Set, Combinatorics, Probability & Number Theory Mathematical Structures for Computer Science Chapter 3 Copyright © 2006 W.H. Freeman & Co.MSCS Slides Set,

Slides:



Advertisements
Similar presentations
COUNTING AND PROBABILITY
Advertisements

Sets, Combinatorics, Probability, and Number Theory Mathematical Structures for Computer Science Chapter 3 Copyright © 2006 W.H. Freeman & Co.MSCS Slides.
Week 21 Basic Set Theory A set is a collection of elements. Use capital letters, A, B, C to denotes sets and small letters a 1, a 2, … to denote the elements.
COUNTING AND PROBABILITY
Counting and Probability The outcome of a random process is sure to occur, but impossible to predict. Examples: fair coin tossing, rolling a pair of dice,
Lecture 14: Oct 28 Inclusion-Exclusion Principle.
Copyright © Zeph Grunschlag, Counting Techniques Zeph Grunschlag.
Discrete Mathematics Lecture 6 Alexander Bukharovich New York University.
The Inclusion/Exclusion Rule for Two or Three Sets
Copyright © Cengage Learning. All rights reserved. CHAPTER 9 COUNTING AND PROBABILITY.
Set, Combinatorics, Probability, and Number Theory Mathematical Structures for Computer Science Chapter 3 Copyright © 2006 W.H. Freeman & Co.MSCS Slides.
Set theory Sets: Powerful tool in computer science to solve real world problems. A set is a collection of distinct objects called elements. Traditionally,
Sets, Combinatorics, Probability, and Number Theory Mathematical Structures for Computer Science Chapter 3 Copyright © 2006 W.H. Freeman & Co.MSCS SlidesProbability.
Sets, Combinatorics, Probability, and Number Theory Mathematical Structures for Computer Science Chapter 3 Copyright © 2006 W.H. Freeman & Co.MSCS SlidesProbability.
Slide 1 Definition Figures 3-4 and 3-5 Events A and B are disjoint (or mutually exclusive) if they cannot both occur together.
Combinatorics 3/15 and 3/ Counting A restaurant offers the following menu: Main CourseVegetablesBeverage BeefPotatoesMilk HamGreen BeansCoffee.
5.1 Basic Probability Ideas
Set, Combinatorics, Probability & Number Theory Mathematical Structures for Computer Science Chapter 3 Copyright © 2006 W.H. Freeman & Co.MSCS Slides Set,
Copyright © Cengage Learning. All rights reserved. CHAPTER 9 COUNTING AND PROBABILITY.
Chapter 8. Section 8. 1 Section Summary Introduction Modeling with Recurrence Relations Fibonacci Numbers The Tower of Hanoi Counting Problems Algorithms.
Topics to be covered: Produce all combinations and permutations of sets. Calculate the number of combinations and permutations of sets of m items taken.
Copyright © Cengage Learning. All rights reserved. CHAPTER 9 COUNTING AND PROBABILITY.
Discrete Mathematical Structures (Counting Principles)
Binomial Coefficients, Inclusion-exclusion principle
9.3 Addition Rule. The basic rule underlying the calculation of the number of elements in a union or difference or intersection is the addition rule.
Set, Combinatorics, Probability & Number Theory Mathematical Structures for Computer Science Chapter 3 Copyright © 2006 W.H. Freeman & Co.MSCS Slides Set,
Fall 2002CMSC Discrete Structures1 One, two, three, we’re… Counting.
Set, Combinatorics, Probability & Number Theory Mathematical Structures for Computer Science Chapter 3 Copyright © 2006 W.H. Freeman & Co.MSCS Slides Set,
1 Lecture 4 (part 1) Combinatorics Reading: Epp Chp 6.
Copyright © Cengage Learning. All rights reserved. CHAPTER 9 COUNTING AND PROBABILITY.
The Principle of Inclusion-Exclusion
15.1 Inclusion/Exclusion OBJ:  to use the inclusion- exclusion principle to solve counting problems involving intersections and unions of sets.
CS201: Data Structures and Discrete Mathematics I
2.2 Set Operations. The Union DEFINITION 1 Let A and B be sets. The union of the sets A and B, denoted by A U B, is the set that contains those elements.
Counting Techniques. L172 Agenda Section 4.1: Counting Basics Sum Rule Product Rule Inclusion-Exclusion.
3.3 Finding Probability Using Sets. Set Theory Definitions Simple event –Has one outcome –E.g. rolling a die and getting a 4 or pulling one name out of.
1 Section 3.3 Principle of Inclusion and Exclusion.
1 Section 6.5 Inclusion/Exclusion. 2 Finding the number of elements in the union of 2 sets From set theory, we know that the number of elements in the.
+ Chapter 5 Overview 5.1 Introducing Probability 5.2 Combining Events 5.3 Conditional Probability 5.4 Counting Methods 1.
Probability theory is the branch of mathematics concerned with analysis of random phenomena. (Encyclopedia Britannica) An experiment: is any action, process.
Basic probability Sep. 16, Introduction Our formal study of probability will base on Set theory Axiomatic approach (base for all our further studies.
Spring 2016 COMP 2300 Discrete Structures for Computation Donghyun (David) Kim Department of Mathematics and Physics North Carolina Central University.
Copyright © Cengage Learning. All rights reserved. CHAPTER 8 RELATIONS.
2/24/20161 One, two, three, we’re… Counting. 2/24/20162 Basic Counting Principles Counting problems are of the following kind: “How many different 8-letter.
Section 7.1. Probability of an Event We first define these key terms: An experiment is a procedure that yields one of a given set of possible outcomes.
Basic Probability. Introduction Our formal study of probability will base on Set theory Axiomatic approach (base for all our further studies of probability)
Chapter 2 1. Chapter Summary Sets (This Slide) The Language of Sets - Sec 2.1 – Lecture 8 Set Operations and Set Identities - Sec 2.2 – Lecture 9 Functions.
Principle of Inclusion and Exclusion
Chapter 7: Counting Principles
Copyright © Zeph Grunschlag,
Introduction to Combinatorics
Discrete Structures for Computer Science
COCS DISCRETE STRUCTURES
Set, Combinatorics, Probability & Number Theory
What is Probability? Quantification of uncertainty.
Applied Discrete Mathematics Week 7: Probability Theory
Copyright © Zeph Grunschlag,
Set Operations Section 2.2.
Set, Combinatorics, Probability & Number Theory
Set, Combinatorics, Probability & Number Theory
COUNTING AND PROBABILITY
15.1 Venn Diagrams.
COUNTING AND PROBABILITY
Copyright © Zeph Grunschlag,
Copyright © Zeph Grunschlag,
Counting Elements of Disjoint Sets: The Addition Rule
Counting Elements of Disjoint Sets: The Addition Rule
Sets, Combinatorics, Probability, and Number Theory
Sets, Combinatorics, Probability, and Number Theory
Presentation transcript:

