What is Probability?
|
|
- Stanley Goodwin
- 5 years ago
- Views:
Transcription
1 Quatificatio of ucertaity. What is Probability? Mathematical model for thigs that occur radomly. Radom ot haphazard, do t kow what will happe o ay oe experimet, but has a log ru order. The cocept of probability is ecessary i work with physical biological or social mechaism that geerate observatio that ca ot be predicted with certaity. Example The relative frequecy of such rasom evets with which they occur i a log series of trails is ofte remarkably stable. Evets possessig this property are called radom or stochastic evets week
2 Relative Frequecy The relative frequecy cocept of probability does ot provide a rigorous defiitio of probability. For our purpose, a iterpretatio based o relative frequecy is a meaigful measure of out belief i the occurrece of the evet. Relative frequecy = proportio of times the eve occurs. Goal: preset a itroductio to the theory of probability, which provides the foudatio for moder statistical iferece. week 2
3 Basic Set Theory A set is a collectio of elemets. Use capital letters, A, B, C to deotes sets ad small letters a, a 2, to deote the elemets of the set. Notatio: meas the elemet a is a elemet of the set A a A A = {a, a 2, a 3 }. The ull, or empty set, deoted by Ф, is the set cosistig of o poits. Thus, Ф is a sub set of every set. The set S cosistig of all elemets uder cosideratio is called the uiversal set. week 3
4 Relatioship Betwee Sets Ay two sets A ad B are equal if A ad B has exactly the same elemets. Notatio: A=B. Example: A = {2, 4, 6}, B = {; is eve ad 2 6} A is a subset of B or A is cotaied i B, if every poit i A is also i B. Notatio: A B Example: A = {2, 4, 6}, B = {; 2 6} = {2, 3, 4, 5, 6} week 4
5 Ve Diagram Sets ad relatioship betwee sets ca be described by usig Ve diagram. Example: We toss a fair die. What is the uiversal set S? week 5
6 Uio ad Itersectio of sets The uio of two sets A ad B, deoted by A B, is the set of all poits that are i at least oe of the sets, i.e., i A or B or both. Example : We toss a fair die The itersectio of two sets A ad B, deoted by A B or AB, is the set of all poits that are members of both A ad B. Example 2: The itersectio of A ad B as defied i example is week 6
7 Properties of uios ad itersectios Uios ad itersectios are: Commutative, i.e., AB = BA ad Associative, i.e., Distributive, i.e., A A A A These laws also apply to arbitrary collectios of sets (ot just pairs). B = ( B C) = ( A B) C B A. ( B C) = ( A B) ( A C) ( B C) = ( A B) ( A C) week 7
8 Disjoit Evets Two sets A ad B are disjoit or mutually exclusive if they have o poits i commo. The A B = Φ. Example 3: Toss a die. Let A = {, 2, 3} ad B = {4, 5}. week 8
9 Complemet of a Set The complemet of a set, deoted by A c or A makes sese oly with respect to some uiversal set. A c is the set of all poits of the uiversal set S that are ot i A. Example: the complemet of set A as defied i example 3 is Note: the sets A ad A c are disjoit. week 9
10 For ay two sets A ad B: C C C ( A B) = A B C C C ( A B) = A B De Morga s Laws For ay collectio of sets A, A 2, A 3, i ay uiversal set S U C A = A = = I( ) C week 0
11 Fiite, Coutable Ifiite ad Ucoutable A set A is fiite if it cotais a fiite umber of elemets. A set A is coutable ifiite if it ca be put ito a oe-to-oe correspodece with the set of positive itegers N. Example: the set of all itegers is coutable ifiite because The whole iterval (0,) is ot coutable ifiite, it is ucoutable. week
12 The Probability Model A experimet is a process by which a observatio is made. For example: roll a die 6 times, toss 3 cois etc. The set of all possible outcomes of a experimet is called the sample space ad is deoted by Ω. The idividual elemets of the sample space are deoted by ω ad are ofte called the sample poits. Examples... A evet is a subset of the sample space. Each sample poit is a simple evet. To defie a probability model we also eed a assessmet of the likelihood of each of these evets. week 2
13 σ Algebra A σ-algebra, F, is a collectio of subsets of Ω satisfyig the followig properties: F cotais Ф ad Ω. F is closed uder takig complemet, i.e., A F F is closed uder takig coutable uio, i.e., A, A2,... F A A2... F A C F. Claim: these properties imply that F is closed uder coutable itersectio. Proof: week 3
14 Probability Measure A probability measure P mappig F [0,] must satisfy A F For, P(A) 0. P(Ω) =. For A A, A,... F, where A i are disjoit, P, 2 3 ( A A A ) = P( A ) + P( A ) + P( ) + L A This property is called coutable additivity. These properties are also called axioms of probability. week 4
15 Formal Defiitio of Probability Model A probability space cosists of three elemets (Ω, F, P) () a set Ω the sample space. (2) a σ-algebra F - collectio of subsets of Ω. (3) a probability measure P mappig F [0,]. week 5
