1. Chapter 7: Counting Principles
Discrete Mathematical Structures:
Theory and Applications

2. Learning Objectives
Learn the basic counting principles— multiplication and addition
Explore the pigeonhole principle
Learn about permutations
Learn about combinations
Discrete Mathematical Structures: Theory and Applications

3. Learning Objectives Explore generalized permutations and combinations
Learn about binomial coefficients and explore the algorithm to compute them
Discover the algorithms to generate permutations and combinations
Become familiar with discrete probability
Discrete Mathematical Structures: Theory and Applications

4. Basic Counting Principles
Discrete Mathematical Structures: Theory and Applications

5. Basic Counting Principles
Discrete Mathematical Structures: Theory and Applications

6. Basic Counting Principles
There are three boxes containing books. The first box contains 15 mathematics books by different authors, the second box contains 12 chemistry books by different authors, and the third box contains 10 computer science books by different authors.
A student wants to take a book from one of the three boxes. In how many ways can the student do this?
Discrete Mathematical Structures: Theory and Applications

7. Basic Counting Principles
Suppose tasks T1, T2, and T3 are as follows:
T1 : Choose a mathematics book.
T2 : Choose a chemistry book.
T3 : Choose a computer science book.
Then tasks T1, T2, and T3 can be done in 15, 12, and 10 ways, respectively.
All of these tasks are independent of each other. Hence, the number of ways to do one of these tasks is = 37.
Discrete Mathematical Structures: Theory and Applications

8. Basic Counting Principles
Discrete Mathematical Structures: Theory and Applications

9. Basic Counting Principles
Morgan is a lead actor in a new movie. She needs to shoot a scene in the morning in studio A and an afternoon scene in studio C. She looks at the map and finds that there is no direct route from studio A to studio C. Studio B is located between studios A and C. Morgan’s friends Brad and Jennifer are shooting a movie in studio B. There are three roads, say A1, A2, and A3, from studio A to studio B and four roads, say B1, B2, B3, and B4, from studio B to studio C. In how many ways can Morgan go from studio A to studio C and have lunch with Brad and Jennifer at Studio B?
Discrete Mathematical Structures: Theory and Applications

10. Basic Counting Principles
There are 3 ways to go from studio A to studio B and 4 ways to go from studio B to studio C.
The number of ways to go from studio A to studio C via studio B is 3 * 4 = 12.
Discrete Mathematical Structures: Theory and Applications

11. Basic Counting Principles
Discrete Mathematical Structures: Theory and Applications

12. Basic Counting Principles
Consider two finite sets, X1 and X2. Then
This is called the inclusion-exclusion principle for two finite sets.
Consider three finite sets, A, B, and C. Then
This is called the inclusion-exclusion principle for three finite sets.
Discrete Mathematical Structures: Theory and Applications

13. Basic Counting Principles
Discrete Mathematical Structures: Theory and Applications

14. Pigeonhole Principle
The pigeonhole principle is also known as the Dirichlet drawer principle, or the shoebox principle.
Discrete Mathematical Structures: Theory and Applications

