Mathacle. A; if u is not an element of A, then A. Some of the commonly used sets and notations are

Similar documents
Mathematics. ( : Focus on free Education) (Chapter 16) (Probability) (Class XI) Exercise 16.2

Mathacle. PSet Stats, Concepts In Statistics Level Number Name: Date:

SDS 321: Introduction to Probability and Statistics


STAT 430/510 Probability

Probability Pearson Education, Inc. Slide

NPTEL STRUCTURAL RELIABILITY

Lecture Notes 1 Basic Probability. Elements of Probability. Conditional probability. Sequential Calculation of Probability

Probability: Terminology and Examples Class 2, Jeremy Orloff and Jonathan Bloom

MODULE 2 RANDOM VARIABLE AND ITS DISTRIBUTION LECTURES DISTRIBUTION FUNCTION AND ITS PROPERTIES

Lecture 3 - Axioms of Probability

Statistics Statistical Process Control & Control Charting

Lecture 1 : The Mathematical Theory of Probability

Conditional Probability

Deep Learning for Computer Vision

324 Stat Lecture Notes (1) Probability

ELEG 3143 Probability & Stochastic Process Ch. 1 Experiments, Models, and Probabilities

Conditional Probability

First Digit Tally Marks Final Count

PERMUTATIONS, COMBINATIONS AND DISCRETE PROBABILITY

Mean, Median and Mode. Lecture 3 - Axioms of Probability. Where do they come from? Graphically. We start with a set of 21 numbers, Sta102 / BME102

Events A and B are said to be independent if the occurrence of A does not affect the probability of B.

Unique Solutions R. 4. Probability. C h a p t e r. G l a n c e

Recap. The study of randomness and uncertainty Chances, odds, likelihood, expected, probably, on average,... PROBABILITY INFERENTIAL STATISTICS

Introduction Probability. Math 141. Introduction to Probability and Statistics. Albyn Jones

Sec$on Summary. Assigning Probabilities Probabilities of Complements and Unions of Events Conditional Probability

27 Binary Arithmetic: An Application to Programming

Lecture Lecture 5

CSC Discrete Math I, Spring Discrete Probability

Probability deals with modeling of random phenomena (phenomena or experiments whose outcomes may vary)

Applied Statistics I

What is Probability? Probability. Sample Spaces and Events. Simple Event

Expected Value 7/7/2006

Probability Distributions for Discrete RV

4/17/2012. NE ( ) # of ways an event can happen NS ( ) # of events in the sample space

Introduction to Probability and Sample Spaces

MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.

Lecture 3: Random variables, distributions, and transformations

MA : Introductory Probability

V. RANDOM VARIABLES, PROBABILITY DISTRIBUTIONS, EXPECTED VALUE

Why should you care?? Intellectual curiosity. Gambling. Mathematically the same as the ESP decision problem we discussed in Week 4.

Probability. VCE Maths Methods - Unit 2 - Probability

6.2 Introduction to Probability. The Deal. Possible outcomes: STAT1010 Intro to probability. Definitions. Terms: What are the chances of?

NLP: Probability. 1 Basics. Dan Garrette December 27, E : event space (sample space)

More on Distribution Function

2. Conditional Probability

Part (A): Review of Probability [Statistics I revision]

Probabilistic models

Lecture notes for probability. Math 124

Follow links for Class Use and other Permissions. For more information send to:

Statistics for Financial Engineering Session 2: Basic Set Theory March 19 th, 2006

2. Sets. 2.1&2.2: Sets and Subsets. Combining Sets. c Dr Oksana Shatalov, Spring

Chapter 2 PROBABILITY SAMPLE SPACE

General Info. Grading

Key Concepts. Key Concepts. Event Relations. Event Relations

CS206 Review Sheet 3 October 24, 2018

CS4705. Probability Review and Naïve Bayes. Slides from Dragomir Radev

Outline. 1. Define likelihood 2. Interpretations of likelihoods 3. Likelihood plots 4. Maximum likelihood 5. Likelihood ratio benchmarks

ANSWERS EXERCISE 1.1 EXERCISE 1.2

Chapter 1 Probability Theory

HW2 Solutions, for MATH441, STAT461, STAT561, due September 9th

Homework 4 Solution, due July 23

EE 178 Lecture Notes 0 Course Introduction. About EE178. About Probability. Course Goals. Course Topics. Lecture Notes EE 178

PROBABILITY CHAPTER LEARNING OBJECTIVES UNIT OVERVIEW

(i) Given that a student is female, what is the probability of having a GPA of at least 3.0?