16 Discrete Sample Space A discrete sample space is oe that cotais either a fiite or a coutable umber of distict sample poits. For a discrete sample space it suffices to assig probabilities to each sample poit. There are experimets for which the sample space is ot coutable ad hece is ot discrete. For example, the experimet cosists of measurig the blood pressure of patiets with heart disease. week 6
17 Calculatig Probabilities whe Ω is Fiite Suppose Ω has distict outcomes, Ω = {ω, ω 2,, ω }. The probability of a evet A is P A = P ω ( ) ( ) I may situatios, the outcomes of Ω are equally likely, the, P( ω i ) =. Example, whe rollig a die P i = for i =, 2,, 6. I these situatios the probability that a evet A occurs is () ω A i 6 i P ( A) Example: = # of outcomes for which A occurs = a week 7
18 Basic Combiatorics Multiplicatio Priciple Suppose we are to make a series of decisios. Suppose there are c choices for decisio ad for each of these there are c 2 choices for decisio 2 etc. The the umber of ways the series of decisios ca be made is c c 2 c 3. Example : Suppose I eed to choose a outfit for tomorrow ad I have 2 pairs of jeas to choose from, 3 shirts ad 2 pairs of shoes that matches with this shirts. The I have = 2 differet outfits. week 8
19 Example 2: The Cartesia product of sets A ad B is the set of all pairs (a,b) where a A, b B.If A has 3 elemets (a,a 2,a 3 ) ad B has 2 elemets (b,b 2 ), the their Cartesia product has 6 members; that is A B = {(a,b ), (a,b 2 ), (a 2,b ), (a 2,b 2 ), (a 3,b ), (a 3,b 2 )}. Example 3: The umber of subsets of a set of size is 2. E.g. if A = (a,a 2 ) the all the possible subsets of A are: {a }, {a 2 }, {a,a 2 },. Exercises: 2.38, 2.52 i textbook. Some more exercise:. We toss R differet dies, what is the total umber of possible outcome? 2. How may differet digit umbers ca be composed of the digits -7? 3. A questioeer cosists of 5 questios: Geder (f / m), Religio (Christia, Muslim, Jewish, Hidu, others), livig arragemet (residece, shared apartmet, family), speak Frech (yes / o), marital status. I how may possible ways this questioeer ca be aswered? Φ week 9
20 Permutatio A order arragemet of distict objects is called a permutatio. The umber of ordered arragemets or permutatio of objects is! = ( ) ( 2) ( factorial ). By covetio 0! =. The umber of ordered arragemets or permutatio of k subjects selected from distict objects is ( ) ( 2) ( k +). It is also the umber of ordered subsets of size k from a set of size. Notatio: P k Example: = 3 ad k = 2 = ( ) ( 2) ( k + ) =! ( k)! The umber of ordered arragemets of k subjects selected with replacemet from objects is k. week 20
21 Examples. How may 3 letter words ca be composed from the Eglish Alphabet s.t: (i) No limitatio (ii) The words has 3 differet letters. 2. How may birthday parties ca 0 people have durig a year s.t.: (i) No limitatio (ii) Each birthday is o a differet day people are gettig ito a elevator i a buildig that has 20 floors. (i) I how may ways they ca get off? (ii) I how may ways they ca get off such that each perso gets off o a differet floor? 4. We eed to arrage 4 math books, 3 physics books ad oe statistic book o a shelf. (i) How may possible arragemets exists to do so? (ii) What is the probability that all the math books will be together? week 2
22 week 22 Combiatios The umber of subsets of size k from a set of size whe the order does ot matter is deoted by or ( choose k ). The umber of uordered subsets of size k selected (without replacemet) from available objects is Importat facts: Exercise: Prove the above. k C k )!!(! k k k = 0 = = = = = k k
23 . Exercise 2.54 from the textbook Examples 2. Exercise 2.55 from the textbook 3. Exercise 2.56 from the textbook 4. We eed to select 5 committee members form a class of 70 studets. (i) How may possible samples exists? (ii) How may possible samples exists if the committee members all have differet rules? week 23
24 week 24 The Biomial Theorem For ay umbers a, b ad ay positive iteger The terms are referred to as biomial coefficiet. ( ) = = + i i i b a i b a 0 k
25 Multiomial Coefficiets The umber of ways to partitioig distict objects ito k distict groups cotaiig, 2,, k objects respectively, k where each object appears i exactly oe group ad i = is! 2... = k! 2! k It is called the multiomial coefficiets because they occur i the expasio 2 ( a a + + a ) = a a k + 2 k 2! k 2 i= a k Where the sum is take over all i = 0,,..., such that k i= i = week 25
26 Examples. A small compay gives bouses to their employees at the ed of the year. 5 employees are etitled to receive these bouses of whom 7 employees will receive 00$ bous, 3 will receive 000$ bous ad the rest will receive 3000$ bous. I how may possible ways these bouses ca be distributed? 2. We eed to arrage 5 math books, 4 physics books ad 2 statistic book o a shelf. (i) How may possible arragemets exists to do so? (ii) How may possible arragemets exists so that books of the same subjects will lie side by side? week 26
What is Probability?
