Section Mathematical Induction and Section Strong Induction and Well-Ordering
|
|
- Chrystal Carson
- 6 years ago
- Views:
Transcription
1 Sectio Mathematical Iductio ad Sectio 4. - Strog Iductio ad Well-Orderig A very special rule of iferece! Defiitio: A set S is well ordered if every subset has a least elemet. Note: [0, 1] is ot well ordered sice (0,1] does ot have a least elemet. Examples: N is well ordered (uder the relatio) Ay coutably ifiite set ca be well ordered The least elemet i a subset is determied by a bijectio (list) which exists from N to the coutably ifiite set. Z ca be well ordered but it is ot well ordered uder the relatio (Z has o smallest elemet). The set of fiite strigs over a alphabet usig lexicographic orderig is well ordered. Let P(x) be a predicate over a well ordered set S. The problem is to prove xp(x). Prepared by: David F. McAllister TP , 007 McGraw-Hill
2 The rule of iferece called The (first) priciple of Mathematical Iductio ca sometimes be used to establish the uiversally quatified assertio. I the case that S = N, the atural umbers, the priciple has the followig form. The hypotheses are P(0) P() P( +1) xp(x) ad H1: P(0) H: P() P( +1) for arbitrary. H1 is called The Basis Step. H is called The Iductio (Iductive) Step We first prove that the predicate is true for the smallest elemet of the set S (0 if S = N). We the show if it is true for a elemet x ( if S = N) implies it is true for the ext elemet i the set ( + 1 if S = N). Prepared by: David F. McAllister TP 1999, 007 McGraw-Hill
3 The kowig it is true for the first elemet meas it must be true for the elemet followig the first or the secod elemet kowig it is true for the secod elemet implies it is true for the third ad so forth. Therefore, iductio is equivalet to modus poes applied a coutable umber of times!! It is like a row of domios: If the th domio falls over the (+1)st must fall over so pushig the first oe dow meas all must fall dow. To prove H we ormally use a Direct Proof. Assumig P() to be true for arbitrary is called the Iductio (Iductive) Hypothesis. Example: (a classic) Prepared by: David F. McAllister TP , 007 McGraw-Hill
4 Prove: i = ( +1) I logical otatio we wish to show [ i = Hece, the predicate P() is ( +1) ] i = ( +1). Note: Idetifyig P(x) is ofte the hardest part! We first prove H1: P(0): 0 = 0 i = Now establish H usig a direct proof: State the Iductio Hypotheses : Assume P() is true for arbitrary 0(0 +1) (this looks as if you are assumig the truth of what is to be proved ad hece we have a circular argumet. This is ot the case.) Now use this ad aythig else you kow to establish that P( + 1) must be true. Prepared by: David F. McAllister TP , 007 McGraw-Hill
5 P( + 1) is the assertio +1 i = ( +1)(( +1)+1) (Note: Write dow the assertio P(+1)! Do't make it hard for yourself because you do't kow what it is you are to prove.) But, +1 i = i + ( +1) i=1 usig the property of summatios. Now apply the iductio hypothesis. Note: you must maipulate the assertio P(+1) so that you ca apply the iductio hypothesis P(). If you do ot apply the iductio hypothesis somewhere, it is ot a valid iductio proof. Use the assumptio P() to substitute to get ( +1) for i +1 i = ( +1) +( +1) ad we maipulate the right side to get Prepared by: David F. McAllister TP , 007 McGraw-Hill
6 +1 i = which is exactly P(+1). Hece, we have established H. ( +1)(( +1)+1) We ow say by the Priciple of Mathematical Iductio it follows that P() is true for all or [ i = ( +1) ] Q.E.D. We ca use the Priciple to prove more geeral assertios because N is well ordered. Suppose we wish to prove for some specific iteger k x[ k P(x)] Now we merely chage the basis step to P(k) ad cotiue. Example: Show is O(). Prepared by: David F. McAllister TP , 007 McGraw-Hill