Solutions Manual. Selected odd-numbered problems in. Chapter 2. for. Proof: Introduction to Higher Mathematics. Seventh Edition

4th IIA-Penn State Astrostatistics School July, 2013 Vainu Bappu Observatory, Kavalur

Term Definition Example Random Phenomena

LECTURE NOTES by DR. J.S.V.R. KRISHNA PRASAD

Expected Value. Lecture A Tiefenbruck MWF 9-9:50am Center 212 Lecture B Jones MWF 2-2:50pm Center 214 Lecture C Tiefenbruck MWF 11-11:50am Center 212

Quantitative Methods for Decision Making

ECE 450 Lecture 2. Recall: Pr(A B) = Pr(A) + Pr(B) Pr(A B) in general = Pr(A) + Pr(B) if A and B are m.e. Lecture Overview

Indian Institute of Technology Bombay. Department of Electrical Engineering. EE 325 Probability and Random Processes Lecture Notes 3 July 28, 2014

Basic Set Concepts (2.1)

Steve Smith Tuition: Maths Notes

Discrete Probability Distribution

Notation: X = random variable; x = particular value; P(X = x) denotes probability that X equals the value x.

What is a random variable

Venn Diagrams; Probability Laws. Notes. Set Operations and Relations. Venn Diagram 2.1. Venn Diagrams; Probability Laws. Notes

3rd IIA-Penn State Astrostatistics School July, 2010 Vainu Bappu Observatory, Kavalur

Week 2. Section Texas A& M University. Department of Mathematics Texas A& M University, College Station 22 January-24 January 2019

Lecture 6 Random Variable. Compose of procedure & observation. From observation, we get outcomes

Answer. Find the gradient of the curve y x at x 4

a + b = b + a and a b = b a. (a + b) + c = a + (b + c) and (a b) c = a (b c). a (b + c) = a b + a c and (a + b) c = a c + b c.

Properties of the Integers

CS 441 Discrete Mathematics for CS Lecture 20. Probabilities. CS 441 Discrete mathematics for CS. Probabilities

3.5. First step analysis

Discrete Random Variable

At t = T the investors learn the true state.

Rules of Probability

CLASS 6 July 16, 2015 STT

CISC 1100/1400 Structures of Comp. Sci./Discrete Structures Chapter 7 Probability. Outline. Terminology and background. Arthur G.

Probability (10A) Young Won Lim 6/12/17

Formal Modeling in Cognitive Science Lecture 19: Application of Bayes Theorem; Discrete Random Variables; Distributions. Background.

Formal Modeling in Cognitive Science

9/6/2016. Section 5.1 Probability. Equally Likely Model. The Division Rule: P(A)=#(A)/#(S) Some Popular Randomizers.

22 (Write this number on your Answer Sheet)

Stat 225 Week 1, 8/20/12-8/24/12, Notes: Set Theory

Chapter 13, Probability from Applied Finite Mathematics by Rupinder Sekhon was developed by OpenStax College, licensed by Rice University, and is

Probability theory. References:

Transcription:

Mathale 1. Definitions of Sets set is a olletion of objets. Eah objet in a set is an element of that set. The apital letters are usually used to denote the sets, and the lower ase letters are used to denote the elements. Given a set, if u is an element of, then u ; if u is not an element of, then u. Some of the ommonly used sets and notations are : Natural numbers (inluding zero, sometimes it is alled whole numbers) : Integers : Positive integers : Rational numbers : Real numbers : Complex numbers : Empty set Example 1.1. Some examples: 2. 2. 2. 2. Notations Sometimes the sets an be desribed by listing the elements is olletion of a, b,, d is {0,1, 2, 3,...} T { a, b,, d} The rule based set-builder notations are used to desribe the sets that are not so easily expressed by listing. For instane, the set S onsists of all real numbers between 1and 1, inlusive, is S { x x, 1 x 1} The empty set is the set with no elements. It is denoted by or {}. 1

Example 1.2. Mathale S { x x, x 0 and x 0} Set and set B are eual if they have exatly the same elements. It is denoted as B. If set and set B do not have exatly the same elements, then set and set B are not eual, or B. Example 1.2. If T { a, b,, d} and R { d, a,, b}, then T R. If a set S is finite, then the number of elements in the set S is denoted as ns ( ). The above example, nt ( ) 4. Subsets and Universal Set Set B is the subset of set if for every element in B, it is also in set. This is denoted as B. For any partiular problem, the universal set U is the set that ontains all the subsets involved with the problem. If is a subset of U, the set of elements of U that are not in is alled the omponent of, denoted by. Example 1.3. If set { a, b,, d, e}, B { b, }, U { a, b,, d, e, f, g}, then B, { f, g}, B { a, d, e, f, g}. Example 1.4. If set C { a, b,, d}, then the set S ontains all the subsets of C are S {, { a},{ b},{ },{ d }, { a, b},{ a, },{ a, d},{ b, },{ b, d},{, d }, { a, b, },{ a, b, d},{ a,, d},{ b,, d }, { a, b,, d }} If the number of element is ns ( ) for set S, then there are ns ( ) 4, and there are 4 2 16 For any set, and. subsets. ( ) 2 ns subsets. In this example, 2. Operations on the Events If and B are two events of the universal set U, then 1.) does not our is the omplement of. It is denoted by ; 2