Quatificatio of ucertaity. What is Probability? Mathematical model for thigs that occur radomly. Radom ot haphazard, do t ow what will happe o ay oe experimet, but has a log ru order. The cocept of probability
More informationAs stated by Laplace, Probability is common sense reduced to calculation.
Note: Hadouts DO NOT replace the book. I most cases, they oly provide a guidelie o topics ad a ituitive feel. The math details will be covered i class, so it is importat to atted class ad also you MUST
More informationChapter 1 : Combinatorial Analysis
STAT/MATH 394 A - PROBABILITY I UW Autum Quarter 205 Néhémy Lim Chapter : Combiatorial Aalysis A major brach of combiatorial aalysis called eumerative combiatorics cosists of studyig methods for coutig
More informationSets and Probabilistic Models
ets ad Probabilistic Models Berli Che Departmet of Computer ciece & Iformatio Egieerig Natioal Taiwa Normal Uiversity Referece: - D. P. Bertsekas, J. N. Tsitsiklis, Itroductio to Probability, ectios 1.1-1.2
More informationHere are some examples of algebras: F α = A(G). Also, if A, B A(G) then A, B F α. F α = A(G). In other words, A(G)
MATH 529 Probability Axioms Here we shall use the geeral axioms of a probability measure to derive several importat results ivolvig probabilities of uios ad itersectios. Some more advaced results will
More informationChapter 1. Probability
Chapter. Probability. Set defiitios 2. Set operatios 3. Probability itroduced through sets ad relative frequecy 4. Joit ad coditioal probability 5. Idepedet evets 6. Combied experimets 7. Beroulli trials
More informationSets and Probabilistic Models
ets ad Probabilistic Models Berli Che Departmet of Computer ciece & Iformatio Egieerig Natioal Taiwa Normal iversity Referece: - D. P. Bertsekas, J. N. Tsitsiklis, Itroductio to Probability, ectios 1.1-1.2
More informationUNIT 2 DIFFERENT APPROACHES TO PROBABILITY THEORY
UNIT 2 DIFFERENT APPROACHES TO PROBABILITY THEORY Structure 2.1 Itroductio Objectives 2.2 Relative Frequecy Approach ad Statistical Probability 2. Problems Based o Relative Frequecy 2.4 Subjective Approach
More informationAxioms of Measure Theory
MATH 532 Axioms of Measure Theory Dr. Neal, WKU I. The Space Throughout the course, we shall let X deote a geeric o-empty set. I geeral, we shall ot assume that ay algebraic structure exists o X so that
More informationIt is always the case that unions, intersections, complements, and set differences are preserved by the inverse image of a function.
MATH 532 Measurable Fuctios Dr. Neal, WKU Throughout, let ( X, F, µ) be a measure space ad let (!, F, P ) deote the special case of a probability space. We shall ow begi to study real-valued fuctios defied
More informationThe Boolean Ring of Intervals
MATH 532 Lebesgue Measure Dr. Neal, WKU We ow shall apply the results obtaied about outer measure to the legth measure o the real lie. Throughout, our space X will be the set of real umbers R. Whe ecessary,
More informationCEE 522 Autumn Uncertainty Concepts for Geotechnical Engineering
CEE 5 Autum 005 Ucertaity Cocepts for Geotechical Egieerig Basic Termiology Set A set is a collectio of (mutually exclusive) objects or evets. The sample space is the (collectively exhaustive) collectio
More informationChapter 0. Review of set theory. 0.1 Sets
Chapter 0 Review of set theory Set theory plays a cetral role i the theory of probability. Thus, we will ope this course with a quick review of those otios of set theory which will be used repeatedly.
More informationTopic 5: Basics of Probability
Topic 5: Jue 1, 2011 1 Itroductio Mathematical structures lie Euclidea geometry or algebraic fields are defied by a set of axioms. Mathematical reality is the developed through the itroductio of cocepts
More information3.1 Counting Principles
3.1 Coutig Priciples Goal: Cout the umber of objects i a set. Notatio: Whe S is a set, S deotes the umber of objects i the set. This is also called S s cardiality. Additio Priciple: Whe you wat to cout
More informationRandom Variables, Sampling and Estimation
Chapter 1 Radom Variables, Samplig ad Estimatio 1.1 Itroductio This chapter will cover the most importat basic statistical theory you eed i order to uderstad the ecoometric material that will be comig
More informationExercises 1 Sets and functions
Exercises 1 Sets ad fuctios HU Wei September 6, 018 1 Basics Set theory ca be made much more rigorous ad built upo a set of Axioms. But we will cover oly some heuristic ideas. For those iterested studets,
More informationSets. Sets. Operations on Sets Laws of Algebra of Sets Cardinal Number of a Finite and Infinite Set. Representation of Sets Power Set Venn Diagram
Sets MILESTONE Sets Represetatio of Sets Power Set Ve Diagram Operatios o Sets Laws of lgebra of Sets ardial Number of a Fiite ad Ifiite Set I Mathematical laguage all livig ad o-livig thigs i uiverse
More informationCSE 191, Class Note 05: Counting Methods Computer Sci & Eng Dept SUNY Buffalo
Coutig Methods CSE 191, Class Note 05: Coutig Methods Computer Sci & Eg Dept SUNY Buffalo c Xi He (Uiversity at Buffalo CSE 191 Discrete Structures 1 / 48 Need for Coutig The problem of coutig the umber
More informationIntroduction to Probability and Statistics Twelfth Edition
Itroductio to Probability ad Statistics Twelfth Editio Robert J. Beaver Barbara M. Beaver William Medehall Presetatio desiged ad writte by: Barbara M. Beaver Itroductio to Probability ad Statistics Twelfth
More informationMath 155 (Lecture 3)
Math 55 (Lecture 3) September 8, I this lecture, we ll cosider the aswer to oe of the most basic coutig problems i combiatorics Questio How may ways are there to choose a -elemet subset of the set {,,,
More informationIf a subset E of R contains no open interval, is it of zero measure? For instance, is the set of irrationals in [0, 1] is of measure zero?