Set, Combinatorics, Probability & Number Theory Mathematical Structures for Computer Science Chapter 3 Copyright © 2006 W.H. Freeman & Co.MSCS Slides Set, Combinatorics, Probability & Number Theory

Section 3.3Principle of Inclusion & Exclusion; Pigeonhole Principle1 Principle of Inclusion & Exclusion If A and B are subsets of universal set S, then (A-B), (B-A) and (A  B) are disjoint sets. As seen from the figure, (A-B)  (B-A)  (A  B) is the same as A  B. For three disjoint sets |(A-B)  (B-A)  (A  B)| = |A-B| + |B-A| + |A  B| We have solved for two finite sets A and B |A-B| = |A| - |A  B| Hence, using this, we get |(A-B)  (B-A)  (A  B)| = |A| - |A  B| + |B-A| + |A  B| = |A| - |A  B| + |B| - |A  B| + |A  B| Hence, |A  B| = |A| + |B| - |A  B|

Section 3.3Principle of Inclusion & Exclusion; Pigeonhole Principle2 Principle of Inclusion & Exclusion |A  B| = |A| + |B| - |A  B| The above equation represents the principle of inclusion and exclusion for two sets A and B. The name comes from the fact that to calculate the elements in a union, we include the individual elements of A and B but subtract the elements common to A and B so that we don’t count them twice. This principle can be generalized to n sets.