7 Proof: We must fid C ad k such that wheever k (or > k-1) C If we try C = 1, the the assertio is ot true util k = 5. Hece we prove by iductio that for all 5. The assertio becomes [ ] ad the predicate P() is Basis step: P(5): 3x5 + 5 = 0 (5) which establishes the basis step. The iductio hypothesis: assume P(): is true for arbitrary. Use this ad ay other clever thigs you kow to show P(+1). Write dow the assertio P(+1)! P(+1): 3( +1)+ 5 ( +1) Now put it i a form which will allow you to apply the iductio hypothesis. Prepared by: David F. McAllister TP , 007 McGraw-Hill
8 We rewrite the left side as (3 + 5) + 3 ad apply the iductio hypothesis to (3+5) which we assume is less tha. Now we must show that which is true iff which is true iff + 3 ( + 1) = But we have already restricted 5 so 1 must hold. Hece we have established the iductio step ad the assertio must be true for all : [ ] Q.E.D. Note: i doubly quatified assertios of the form m [P(m,)] we ofte assume m (or ) is arbitrary to elimiate a quatifier ad prove the remaiig result usig iductio. Prepared by: David F. McAllister TP , 007 McGraw-Hill
9 Aother Example: All horses are the same color. Proof: We do iductio o the size of sets of horses of the same color. Basis step: The assertio is obviously true for all sets of 0 horses (ad all sets with 1 horse). Iductio step: The iductio hypothesis becomes 'Assume the assertio is true for all sets with horses.' Now show it must be true for all sets of +1 horses. But every set of +1 horses has a overlap of horses which are the same color. + 1 horses X X X X X X X... X X horses horses Hece the set of +1 horses must have the same color. Therefore, all horses have the same color. What's wrog? Prepared by: David F. McAllister TP , 007 McGraw-Hill
10 The Secod Priciple of Mathematical Iductio The rule of iferece becomes: H1: P(0) H: P(0) P(1)... P() P( +1) xp(x) The two rules are equivalet but sometimes the secod is easier to apply. See your text for the classic examples. Prepared by: David F. McAllister TP , 007 McGraw-Hill
A New Constructive Proof of Graham's Theorem and More New Classes of Functionally Complete Functions
A New Costructive Proof of Graham's Theorem ad More New Classes of Fuctioally Complete Fuctios Azhou Yag, Ph.D. Zhu-qi Lu, Ph.D. Abstract A -valued two-variable truth fuctio is called fuctioally complete,
More informationThe Limit of a Sequence (Brief Summary) 1
The Limit of a Sequece (Brief Summary). Defiitio. A real umber L is a it of a sequece of real umbers if every ope iterval cotaiig L cotais all but a fiite umber of terms of the sequece. 2. Claim. A sequece
More informationSolutions to Problem Sheet 1
Solutios to Problem Sheet ) Use Theorem.4 to prove that p log for all real x 3. This is a versio of Theorem.4 with the iteger N replaced by the real x. Hit Give x 3 let N = [x], the largest iteger x. The,
More information1 The Power of Compounding
1 The Power of Compoudig 1.1 Simple vs Compoud Iterest You deposit $1,000 i a bak that pays 5% iterest each year. At the ed of the year you will have eared $50. The bak seds you a check for $50 dollars.
More informationNotes on Expected Revenue from Auctions
Notes o Epected Reveue from Auctios Professor Bergstrom These otes spell out some of the mathematical details about first ad secod price sealed bid auctios that were discussed i Thursday s lecture You
More informationBinomial Theorem. Combinations with repetition.
Biomial.. Permutatios ad combiatios Give a set with elemets. The umber of permutatios of the elemets the set: P() =! = ( 1) ( 2)... 1 The umber of r-permutatios of the set: P(, r) =! = ( 1) ( 2)... ( r
More informationSequences and Series
Sequeces ad Series Matt Rosezweig Cotets Sequeces ad Series. Sequeces.................................................. Series....................................................3 Rudi Chapter 3 Exercises........................................
