

THHTH

Total numbers of ways to arrange in sequence

5 coins with 3 heads:

Konstantin Busch - LSU

Slide 44

Probability that any particular sequence has

3 heads and 2 tails is specified positions:

For example:

HHHTT

HTHHT

HTHTH

Konstantin Busch - LSU

Slide 45

Probability of having 3 heads:

st

1st

sequence

success

(3 heads)

2nd

sequence

success

(3 heads)

sequence

success

(3 heads)

Konstantin Busch - LSU

Slide 46

Throw the biased coin 5 times

Probability to have exactly 3 heads:

Probability to have 3 heads and 2 tails

in specified sequence positions

All possible ways to arrange in sequence 5 coins with 3 heads

Konstantin Busch - LSU

Slide 47

Theorem:

Probability to have successes

in independent Bernoulli trials:

Also known as

binomial probability distribution:

Konstantin Busch - LSU

Slide 48

Proof:

Total number of

sequences with

successes and

failures

Probability that

a sequence has

successes and

failures

in specified positions

Example:

SFSFFS…SSF

End of Proof

Konstantin Busch - LSU

Slide 49

Example:

Random uniform binary strings

probability for 0 bit is 0.9

probability for 1 bit is 0.1

What is probability of 8 bit 0s out of 10 bits?

i.e. 0100001000

Konstantin Busch - LSU

Slide 50

Birthday Problem

Birthday collision: two people have birthday

in same day

Problem:

How many people should be in a room

so that the probability of birthday collision

is at least ½?

Assumption: equal probability to be born in any day

Konstantin Busch - LSU

Slide 51

366 days in a year

If the number of people is 367 or more

then birthday collision is guaranteed by

pigeonhole principle

Assume that we have people

Konstantin Busch - LSU

Slide 52

We will compute

:probability that people have

all different birthdays

It will helps us to get

:probability that there is

a birthday collision among people

Konstantin Busch - LSU

Slide 53

Sample space:

Cartesian product

1st person’s

Birthday

choices

2nd person’s

Birthday

choices

nth person’s

Birthday

choices

Sample space size:

Konstantin Busch - LSU

Slide 54

Event set:

each person’s birthday is different

1st person’s

birthday

2nd person’s

birthday

nth person’s

birthday

#choices

#choices

#choices

Sample size:

Konstantin Busch - LSU

Slide 55

Probability of no birthday collision

Probability of birthday collision:

Konstantin Busch - LSU

Slide 56

Probability of birthday collision:

Therefore:

people have probability

at least ½ of birthday collision

Konstantin Busch - LSU

Slide 57

The birthday problem analysis can be used

to determine appropriate hash table sizes

that minimize collisions

Hash function collision:

Konstantin Busch - LSU

Slide 58

Monte Carlo algorithms

Randomized algorithms:

algorithms with randomized choices

(Example: quicksort)

Monte Carlo algorithms:

randomized algorithms whose output

is correct with some probability

(may produce wrong output)

Konstantin Busch - LSU

Slide 59

Primality_Test( ) {

for( to ) {

if (Miller_Test( ) == failure)

return(false) // n is not prime

}

return(true) // most likely n is prime

}

Konstantin Busch - LSU

Slide 60

Miller_Test( ) {

for ( to ) {

if ( or )

return(success)

}

return(failure)

}

Konstantin Busch - LSU

Slide 61

A prime number passes the

Miller test for every

A composite number passes the

Miller test in range

For fewer than numbers

false positive with probability:

Konstantin Busch - LSU

Slide 62

If the primality test algorithm returns false

then the number is not prime for sure

If the algorithm returns true then the

answer is correct (number is prime)

with high probability:

for

Konstantin Busch - LSU


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