15. Pigeonhole Principle
Discrete Mathematical Structures: Theory and Applications

16. Discrete Mathematical Structures: Theory and Applications

17. Pigeonhole Principle
Discrete Mathematical Structures: Theory and Applications

18. Permutations
Discrete Mathematical Structures: Theory and Applications

19. Permutations
Discrete Mathematical Structures: Theory and Applications

20. Combinations
Discrete Mathematical Structures: Theory and Applications

21. Combinations
Discrete Mathematical Structures: Theory and Applications

22. Generating Permutations and Combinations
Discrete Mathematical Structures: Theory and Applications

23. Generating Permutations and Combinations
Discrete Mathematical Structures: Theory and Applications

24. Generating Permutations and Combinations
Discrete Mathematical Structures: Theory and Applications

25. Generating Permutations and Combinations
Discrete Mathematical Structures: Theory and Applications

26. Generating Permutations and Combinations
Discrete Mathematical Structures: Theory and Applications

27. Generating Permutations and Combinations
Discrete Mathematical Structures: Theory and Applications

28. Discrete Mathematical Structures: Theory and Applications

29. Discrete Mathematical Structures: Theory and Applications

30. Discrete Probability Definition 7.8.1
A probabilistic experiment, or random experiment, or simply an experiment, is the process by which an observation is made.
In probability theory, any action or process that leads to an observation is referred to as an experiment.
Examples include:
Tossing a pair of fair coins.
Throwing a balanced die.
Counting cars that drive past a toll booth.
Discrete Mathematical Structures: Theory and Applications

31. Discrete Probability Definition 7.8.3
The sample space associated with a probabilistic experiment is the set consisting of all possible outcomes of the experiment and is denoted by S.
The elements of the sample space are referred to as sample points.
A discrete sample space is one that contains either a finite or a countable number of distinct sample points.
Discrete Mathematical Structures: Theory and Applications

32. Discrete Probability Definition 7.8.6 Definition 7.8.7
An event in a discrete sample space S is a collection of sample points, i.e., any subset of S. In other words, an event is a set consisting of possible outcomes of the experiment.
Definition 7.8.7
A simple event is an event that cannot be decomposed. Each simple event corresponds to one and only one sample point. Any event that can be decomposed into more than one simple event is called a compound event.
Discrete Mathematical Structures: Theory and Applications

33. Discrete Probability Definition 7.8.8
Let A be an event connected with a probabilistic experiment E and let S be the sample space of E. The event B of nonoccurrence of A is called the complementary event of A. This means that the subset B is the complement A’ of A in S.
In an experiment, two or more events are said to be equally likely if, after taking into consideration all relevant evidences, none can be expected in reference to another.
Discrete Mathematical Structures: Theory and Applications

34. Discrete Probability
Discrete Mathematical Structures: Theory and Applications

35. Discrete Probability Axiomatic Approach
Analyzing the concept of equally likely probability, we see that three conditions must hold.
The probability of occurrence of any event must be greater than or equal to 0.
The probability of the whole sample space must be 1.
If two events are mutually exclusive, the probability of their union is the sum of their respective probabilities.
These three fundamental concepts form the basis of the definition of probability.
Discrete Mathematical Structures: Theory and Applications

36. Discrete Probability
Discrete Mathematical Structures: Theory and Applications

37. Discrete Probability
Discrete Mathematical Structures: Theory and Applications

38. Discrete Probability
Discrete Mathematical Structures: Theory and Applications

39. Discrete Probability Conditional Probability
Consider the throw of two distinct balanced dice. To find the probability of getting a sum of 7, when it is given that the digit in the first die is greater than that in the second.
In the probabilistic experiment of throwing two dice the sample space S consists of 6 * 6 = 36 outcomes.
Assume that each of these outcomes is equally likely. Let A be the event: The sum of the digits of the two dice is 7, and let B be the event: The digit in the first die is greater than the second.
Discrete Mathematical Structures: Theory and Applications

40. Discrete Probability Conditional Probability
P(A)=6/36
B : {(6, 1), (6, 2), (6, 3), (6, 4), (6, 5), (5 , 1), (5 , 2), (5 , 3),(5 , 4), (4, 1), (4, 2), (4, 3), (3, 1), (3, 2), (2, 1)}.
P(B)= 15/36=0.417
Discrete Mathematical Structures: Theory and Applications

41. Discrete Probability Conditional Probability
Let C be the event: The sum of the digits in the two dice is 7 but the digit in the first die is greater than the second. Then C : {(6, 1), (5 , 2), (4, 3)} = A ∩ B.
P(A ∩ B)=3/36=0.083
P(A|B)=0.083/0.417=0.199
Discrete Mathematical Structures: Theory and Applications