More informationLecture 9: The law of large numbers and central limit theorem
Lecture 9: The law of large umbers ad cetral limit theorem Theorem.4 Let X,X 2,... be idepedet radom variables with fiite expectatios. (i) (The SLLN). If there is a costat p [,2] such that E X i p i i=
More information5. Best Unbiased Estimators
Best Ubiased Estimators http://www.math.uah.edu/stat/poit/ubiased.xhtml 1 of 7 7/16/2009 6:13 AM Virtual Laboratories > 7. Poit Estimatio > 1 2 3 4 5 6 5. Best Ubiased Estimators Basic Theory Cosider agai
More informationMath 312, Intro. to Real Analysis: Homework #4 Solutions
Math 3, Itro. to Real Aalysis: Homework #4 Solutios Stephe G. Simpso Moday, March, 009 The assigmet cosists of Exercises 0.6, 0.8, 0.0,.,.3,.6,.0,.,. i the Ross textbook. Each problem couts 0 poits. 0.6.
More informationUsing Math to Understand Our World Project 5 Building Up Savings And Debt
Usig Math to Uderstad Our World Project 5 Buildig Up Savigs Ad Debt Note: You will have to had i aswers to all umbered questios i the Project Descriptio See the What to Had I sheet for additioal materials
More informationLimits of sequences. Contents 1. Introduction 2 2. Some notation for sequences The behaviour of infinite sequences 3
Limits of sequeces I this uit, we recall what is meat by a simple sequece, ad itroduce ifiite sequeces. We explai what it meas for two sequeces to be the same, ad what is meat by the -th term of a sequece.
More informationChapter 10 - Lecture 2 The independent two sample t-test and. confidence interval
Assumptios Idepedet Samples - ukow σ 1, σ - 30 or m 30 - Upooled case Idepedet Samples - ukow σ 1, σ - 30 or m 30 - Pooled case Idepedet samples - Pooled variace - Large samples Chapter 10 - Lecture The
More information14.30 Introduction to Statistical Methods in Economics Spring 2009
MIT OpeCourseWare http://ocwmitedu 430 Itroductio to Statistical Methods i Ecoomics Sprig 009 For iformatio about citig these materials or our Terms of Use, visit: http://ocwmitedu/terms 430 Itroductio
More informationA random variable is a variable whose value is a numerical outcome of a random phenomenon.
The Practice of Statistics, d ed ates, Moore, ad Stares Itroductio We are ofte more iterested i the umber of times a give outcome ca occur tha i the possible outcomes themselves For example, if we toss
More informationInferential Statistics and Probability a Holistic Approach. Inference Process. Inference Process. Chapter 8 Slides. Maurice Geraghty,
Iferetial Statistics ad Probability a Holistic Approach Chapter 8 Poit Estimatio ad Cofidece Itervals This Course Material by Maurice Geraghty is licesed uder a Creative Commos Attributio-ShareAlike 4.0
More informationFurther Pure 1 Revision Topic 5: Sums of Series
The OCR syllabus says that cadidates should: Further Pure Revisio Topic 5: Sums of Series Cadidates should be able to: (a) use the stadard results for Σr, Σr, Σr to fid related sums; (b) use the method
More informationChapter 5: Sequences and Series
Chapter 5: Sequeces ad Series 1. Sequeces 2. Arithmetic ad Geometric Sequeces 3. Summatio Notatio 4. Arithmetic Series 5. Geometric Series 6. Mortgage Paymets LESSON 1 SEQUENCES I Commo Core Algebra I,
More informationAnnual compounding, revisited
Sectio 1.: No-aual compouded iterest MATH 105: Cotemporary Mathematics Uiversity of Louisville August 2, 2017 Compoudig geeralized 2 / 15 Aual compoudig, revisited The idea behid aual compoudig is that
More informationWe learned: $100 cash today is preferred over $100 a year from now
Recap from Last Week Time Value of Moey We leared: $ cash today is preferred over $ a year from ow there is time value of moey i the form of willigess of baks, busiesses, ad people to pay iterest for its
More informationEstimating Proportions with Confidence
Aoucemets: Discussio today is review for midterm, o credit. You may atted more tha oe discussio sectio. Brig sheets of otes ad calculator to midterm. We will provide Scatro form. Homework: (Due Wed Chapter
More information. (The calculated sample mean is symbolized by x.)