2 Lebesgue Measure I Chapter 1 we defied the cocept of a set of measure zero, ad we have observed that every coutable set is of measure zero. Here are some atural questios: If a subset E of R cotais a
More informationPermutations, Combinations, and the Binomial Theorem
Permutatios, ombiatios, ad the Biomial Theorem Sectio Permutatios outig methods are used to determie the umber of members of a specific set as well as outcomes of a evet. There are may differet ways to
More informationIntroduction to Probability. Ariel Yadin. Lecture 7
Itroductio to Probability Ariel Yadi Lecture 7 1. Idepedece Revisited 1.1. Some remiders. Let (Ω, F, P) be a probability space. Give a collectio of subsets K F, recall that the σ-algebra geerated by K,
More informationMATHEMATICS. The assessment objectives of the Compulsory Part are to test the candidates :
MATHEMATICS INTRODUCTION The public assessmet of this subject is based o the Curriculum ad Assessmet Guide (Secodary 4 6) Mathematics joitly prepared by the Curriculum Developmet Coucil ad the Hog Kog
More informationMATHEMATICS. The assessment objectives of the Compulsory Part are to test the candidates :
MATHEMATICS INTRODUCTION The public assessmet of this subject is based o the Curriculum ad Assessmet Guide (Secodary 4 6) Mathematics joitly prepared by the Curriculum Developmet Coucil ad the Hog Kog
More informationSTA 348 Introduction to Stochastic Processes. Lecture 1
STA 348 Itroductio to Stochastic Processes Lecture 1 1 Admiis-trivia Istructor: Sotirios Damouras Proouced Sho-tee-ree-os or Sam Cotact Ifo: email: sotirios.damouras@utoroto.ca Office hours: SE/DV 4062,
More informationCommutativity in Permutation Groups
Commutativity i Permutatio Groups Richard Wito, PhD Abstract I the group Sym(S) of permutatios o a oempty set S, fixed poits ad trasiet poits are defied Prelimiary results o fixed ad trasiet poits are
More informationLectures 1 5 Probability Models
Lectures 1 5 Probability Models Aalogy with Geometry: abstract model for chace pheomea Laguage ad Symbols of Chace Experimets: Sample space S, cosistig of all possible outcomes (elemets e, f,..., evets
More informationLecture 10: Mathematical Preliminaries
Lecture : Mathematical Prelimiaries Obective: Reviewig mathematical cocepts ad tools that are frequetly used i the aalysis of algorithms. Lecture # Slide # I this
More informationAn Introduction to Randomized Algorithms
A Itroductio to Radomized Algorithms The focus of this lecture is to study a radomized algorithm for quick sort, aalyze it usig probabilistic recurrece relatios, ad also provide more geeral tools for aalysis
More informationLecture 2: April 3, 2013
TTIC/CMSC 350 Mathematical Toolkit Sprig 203 Madhur Tulsiai Lecture 2: April 3, 203 Scribe: Shubhedu Trivedi Coi tosses cotiued We retur to the coi tossig example from the last lecture agai: Example. Give,
More informationLecture Notes for Analysis Class
Lecture Notes for Aalysis Class Topological Spaces A topology for a set X is a collectio T of subsets of X such that: (a) X ad the empty set are i T (b) Uios of elemets of T are i T (c) Fiite itersectios
More informationPermutations & Combinations. Dr Patrick Chan. Multiplication / Addition Principle Inclusion-Exclusion Principle Permutation / Combination
Discrete Mathematic Chapter 3: C outig 3. The Basics of Coutig 3.3 Permutatios & Combiatios 3.5 Geeralized Permutatios & Combiatios 3.6 Geeratig Permutatios & Combiatios Dr Patrick Cha School of Computer
More informationIntroduction to Probability. Ariel Yadin. Lecture 2
Itroductio to Probability Ariel Yadi Lecture 2 1. Discrete Probability Spaces Discrete probability spaces are those for which the sample space is coutable. We have already see that i this case we ca take
More informationSome Basic Counting Techniques
Some Basic Coutig Techiques Itroductio If A is a oempty subset of a fiite sample space S, the coceptually simplest way to fid the probability of A would be simply to apply the defiitio P (A) = s A p(s);
More informationSection 5.1 The Basics of Counting
1 Sectio 5.1 The Basics of Coutig Combiatorics, the study of arragemets of objects, is a importat part of discrete mathematics. I this chapter, we will lear basic techiques of coutig which has a lot of
More informationQuick Review of Probability
Quick Review of Probability Berli Che Departmet of Computer Sciece & Iformatio Egieerig Natioal Taiwa Normal Uiversity Refereces: 1. W. Navidi. Statistics for Egieerig ad Scietists. Chapter & Teachig Material.
