1. Chapter 2. Axioms of Probability

7. Sample Space
Sample space: the set of all possible outcomes of an experiment.
Coin flipping S = {H, T}
Flipping two coins
S = {(H, H), (H, T), (T, H), (T, T)}
Order of finish in a race among the 7 horses having post positions 1, 2, 3, 4, 5, 6, 7, then
S = {all 7! permutations of (1, 2, 3, 4, 5, 6, 7)}
Measuring the lifetime of a light bulb
S = {x: 0 ≤ x < ∞}
Probabilities are defined for events. To talk about a particular event, we first have to look at the space that contains all the possible events – sample space.

8. Sample Space Weather forecast Stock market
Sunny, cloudy, rainy, snowy, …
Stock market
Up, down, no change
[0, +∞)

9. Events Event: any subset E of the sample space S.
A set consisting some possible outcomes of the experiment.
For flipping coin, E = {H} is the event that a head is seen.
Flipping two coins, E = {(H, H), (H, T)} is the event that a head appears on the first coin.
Light bulb, E = {x: 0 ≤ x ≤ 100} is the event that the light bulb lasts less than 100 hours.

10. Operations for events Union Intersection of E and F, EF
Outcomes that are either in E or in F, or in both.
Intersection of E and F, EF
Outcomes that are both in E and F
Also written as

11. If E and F have no outcomes in common, then
we say E and F are mutually exclusive
Union and intersection of multiple events

12. Complement Ec E is contained in F, Outcomes in S that are not in E.
All of the outcomes in E are also in F.

13. Venn diagram
Venn diagram: a graphical representation for illustrating logical relations among events.
Union (AUB) S A B U

14. Venn diagram: intersectionB
A∩B

15. Venn diagram: complimentS A Ac

16. Venn diagram: containedS A B
BA

17. Some rules Commutative laws Associative laws Distributive laws
The rules can be verified using Venn diagram

18. E G F

19. DeMorgan’s laws

20. Intersection of Greek, Latin and Russian upper case letters

21. Looks cool, but…

22. Beef
Wheat
Hamburger

23. Axioms of probability
Probability as a measure of frequency of occurrences.
In coin flipping experiment, how do we know that the proportion of heads obtained in the first n flips will converge to some value as n gets large?
How do we know that, if the experiment is repeatedly performed a second time, we shall again obtain the same limiting proportion of heads?

24. Probability as a measure of belief
Sports
Horse racing
Investment decisions

25. Frequentist vs Bayesian
Different ways of defining probability
Different types of statistical approaches
Frequentist’s and Bayesian methods
Different philosophies for statistics and science
Should we bring one’s belief into science?
Many interesting articles
Link 1
Link 2
Link 3
Link 4

26. Axioms of probability
How about a set of simpler and more self-evident axioms?
We assume that for each event E in the sample space S there exist a value P(E), referred to as the probability of E.

27. Axioms of probability
Axiom 1
Axiom 2
P(S) = 1

28. Axiom 3

29. Ex 3a
Tossing a coin.
If we assume that a head is as likely to appear as a tail, then we have
P({H}) = P({T})=1/2
On the other hand, if the coin were biased and we felt that a head were twice as likely to appear as a tail, then we have
P({H}) = 2/3 and P({T}) = 1/3

30. Ex 3b
Rolling a die.
If we suppose that all six sides are equally likely to appear, then we would have 1/6 for each possible number from 1 to 6.
The probability of rolling an even number would be
P({2, 4, 6}) = P({2}) + P({4}) + P({6}) = 1/2

31. 2.4 Some simple propositions
Proposition 4.1 P(Ec) = 1-P(E)
Proposition 4.2

32. Inclusion-exclusion identity
Proposition 4.3

33. Prove using Venn diagramF
E∩F

34. Movie recommendation system
A movie recommendation system asks a user some questions and then decides what movies it recommends to the user. The goal is to make good recommendations by asking as few questions as possible.
After asking a few questions to a user, the system finds from its database that people with similar characteristics will watch movie A with probability .5, watch movie B with probability .4 and watch both movies with probability .3. What is the probability the user will watch at least one movie? If the probability is greater of equal than .7, the system will recommend the two movies without asking more questions. Otherwise, more questions will be asked.