Stat 40, sectio 5.4 The Cetral Limit Theorem otes by Tim Pilachowski If you have t doe it yet, go to the Stat 40 page ad dowload the hadout 5.4 supplemet Cetral Limit Theorem. The homework (both practice
More informationToday: Finish Chapter 9 (Sections 9.6 to 9.8 and 9.9 Lesson 3)
Today: Fiish Chapter 9 (Sectios 9.6 to 9.8 ad 9.9 Lesso 3) ANNOUNCEMENTS: Quiz #7 begis after class today, eds Moday at 3pm. Quiz #8 will begi ext Friday ad ed at 10am Moday (day of fial). There will be
More informationphysicsandmathstutor.com
physicsadmathstutor.com 9. (a) A geometric series has first term a ad commo ratio r. Prove that the sum of the first terms of the series is a(1 r ). 1 r (4) Mr. Kig will be paid a salary of 35 000 i the
More informationMATH 205 HOMEWORK #1 OFFICIAL SOLUTION
MATH 205 HOMEWORK #1 OFFICIAL SOLUTION Problem 2: Show that if there exists a ijective field homomorhism F F the char F = char F. Solutio: Let ϕ be the homomorhism, suose that char F =. Note that ϕ(1 =
More information1 Random Variables and Key Statistics
Review of Statistics 1 Radom Variables ad Key Statistics Radom Variable: A radom variable is a variable that takes o differet umerical values from a sample space determied by chace (probability distributio,
More informationGame Theory. Lecture Notes By Y. Narahari. Department of Computer Science and Automation Indian Institute of Science Bangalore, India July 2012
Game Theory Lecture Notes By Y. Narahari Departmet of Computer Sciece ad Automatio Idia Istitute of Sciece Bagalore, Idia July 01 Chapter 4: Domiat Strategy Equilibria Note: This is a oly a draft versio,
More informationMA Lesson 11 Section 1.3. Solving Applied Problems with Linear Equations of one Variable
MA 15200 Lesso 11 Sectio 1. I Solvig Applied Problems with Liear Equatios of oe Variable 1. After readig the problem, let a variable represet the ukow (or oe of the ukows). Represet ay other ukow usig
More informationRafa l Kulik and Marc Raimondo. University of Ottawa and University of Sydney. Supplementary material
Statistica Siica 009: Supplemet 1 L p -WAVELET REGRESSION WITH CORRELATED ERRORS AND INVERSE PROBLEMS Rafa l Kulik ad Marc Raimodo Uiversity of Ottawa ad Uiversity of Sydey Supplemetary material This ote
More informationNORMALIZATION OF BEURLING GENERALIZED PRIMES WITH RIEMANN HYPOTHESIS
Aales Uiv. Sci. Budapest., Sect. Comp. 39 2013) 459 469 NORMALIZATION OF BEURLING GENERALIZED PRIMES WITH RIEMANN HYPOTHESIS We-Bi Zhag Chug Ma Pig) Guagzhou, People s Republic of Chia) Dedicated to Professor
More informationCHAPTER 8 Estimating with Confidence
CHAPTER 8 Estimatig with Cofidece 8.2 Estimatig a Populatio Proportio The Practice of Statistics, 5th Editio Stares, Tabor, Yates, Moore Bedford Freema Worth Publishers Estimatig a Populatio Proportio
More informationIntroduction to Probability and Statistics Chapter 7
Itroductio to Probability ad Statistics Chapter 7 Ammar M. Sarha, asarha@mathstat.dal.ca Departmet of Mathematics ad Statistics, Dalhousie Uiversity Fall Semester 008 Chapter 7 Statistical Itervals Based
More informationST 305: Exam 2 Fall 2014
ST 305: Exam Fall 014 By hadig i this completed exam, I state that I have either give or received assistace from aother perso durig the exam period. I have used o resources other tha the exam itself ad
More informationAnomaly Correction by Optimal Trading Frequency
Aomaly Correctio by Optimal Tradig Frequecy Yiqiao Yi Columbia Uiversity September 9, 206 Abstract Uder the assumptio that security prices follow radom walk, we look at price versus differet movig averages.