More informationThe Random Walk For Dummies
The Radom Walk For Dummies Richard A Mote Abstract We look at the priciples goverig the oe-dimesioal discrete radom walk First we review five basic cocepts of probability theory The we cosider the Beroulli
More informationSolution. 1 Solutions of Homework 1. Sangchul Lee. October 27, Problem 1.1
Solutio Sagchul Lee October 7, 017 1 Solutios of Homework 1 Problem 1.1 Let Ω,F,P) be a probability space. Show that if {A : N} F such that A := lim A exists, the PA) = lim PA ). Proof. Usig the cotiuity
More informationLecture 12: November 13, 2018
Mathematical Toolkit Autum 2018 Lecturer: Madhur Tulsiai Lecture 12: November 13, 2018 1 Radomized polyomial idetity testig We will use our kowledge of coditioal probability to prove the followig lemma,
More information1 of 7 7/16/2009 6:06 AM Virtual Laboratories > 6. Radom Samples > 1 2 3 4 5 6 7 6. Order Statistics Defiitios Suppose agai that we have a basic radom experimet, ad that X is a real-valued radom variable
More informationQuick Review of Probability
Quick Review of Probability Berli Che Departmet of Computer Sciece & Iformatio Egieerig Natioal Taiwa Normal Uiversity Refereces: 1. W. Navidi. Statistics for Egieerig ad Scietists. Chapter 2 & Teachig
More informationThe Binomial Theorem
The Biomial Theorem Lecture 47 Sectio 9.7 Robb T. Koether Hampde-Sydey College Fri, Apr 8, 204 Robb T. Koether (Hampde-Sydey College The Biomial Theorem Fri, Apr 8, 204 / 25 Combiatios 2 Pascal s Triagle
More informationPb ( a ) = measure of the plausibility of proposition b conditional on the information stated in proposition a. & then using P2
Axioms for Probability Logic Pb ( a ) = measure of the plausibility of propositio b coditioal o the iformatio stated i propositio a For propositios a, b ad c: P: Pb ( a) 0 P2: Pb ( a& b ) = P3: Pb ( a)
More informationDiscrete probability distributions
Discrete probability distributios I the chapter o probability we used the classical method to calculate the probability of various values of a radom variable. I some cases, however, we may be able to develop
More information(ii) Two-permutations of {a, b, c}. Answer. (B) P (3, 3) = 3! (C) 3! = 6, and there are 6 items in (A). ... Answer.
SOLUTIONS Homewor 5 Due /6/19 Exercise. (a Cosider the set {a, b, c}. For each of the followig, (A list the objects described, (B give a formula that tells you how may you should have listed, ad (C verify
More informationProduct measures, Tonelli s and Fubini s theorems For use in MAT3400/4400, autumn 2014 Nadia S. Larsen. Version of 13 October 2014.
Product measures, Toelli s ad Fubii s theorems For use i MAT3400/4400, autum 2014 Nadia S. Larse Versio of 13 October 2014. 1. Costructio of the product measure The purpose of these otes is to preset the
More informationCIS Spring 2018 (instructor Val Tannen)
CIS 160 - Sprig 2018 (istructor Val Tae) Lecture 5 Thursday, Jauary 25 COUNTING We cotiue studyig how to use combiatios ad what are their properties. Example 5.1 How may 8-letter strigs ca be costructed
More informationStat 198 for 134 Instructor: Mike Leong
Chapter 2: Repeated Trials ad Samplig Sectio 2.1 Biomial Distributio 2.2 Normal Approximatio: Method 2.3 Normal Approximatios: Derivatio (Skip) 2.4 Poisso Approximatio 2.5 Radom Samplig Chapter 2 Table
More informationProblems from 9th edition of Probability and Statistical Inference by Hogg, Tanis and Zimmerman:
Math 224 Fall 2017 Homework 4 Drew Armstrog Problems from 9th editio of Probability ad Statistical Iferece by Hogg, Tais ad Zimmerma: Sectio 2.3, Exercises 16(a,d),18. Sectio 2.4, Exercises 13, 14. Sectio
More information} is said to be a Cauchy sequence provided the following condition is true.