Mathale 2.) either or B our is the union of and B. It is denoted by B ; 3.) both and B our is the intersetion of and B. It is denoted by B ; 4.) ours and B does not our is the differene of and B. It is denoted by B B. Example 2.1. In the experiment of rolling a die one, the universal set is U {1, 2,3,4,5,6}. Let = an odd number turns up B = the number that turns up is divisible by 3 Express the following events in terms of and B: E = the number that turns up is even? F = the number that turns up is odd or is divisible by 3? G = the number that turns up is odd and divisible by 3? H = the number that turns up is odd but not divisible by 3? 3

Mathale Solution: {1,3, 5}, B {3, 6}, and E is the omplement of : E U {2,4,6} F is the union of and B: F B {1,3, 5,6} G is the intersetion of and B: G B {3} H is the differene of and B: H B B {1,5} Notie that E U and E { }. In general, two events and B are exhaustive if B U, in partiular U. That is, either or B will our. Two events and B are disjoint or mutually exlusive if B, where is the impossible event, in partiular,. That is, if ours, then B an not our. Example 2.2. Toss a oin three times. Let H= Heads and T= Tails. Express the following sets in terms of H and T: The universal set U = we throw tails exatly two times B = we throw tails at least two times C = tails did not appear before a heads appeared D = two heads E : F ( C D) : G D : 4

Mathale Solution: U { HHH, HHT, HTH, THH, HTT, TTH, THT, TTT} { HTT, THT, TTH} B { HTT, THT, TTH, TTT} C { HTT, HTH, HHT} D { HHT, THH, HTH} E { HHH, HHT, HTH, THH, TTT} F { HTT, THT, TTH, HHT, HTH} G { HTT, THT, TTH} Example 2.3. If it is hot and B it is sunny, state the following sets in terms of and B: 1.) B 2.) B 3.) B 4.) B 5.) B 6.) B B 5

Mathale Solution: 1.) B it is hot and sunny 2.) B it is not hot but sunny 3.) B it is neither hot nor sunny 4.) B it is not true that it is neither hot nor sunny 5.) B it is hot or sunny B B it is either not hot and sunny or hot and not sunny 6.) Example 2.4. p It is raining Mary is sik t Bob stayed up late last night r Paris is the apital of Frane s John is a loud-mouth Express eah of the following statements in terms of the delarative sentenes above: Negation: 1.) It isn t raining 2.) It is not the ase that Mary isn t sik 3.) Paris is not a apital of Frane 4.) John is in no way a loud-mouth Conjuntion: 5.) It is raining and Mary is sik 6.) Bob stayed up late last night and John is a loud-mouth 7.) Paris isn t the apital of Frane and It isn t raining 8.) John is a loud-mouth but Mary isn t sik 6

Mathale 9.) It is not the ase that it is raining and Mary is sik Disjuntion: 10.) It is raining or Mary is sik 11.) Paris is the apital of Frane and it is raining or John is a loud-mouth 12.) Mary is sik or Mary isn t sik 13.) John is a loud-mouth or Mary is sik or it is raining 14.) It is not the ase that Mary is sik or Bob stayed up late last night Mixed statements: 15.) It is raining but Mary is not sik 16.) Either it is not raining and Mary is sik or Paris is not the apital of Frane 17.) Neither it is raining nor Mary is sik 18.) It is not true that both it is raining and Mary is sik Solution: 1.) p 2.) ( ) 3.) r 4.) s 5.) p 6.) t s 7.) r p 8.) s 9.) p 10.) p 11.) 12.) r p s 7

Mathale 13.) ( s ) p 14.) ( t) 15.) p 16.) 17.) p r p 18.) p 3. DeMorgan s Laws and Properties of Set Operations DeMorgan s Laws. For any two events and B, Example 3.1. B B and B B Let J = John is to blame and M = Mary is to blame. Use DeMorgan s Laws to show event and event B are euivalent, where = It is ertainly not true that neither John nor Mary is to blame B = John or Mary is to blame, or both Hints: find Solution: J and M first and express and B in terms of J, M, J and M. J M John is not to blame Mary is not to blame J M J M B 8

Mathale Properties and Laws of Sets Commutative: B B ssoiative: ( B C) ( B) C Distributive: ( B C) ( B) ( C) ( B C) ( B) ( C) Partition: B B exlusive., where B and B are mutually Other properties: ( ) B B B ( B ) B Example 3.2. Prove eah identity a.) B B ( B ) B ( B ) B ( B ) B ( B ) B ( B ) ( B B ) ( B ) B B 9

Mathale b.) ( B ) B ( B ) ( B ) ( B) ( ) B.) ( B) C ( B C) ( B) C ( B ) C ( B C ) ( B C) ( B C) d.) ( B ( B )) B ( B ( B )) B ( B ) B ( B ) B e.) ( B) B ( B) ( B) ( B ) ( B) ( ) ( B) B 10

Mathale f.) ( B) ( B ) ( B) ( B) ( B) ( B ) ( B ) ( B ) (( B ) B) ( B ) ) (( B) ( B B)) (( ) ( B )) (( B) ) ( ( B )) ( B) ( B ) ( B) ( B ) ( B) ( B ) ( B) ( B) 11