35. Movie recommendation system
MA: event that the user will watch movie A
MB: event that the user will watch movie B

36. Ex 4a
J is taking two books along on her holiday vacation. With probability .5 she will like the first book; with probability .4 she will like the second book; with probability .3 she will like both book. What is the probability she like neither book?

37. Probability of any of the three events E or F or G occurs.
By proposition 4.3
Distributive law

39. Proposition 4.4 Inclusion-exclusion identity

40. 2.5 Sample space having equally likely outcomes
For S = {1, 2, …, N}
P({1}) = P({2}) = … = P({N})
P({i})=1/N
For any event E
P(E) = (number of outcomes in E) / (number of outcomes in S)
Probability of any events equals the proportion of outcomes in the sample space that are contained in E.

41. Ex 5a
If two dice are rolled, what is the probability that the sum of the upturned faces will equal 7?
There are 6 possible outcomes, namely (1,6), (2,5), (3,4), (4,3), (5,2), (6,1), that give the sum of 7.
Since there are totally 36 outcomes, the desired probability is 1/6.

42. Ex 5b
If 3 balls are randomly drawn from a bowl containing 6 white and 5 black balls, what is the probability that one of the drawn balls is white and the other two black?

43. Ex 5d
An urn contains n balls, of which one is special. If k of these balls are withdrawn one at a time, with each selection being equally likely to be any of the balls that remain at the time, what is the probability that the special ball is chosen?

44. Ex 5e
Suppose that n+m balls, of which n are red and m are blue, are arranged in a linear order in such a way that all (n+m)! possible orderings are equally likely. If we record the result of this experiment by only listing the colors of the successive balls, show that all the possible results remain equally likely.

45. Ex 5f
A poker hand consists of 5 cards. If the cards have distinct consecutive values and are not all of the same suit, we say that the hand is a straight. For instance, a hand consisting of the five of spades, six of spades, seven of spades, eight of spades, and nine of hearts is a straight. What is the probability that one is dealt a straight?

46. Ex 5g
A 5-card poker hand is said to be a full house if it consists of 3 cards of the same denomination and 2 cards of the same denomination. (That is, a full house is three of a kind plus a pair) What is the probability that one is dealt a full house?

47. Ex 5h
In the game of bridge the entire deck of 52 cards is dealt out to 4 players. What is the probability that
(a) one of the players receive all 13 spades;
(b) each players receives 1 ace?

48. Ex 5i
If n people are present in a room, what is the probability that no two of them celebrate their birthday on the same day of the year? How large need n be so that this probability is less than 1/2?
When n ≥ 23, this probability is less than ½.
With 50 people, the probability that at least two
share the same birthday is approximately .97.

49. Ex 5j
A deck of 52 playing cards is shuffled and the cards turned up one at a time until the first ace appears. Is the next card – that is, the card following the first ace – more likely to be the ace of spades or the two of clubs?

50. Ex 5l
A total of 36 members of a club play tennis, 28 play squash, and 18 play badminton. Furthermore, 22 of the members play both tennis and squash, 12 play both tennis and badminton, 9 play both squash and badminton, and 4 play all three sports. How many members of this club play at least one of these sports?

51. A lazy professor
Consider the following problem. Suppose that a certain professor is lazy and lets his students grade their own quizzes. After he collects the quizzes from his n students, he randomly assigns the quizzes back to these n students for grading. If a student is assigned his or her own quiz, we say that it is a match. What is the probability that none of the n students is a match?

52. Ei, i = 1, 2,..,N the event that the ith student picks his/her own quiz.
We first calculate the complementary probability of at least one student’s picking his/her own quiz.

53. Ex 5m The matching problem
Suppose that each of N men at a party throws his hat into the center of the room. The hats are first mixed up, and then each man randomly selects a hat. What is the probability that (a) none of the men selects his own hat; (b) exactly k of the men select their own hats?

54. Summary of Chapter 2 Sample space Event
Union, intersection, complement of events
Axioms 1, 2, 3
Propositions 4.1-4

55. Homework problems
Chapter 2 Problems 3, 5, 7, 8, 9, 12, 14, 31, 37, 41.