Math 4200, Fial Exam Review I. Itroductio to Proofs 1. Prove the Pythagorea theorem. 2. Show that 43 is a irratioal umber. II. Itroductio to Logic 1. Costruct a truth table for the statemet ( p ad ~ r
More information1.2 AXIOMATIC APPROACH TO PROBABILITY AND PROPERTIES OF PROBABILITY MEASURE 1.2 AXIOMATIC APPROACH TO PROBABILITY AND
NTEL- robability ad Distributios MODULE 1 ROBABILITY LECTURE 2 Topics 1.2 AXIOMATIC AROACH TO ROBABILITY AND ROERTIES OF ROBABILITY MEASURE 1.2.1 Iclusio-Exclusio Forula I the followig sectio we will discuss
More informationPROBABILITY LOGIC: Part 2
James L Bec 2 July 2005 PROBABILITY LOGIC: Part 2 Axioms for Probability Logic Based o geeral cosideratios, we derived axioms for: Pb ( a ) = measure of the plausibility of propositio b coditioal o the
More informationAdvanced Stochastic Processes.
Advaced Stochastic Processes. David Gamarik LECTURE 2 Radom variables ad measurable fuctios. Strog Law of Large Numbers (SLLN). Scary stuff cotiued... Outlie of Lecture Radom variables ad measurable fuctios.
More informationii. O = {x x = 2k + 1 for some integer k} (This set could have been listed O = { -3, -1, 1, 3, 5 }.)
Sets 1 Math 3312 Set Theory Sprig 2008 Itroductio Set theory is a brach of mathematics that deals with the properties of welldefied collectios of objects, which may or may ot be of a mathematical ature,
More informationSingular Continuous Measures by Michael Pejic 5/14/10
Sigular Cotiuous Measures by Michael Peic 5/4/0 Prelimiaries Give a set X, a σ-algebra o X is a collectio of subsets of X that cotais X ad ad is closed uder complemetatio ad coutable uios hece, coutable
More informationNOTES ON PROBABILITY THEORY FOR ENEE 620. Adrian Papamarcou. (with references to Probability and Measure by Patrick Billingsley)
NOTES ON PROBABILITY THEORY FOR ENEE 620 Adria Papamarcou (with refereces to Probability ad Measure by Patrick Billigsley) Revised February 2006 0 1. Itroductio to radom processes A radom process is a
More information1 Introduction. 1.1 Notation and Terminology
1 Itroductio You have already leared some cocepts of calculus such as limit of a sequece, limit, cotiuity, derivative, ad itegral of a fuctio etc. Real Aalysis studies them more rigorously usig a laguage
More informationProbability and Statistics
robability ad Statistics rof. Zheg Zheg Radom Variable A fiite sigle valued fuctio.) that maps the set of all eperimetal outcomes ito the set of real umbers R is a r.v., if the set ) is a evet F ) for
More informationClass notes on sec+ons 4.2, 5.1, 5.2 and 5.3
Class otes o sec+os 4.2, 5.1, 5.2 ad 5.3 Topics covered: Recursive defii1o Rudimetary discussio o rela1os Fuc1os: ijec1ve, surjec1ve ad bijec1ve Recursive Defii+os Recursively defied sequece Cosider the
More informationCS 330 Discussion - Probability
CS 330 Discussio - Probability March 24 2017 1 Fudametals of Probability 11 Radom Variables ad Evets A radom variable X is oe whose value is o-determiistic For example, suppose we flip a coi ad set X =
More informationLecture 3 : Random variables and their distributions
Lecture 3 : Radom variables ad their distributios 3.1 Radom variables Let (Ω, F) ad (S, S) be two measurable spaces. A map X : Ω S is measurable or a radom variable (deoted r.v.) if X 1 (A) {ω : X(ω) A}
More informationGenerating Functions II
Geeratig Fuctios II Misha Lavrov ARML Practice 5/4/2014 Warm-up problems 1. Solve the recursio a +1 = 2a, a 0 = 1 by usig commo sese. 2. Solve the recursio b +1 = 2b + 1, b 0 = 1 by usig commo sese ad
More informationApproximations and more PMFs and PDFs
Approximatios ad more PMFs ad PDFs Saad Meimeh 1 Approximatio of biomial with Poisso Cosider the biomial distributio ( b(k,,p = p k (1 p k, k λ: k Assume that is large, ad p is small, but p λ at the limit.
More informationIn number theory we will generally be working with integers, though occasionally fractions and irrationals will come into play.
Number Theory Math 5840 otes. Sectio 1: Axioms. I umber theory we will geerally be workig with itegers, though occasioally fractios ad irratioals will come ito play. Notatio: Z deotes the set of all itegers
More informationLecture Overview. 2 Permutations and Combinations. n(n 1) (n (k 1)) = n(n 1) (n k + 1) =
COMPSCI 230: Discrete Mathematics for Computer Sciece April 8, 2019 Lecturer: Debmalya Paigrahi Lecture 22 Scribe: Kevi Su 1 Overview I this lecture, we begi studyig the fudametals of coutig discrete objects.