Section 3.3Principle of Inclusion & Exclusion; Pigeonhole Principle3 Example: Inclusion and Exclusion Principle Example 1: How many integers from 1 to 1000 are either multiples of 3 or multiples of 5? Let us assume that A = set of all integers from 1 to 1000 that are multiples of 3. Let us assume that B = set of all integers from 1 to 1000 that are multiples of 5. A  B = The set of all integers from 1 to 1000 that are multiples of either 3 or 5. A  B = The set of all integers that are both multiples of 3 and 5, which also is the set of integers that are multiples of 15. To use the inclusion/exclusion principle to obtain |A  B|, we need |A|, |B| and |A  B|. From 1 to 1000, every third integer is a multiple of 3, each of this multiple can be represented as 3p, for any integer p from 1 through 333, Hence |A| = 333. Similarly for multiples of 5, each multiple of 5 is of the form 5q for some integer q from 1 through 200. Hence, we have |B| = 200.

Section 3.3Principle of Inclusion & Exclusion; Pigeonhole Principle4 Example: Inclusion/exclusion principle for 3 sets To determine the number of multiples of 15 from 1 through 1000, each multiple of 15 is of the form 15r for some integer r from 1 through 66. Hence, |A  B| = 66. From the principle, we have the number of integers either multiples of 3 or multiples of 5 from 1 to 1000 given by |A  B| = – 66 = 467. Example 2: In a class of students undergoing a computer course the following were observed. Out of a total of 50 students: 30 know Pascal, 18 know Fortran, 26 know COBOL, 9 know both Pascal and Fortran, 16 know both Pascal and COBOL, 8 know both Fortran and COBOL, 47 know at least one of the three languages. From this we have to determine a. How many students know none of these languages? b. How many students know all three languages?

Section 3.3Principle of Inclusion & Exclusion; Pigeonhole Principle5 Example: Inclusion/exclusion principle for 3 sets a. We know that 47 students know at least one of the three languages in the class of 50. The number of students who do not know any of three languages is given by the difference between the number of students in class and the number of students who know at least one language. Hence, the students who know none of these languages = 50 – 47 = 3. b. Students know all three languages, so we need to find |A  B  C|.  A = All the students who know Pascal in class. B = All the students who know COBOL in the class. C = All the students who know FORTRAN in class. We have to derive the inclusion/exclusion formula for three sets |A  B  C| = |A  (B  C)| = |A| + |B  C| - |A  (B  C)|

Section 3.3Principle of Inclusion & Exclusion; Pigeonhole Principle6 Example: Inclusion/exclusion principle for 3 sets |A  B  C| = |A  (B  C)| = |A| + |B  C| - |A  (B  C)| = |A| + |B| + |C| - |B  C| - |(A  B)  (A  C)| = |A| + |B| + |C| - |B  C| - (|A  B| + |A  C| - |A  B  C|) = |A| + |B| + |C| - |B  C| - |A  B| - |A  C| + |A  B  C| Given in the problem are the following: |B  C| = 8 |A  B| = 9 |A  C| =16 |A  B  C| = 50 Hence, using the above formula, we have 47 = |A  B  C| Hence, |A  B  C| = 6

Section 3.3Principle of Inclusion & Exclusion; Pigeonhole Principle7 Principle of Inclusion and Exclusion Given the finite sets A 1,……, A n, n ≥ 2, then In this above equation, the term says add together elements in all intersections of the form where i < j. What are these terms for n = 3? this gives A 1  A 2 (i=1, j=2), A 1  A 3 (i=1, j=3), and A 2  A 3 (i=2, j=3). This agrees with the proof of inclusion/exclusion principle for three sets in the previous slide, where A 1 = A, A 2 = B, and A 3 = C.

Section 3.3Principle of Inclusion & Exclusion; Pigeonhole Principle8 Pigeonhole Principle If more than k items are placed into k bins, then at least one bin has more than one item. How many people must be in a room to guarantee that two people have the last name begin with the same initial? How many times must a single die be rolled in order to guarantee getting the same value twice? 26 alphabets, hence 27 people are must be in the room. 6 possible outcomes, hence 7 times.