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Page 1: Tutorial 5: Discrete Probability I Reference:  cps102/lecture11.ppt Steve Gu Feb 15, 2008.

Tutorial 5: Discrete Probability I

Reference:http://www.cs.duke.edu/courses/fall07/cps102/lecture11.ppt

Steve GuFeb 15, 2008

Page 2: Tutorial 5: Discrete Probability I Reference:  cps102/lecture11.ppt Steve Gu Feb 15, 2008.

Language of Probability

The formal language of probability is a very important tool in describing and analyzing probability distribution

Page 3: Tutorial 5: Discrete Probability I Reference:  cps102/lecture11.ppt Steve Gu Feb 15, 2008.

Probability Space• A Probability space has 3 elements:

– Sample Space : Ω• All the possible individual outcomes of an experiment

– Event Space : ₣• Set of all possible subsets of elements taken from Ω

– Probability Measure : P• A mapping from event space to real numbers such

that for any E and F from ₣– P(E)>=0– P(Ω)=1– P(E union F) = P(E) + P(F)

• We can write a probability space as (Ω, ₣,P)

Page 4: Tutorial 5: Discrete Probability I Reference:  cps102/lecture11.ppt Steve Gu Feb 15, 2008.

Ω

Sample space

Sample Space: Ω

p(x) = 0.2probability of x

0.20.13

0.06

0.110.17

0.10.13

00.1

Page 5: Tutorial 5: Discrete Probability I Reference:  cps102/lecture11.ppt Steve Gu Feb 15, 2008.

EventsAny set E Ω is called an event

p(x)x E

PrD[E] = S0.17

0.10.13

0

PrD[E] = 0.4

Page 6: Tutorial 5: Discrete Probability I Reference:  cps102/lecture11.ppt Steve Gu Feb 15, 2008.

Uniform DistributionIf each element has equal probability, the distribution is said to be uniform

p(x) = x E

PrD[E] = |E||Ω|

Page 7: Tutorial 5: Discrete Probability I Reference:  cps102/lecture11.ppt Steve Gu Feb 15, 2008.

A fair coin is tossed 100 times in a row

What is the probability that we get exactly half heads?

Page 8: Tutorial 5: Discrete Probability I Reference:  cps102/lecture11.ppt Steve Gu Feb 15, 2008.

The sample space Ω is the set of all outcomes

H,T100Each sequence in Ω is

equally likely, and hence has probability 1/|Ω|

=1/2100

Using the Language

Page 9: Tutorial 5: Discrete Probability I Reference:  cps102/lecture11.ppt Steve Gu Feb 15, 2008.

Ω = all sequencesof 100 tosses

x = HHTTT……THp(x) = 1/|Ω|

Visually

Page 10: Tutorial 5: Discrete Probability I Reference:  cps102/lecture11.ppt Steve Gu Feb 15, 2008.

Set of all 2100 sequencesH,T100

Probability of event E = proportion of E in Ω

Event E = Set of sequences with 50 H’s and 50 T’s

10050 / 2100

Page 11: Tutorial 5: Discrete Probability I Reference:  cps102/lecture11.ppt Steve Gu Feb 15, 2008.

Suppose we roll a white die and a black

die

What is the probability that sum is 7 or 11?

Page 12: Tutorial 5: Discrete Probability I Reference:  cps102/lecture11.ppt Steve Gu Feb 15, 2008.

(1,1), (1,2), (1,3), (1,4), (1,5), (1,6), (2,1), (2,2), (2,3), (2,4), (2,5), (2,6), (3,1), (3,2), (3,3), (3,4), (3,5), (3,6), (4,1), (4,2), (4,3), (4,4), (4,5), (4,6), (5,1), (5,2), (5,3), (5,4), (5,5), (5,6), (6,1), (6,2), (6,3), (6,4), (6,5), (6,6)

Pr[E] = |E|/|Ω| = proportion of E in S = 8/36

Same Methodology!Ω =

Page 13: Tutorial 5: Discrete Probability I Reference:  cps102/lecture11.ppt Steve Gu Feb 15, 2008.

23 people are in a roomSuppose that all possible birthdays are equally likelyWhat is the probability that two people will have the same birthday?

Page 14: Tutorial 5: Discrete Probability I Reference:  cps102/lecture11.ppt Steve Gu Feb 15, 2008.

x = (17,42,363,1,…, 224,177)23 numbers

And The Same Methods Again!Sample space Ω = 1, 2, 3, …, 36623

Event E = x W | two numbers in x are same Count |E| instead!What is |E|?