More informationLecture 1 Probability and Statistics
Wikipedia: Lecture 1 Probability ad Statistics Bejami Disraeli, British statesma ad literary figure (1804 1881): There are three kids of lies: lies, damed lies, ad statistics. popularized i US by Mark
More informationLecture Notes 15 Hypothesis Testing (Chapter 10)
1 Itroductio Lecture Notes 15 Hypothesis Testig Chapter 10) Let X 1,..., X p θ x). Suppose we we wat to kow if θ = θ 0 or ot, where θ 0 is a specific value of θ. For example, if we are flippig a coi, we
More informationB Supplemental Notes 2 Hypergeometric, Binomial, Poisson and Multinomial Random Variables and Borel Sets
B671-672 Supplemetal otes 2 Hypergeometric, Biomial, Poisso ad Multiomial Radom Variables ad Borel Sets 1 Biomial Approximatio to the Hypergeometric Recall that the Hypergeometric istributio is fx = x
More informationPutnam Training Exercise Counting, Probability, Pigeonhole Principle (Answers)
Putam Traiig Exercise Coutig, Probability, Pigeohole Pricile (Aswers) November 24th, 2015 1. Fid the umber of iteger o-egative solutios to the followig Diohatie equatio: x 1 + x 2 + x 3 + x 4 + x 5 = 17.
More informationDiscrete Mathematics for CS Spring 2005 Clancy/Wagner Notes 21. Some Important Distributions
CS 70 Discrete Mathematics for CS Sprig 2005 Clacy/Wager Notes 21 Some Importat Distributios Questio: A biased coi with Heads probability p is tossed repeatedly util the first Head appears. What is the
More informationExpectation and Variance of a random variable
Chapter 11 Expectatio ad Variace of a radom variable The aim of this lecture is to defie ad itroduce mathematical Expectatio ad variace of a fuctio of discrete & cotiuous radom variables ad the distributio
More informationn outcome is (+1,+1, 1,..., 1). Let the r.v. X denote our position (relative to our starting point 0) after n moves. Thus X = X 1 + X 2 + +X n,
CS 70 Discrete Mathematics for CS Sprig 2008 David Wager Note 9 Variace Questio: At each time step, I flip a fair coi. If it comes up Heads, I walk oe step to the right; if it comes up Tails, I walk oe
More informationProbability and statistics: basic terms
Probability ad statistics: basic terms M. Veeraraghava August 203 A radom variable is a rule that assigs a umerical value to each possible outcome of a experimet. Outcomes of a experimet form the sample
More informationSets are collection of objects that can be displayed in different forms. Two of these forms are called Roster Method and Builder Set Notation.
Sectio 2.1 Set ad Set Operators Defiitio of a set set is a collectio of objects thigs or umbers. Sets are collectio of objects that ca be displayed i differet forms. Two of these forms are called Roster
More informationDiscrete Mathematics and Probability Theory Spring 2012 Alistair Sinclair Note 15
CS 70 Discrete Mathematics ad Probability Theory Sprig 2012 Alistair Siclair Note 15 Some Importat Distributios The first importat distributio we leared about i the last Lecture Note is the biomial distributio
More informationARRANGEMENTS IN A CIRCLE
ARRANGEMENTS IN A CIRCLE Whe objects are arraged i a circle, the total umber of arragemets is reduced. The arragemet of (say) four people i a lie is easy ad o problem (if they liste of course!!). With
More informationMeasure and Measurable Functions
3 Measure ad Measurable Fuctios 3.1 Measure o a Arbitrary σ-algebra Recall from Chapter 2 that the set M of all Lebesgue measurable sets has the followig properties: R M, E M implies E c M, E M for N implies
More informationHomework 3. = k 1. Let S be a set of n elements, and let a, b, c be distinct elements of S. The number of k-subsets of S is
Homewor 3 Chapter 5 pp53: 3 40 45 Chapter 6 p85: 4 6 4 30 Use combiatorial reasoig to prove the idetity 3 3 Proof Let S be a set of elemets ad let a b c be distict elemets of S The umber of -subsets of
More informationM A T H F A L L CORRECTION. Algebra I 1 4 / 1 0 / U N I V E R S I T Y O F T O R O N T O
M A T H 2 4 0 F A L L 2 0 1 4 HOMEWORK ASSIGNMENT #4 CORRECTION Algebra I 1 4 / 1 0 / 2 0 1 4 U N I V E R S I T Y O F T O R O N T O P r o f e s s o r : D r o r B a r - N a t a Correctio Homework Assigmet
More informationDiscrete Mathematics and Probability Theory Summer 2014 James Cook Note 15
CS 70 Discrete Mathematics ad Probability Theory Summer 2014 James Cook Note 15 Some Importat Distributios I this ote we will itroduce three importat probability distributios that are widely used to model
More informationInfinite Sequences and Series
Chapter 6 Ifiite Sequeces ad Series 6.1 Ifiite Sequeces 6.1.1 Elemetary Cocepts Simply speakig, a sequece is a ordered list of umbers writte: {a 1, a 2, a 3,...a, a +1,...} where the elemets a i represet
More informationChapter 1. Probability Spaces. 1.1 The sample space. Examples:
Chapter 1 Probability Spaces 1.1 The sample space The ituitive meaig of probability is related to some experimet, whether real or coceptual (e.g., playig the lottery, testig whether a ewbor is a boy, measurig
More informationTopic 10: Introduction to Estimation
Topic 0: Itroductio to Estimatio Jue, 0 Itroductio I the simplest possible terms, the goal of estimatio theory is to aswer the questio: What is that umber? What is the legth, the reactio rate, the fractio
More informationCombinatorics II. Combinatorics. Product Rule. Sum Rule II. Theorem (Product Rule) Theorem (Sum Rule)
Combiatorics Combiatorics I Slides by Christopher M. Bourke Istructor: Berthe Y. Choueiry Fall 27 Computer Sciece & Egieerig 235 to Discrete Mathematics Sectios 5.-5.6 & 7.5-7.6 of Rose cse235@cse.ul.edu
More informationIntegrable Functions. { f n } is called a determining sequence for f. If f is integrable with respect to, then f d does exist as a finite real number
MATH 532 Itegrable Fuctios Dr. Neal, WKU We ow shall defie what it meas for a measurable fuctio to be itegrable, show that all itegral properties of simple fuctios still hold, ad the give some coditios
More informationProblem Set 2 Solutions
CS271 Radomess & Computatio, Sprig 2018 Problem Set 2 Solutios Poit totals are i the margi; the maximum total umber of poits was 52. 1. Probabilistic method for domiatig sets 6pts Pick a radom subset S
More informationBinomial distribution questions: formal word problems
Biomial distributio questios: formal word problems For the followig questios, write the iformatio give i a formal way before solvig the problem, somethig like: X = umber of... out of 2, so X B(2, 0.2).
More informationFrequentist Inference
Frequetist Iferece The topics of the ext three sectios are useful applicatios of the Cetral Limit Theorem. Without kowig aythig about the uderlyig distributio of a sequece of radom variables {X i }, for
More informationStatistical Inference (Chapter 10) Statistical inference = learn about a population based on the information provided by a sample.
Statistical Iferece (Chapter 10) Statistical iferece = lear about a populatio based o the iformatio provided by a sample. Populatio: The set of all values of a radom variable X of iterest. Characterized
More informationFACULTY OF MATHEMATICAL STUDIES MATHEMATICS FOR PART I ENGINEERING. Lectures
FACULTY OF MATHEMATICAL STUDIES MATHEMATICS FOR PART I ENGINEERING Lectures MODULE 5 STATISTICS II. Mea ad stadard error of sample data. Biomial distributio. Normal distributio 4. Samplig 5. Cofidece itervals
More informationStatistics 511 Additional Materials
Cofidece Itervals o mu Statistics 511 Additioal Materials This topic officially moves us from probability to statistics. We begi to discuss makig ifereces about the populatio. Oe way to differetiate probability
More informationThe Borel hierarchy classifies subsets of the reals by their topological complexity. Another approach is to classify them by size.
Lecture 7: Measure ad Category The Borel hierarchy classifies subsets of the reals by their topological complexity. Aother approach is to classify them by size. Filters ad Ideals The most commo measure
More informationWeek 5-6: The Binomial Coefficients
Wee 5-6: The Biomial Coefficiets March 6, 2018 1 Pascal Formula Theorem 11 (Pascal s Formula For itegers ad such that 1, ( ( ( 1 1 + 1 The umbers ( 2 ( 1 2 ( 2 are triagle umbers, that is, The petago umbers
More informationA PROBABILITY PRIMER
CARLETON COLLEGE A ROBABILITY RIMER SCOTT BIERMAN (Do ot quote without permissio) A robability rimer INTRODUCTION The field of probability ad statistics provides a orgaizig framework for systematically
More informationCHAPTER SUMMARIES MAT102 Dr J Lubowsky Page 1 of 13 Chapter 1: Introduction to Statistics
CHAPTER SUMMARIES MAT102 Dr J Lubowsky Page 1 of 13 Chapter 1: Itroductio to Statistics Misleadig Iformatio: Surveys ad advertisig claims ca be biased by urepresetative samples, biased questios, iappropriate
More informationPairs of disjoint q-element subsets far from each other
Pairs of disjoit q-elemet subsets far from each other Hikoe Eomoto Departmet of Mathematics, Keio Uiversity 3-14-1 Hiyoshi, Kohoku-Ku, Yokohama, 223 Japa, eomoto@math.keio.ac.jp Gyula O.H. Katoa Alfréd
More informationCS 171 Lecture Outline October 09, 2008
CS 171 Lecture Outlie October 09, 2008 The followig theorem comes very hady whe calculatig the expectatio of a radom variable that takes o o-egative iteger values. Theorem: Let Y be a radom variable that
More informationMath 475, Problem Set #12: Answers
Math 475, Problem Set #12: Aswers A. Chapter 8, problem 12, parts (b) ad (d). (b) S # (, 2) = 2 2, sice, from amog the 2 ways of puttig elemets ito 2 distiguishable boxes, exactly 2 of them result i oe
More information