Page 15: Tutorial 5: Discrete Probability I Reference:  cps102/lecture11.ppt Steve Gu Feb 15, 2008.

all sequences in S that have no repeated numbers

E =

|Ω| = 36623

|E| = (366)(365)…(344)

= 0.494…|Ω||E|

|E||Ω| = 0.506…

Page 16: Tutorial 5: Discrete Probability I Reference:  cps102/lecture11.ppt Steve Gu Feb 15, 2008.

and is defined to be =

S

A

Bproportion of A B

More Language Of ProbabilityThe probability of event A given event B is written Pr[ A | B ]

to B

Pr [ A B ] Pr [ B ]

Page 17: Tutorial 5: Discrete Probability I Reference:  cps102/lecture11.ppt Steve Gu Feb 15, 2008.

event A = white die = 1

event B = total = 7

Suppose we roll a white die and black dieWhat is the probability that the white is 1 given that the total is 7?

Page 18: Tutorial 5: Discrete Probability I Reference:  cps102/lecture11.ppt Steve Gu Feb 15, 2008.

(1,1), (1,2), (1,3), (1,4), (1,5), (1,6), (2,1), (2,2), (2,3), (2,4), (2,5), (2,6), (3,1), (3,2), (3,3), (3,4), (3,5), (3,6), (4,1), (4,2), (4,3), (4,4), (4,5), (4,6), (5,1), (5,2), (5,3), (5,4), (5,5), (5,6), (6,1), (6,2), (6,3), (6,4), (6,5), (6,6)

Ω =

|B|Pr[B] 1/6|A B|

=Pr [ A | B ] Pr [ A B ] 1/36= =

event A = white die = 1

event B = total = 7

Page 19: Tutorial 5: Discrete Probability I Reference:  cps102/lecture11.ppt Steve Gu Feb 15, 2008.

Independence!A and B are independent events if

Pr[ A | B ] = Pr[ A ]

Pr[ A B ] = Pr[ A ] Pr[ B ]

Pr[ B | A ] = Pr[ B ]

Page 20: Tutorial 5: Discrete Probability I Reference:  cps102/lecture11.ppt Steve Gu Feb 15, 2008.

E.g., A1, A2, A3are independent

events if:

Pr[A1 | A2 A3] = Pr[A1]Pr[A2 | A1 A3] = Pr[A2]Pr[A3 | A1 A2] = Pr[A3]

Pr[A1 | A2 ] = Pr[A1] Pr[A1 | A3 ] = Pr[A1]Pr[A2 | A1 ] = Pr[A2] Pr[A2 | A3] = Pr[A2]Pr[A3 | A1 ] = Pr[A3] Pr[A3 | A2] = Pr[A3]

Independence!A1, A2, …, Ak are independent events if knowing if some of them occurred does not change the probability of any of the others occurring

Page 21: Tutorial 5: Discrete Probability I Reference:  cps102/lecture11.ppt Steve Gu Feb 15, 2008.

Silver and GoldOne bag has two silver coins, another has two gold coins, and the third has one of eachOne bag is selected at random. One coin from it is selected at random. It turns out to be goldWhat is the probability that the other coin is gold?

Page 22: Tutorial 5: Discrete Probability I Reference:  cps102/lecture11.ppt Steve Gu Feb 15, 2008.

Let G1 be the event that the first coin is goldPr[G1] = 1/2

Let G2 be the event that the second coin is goldPr[G2 | G1 ] = Pr[G1 and G2] / Pr[G1]

= (1/3) / (1/2)= 2/3

Note: G1 and G2 are not independent

Page 23: Tutorial 5: Discrete Probability I Reference:  cps102/lecture11.ppt Steve Gu Feb 15, 2008.

The Monty Hall Problem• http://www.youtube.com/watch?v=mhlc7peGl

Gg

Page 24: Tutorial 5: Discrete Probability I Reference:  cps102/lecture11.ppt Steve Gu Feb 15, 2008.

The Monty Hall Problem

http://en.wikipedia.org/wiki/Monty_Hall_problem

Page 25: Tutorial 5: Discrete Probability I Reference:  cps102/lecture11.ppt Steve Gu Feb 15, 2008.

The Monty Hall Problem

Page 26: Tutorial 5: Discrete Probability I Reference:  cps102/lecture11.ppt Steve Gu Feb 15, 2008.

The Monty Hall Problem• Still doubt?• Try playing the game online:– http://www.theproblemsite.com/games/

monty_hall_game.asp

Page 27: Tutorial 5: Discrete Probability I Reference:  cps102/lecture11.ppt Steve Gu Feb 15, 2008.

Thank you!

Q&A