More information2.6 Rational Functions and Their Graphs
.6 Ratioal Fuctios ad Their Graphs Sectio.6 Notes Page Ratioal Fuctio: a fuctio with a variable i the deoiator. To fid the y-itercept for a ratioal fuctio, put i a zero for. To fid the -itercept for a
More information0.07. i PV Qa Q Q i n. Chapter 3, Section 2
Chapter 3, Sectio 2 1. (S13HW) Calculate the preset value for a auity that pays 500 at the ed of each year for 20 years. You are give that the aual iterest rate is 7%. 20 1 v 1 1.07 PV Qa Q 500 5297.01
More informationStandard Deviations for Normal Sampling Distributions are: For proportions For means _
Sectio 9.2 Cofidece Itervals for Proportios We will lear to use a sample to say somethig about the world at large. This process (statistical iferece) is based o our uderstadig of samplig models, ad will
More informationEXERCISE - BINOMIAL THEOREM
BINOMIAL THOEREM / EXERCISE - BINOMIAL THEOREM LEVEL I SUBJECTIVE QUESTIONS. Expad the followig expressios ad fid the umber of term i the expasio of the expressios. (a) (x + y) 99 (b) ( + a) 9 + ( a) 9
More informationChapter 3. Compound interest
Chapter 3 Compoud iterest 1 Simple iterest ad compoud amout formula Formula for compoud amout iterest is: S P ( 1 Where : S: the amout at compoud iterest P: the pricipal i: the rate per coversio period
More information4.5 Generalized likelihood ratio test
4.5 Geeralized likelihood ratio test A assumptio that is used i the Athlete Biological Passport is that haemoglobi varies equally i all athletes. We wish to test this assumptio o a sample of k athletes.
More informationr i = a i + b i f b i = Cov[r i, f] The only parameters to be estimated for this model are a i 's, b i 's, σe 2 i
The iformatio required by the mea-variace approach is substatial whe the umber of assets is large; there are mea values, variaces, ad )/2 covariaces - a total of 2 + )/2 parameters. Sigle-factor model:
More informationpoint estimator a random variable (like P or X) whose values are used to estimate a population parameter
Estimatio We have oted that the pollig problem which attempts to estimate the proportio p of Successes i some populatio ad the measuremet problem which attempts to estimate the mea value µ of some quatity
More informationSTRAND: FINANCE. Unit 3 Loans and Mortgages TEXT. Contents. Section. 3.1 Annual Percentage Rate (APR) 3.2 APR for Repayment of Loans
CMM Subject Support Strad: FINANCE Uit 3 Loas ad Mortgages: Text m e p STRAND: FINANCE Uit 3 Loas ad Mortgages TEXT Cotets Sectio 3.1 Aual Percetage Rate (APR) 3.2 APR for Repaymet of Loas 3.3 Credit Purchases
More informationUnbiased estimators Estimators
19 Ubiased estimators I Chapter 17 we saw that a dataset ca be modeled as a realizatio of a radom sample from a probability distributio ad that quatities of iterest correspod to features of the model distributio.
More informationWhere a business has two competing investment opportunities the one with the higher NPV should be selected.
Where a busiess has two competig ivestmet opportuities the oe with the higher should be selected. Logically the value of a busiess should be the sum of all of the projects which it has i operatio at the
More informationBinomial Model. Stock Price Dynamics. The Key Idea Riskless Hedge
Biomial Model Stock Price Dyamics The value of a optio at maturity depeds o the price of the uderlyig stock at maturity. The value of the optio today depeds o the expected value of the optio at maturity
More informationMath 124: Lecture for Week 10 of 17
What we will do toight 1 Lecture for of 17 David Meredith Departmet of Mathematics Sa Fracisco State Uiversity 2 3 4 April 8, 2008 5 6 II Take the midterm. At the ed aswer the followig questio: To be revealed
More informationClass Sessions 2, 3, and 4: The Time Value of Money
Class Sessios 2, 3, ad 4: The Time Value of Moey Associated Readig: Text Chapter 3 ad your calculator s maual. Summary Moey is a promise by a Bak to pay to the Bearer o demad a sum of well, moey! Oe risk
More information1. Suppose X is a variable that follows the normal distribution with known standard deviation σ = 0.3 but unknown mean µ.
Chapter 9 Exercises Suppose X is a variable that follows the ormal distributio with kow stadard deviatio σ = 03 but ukow mea µ (a) Costruct a 95% cofidece iterval for µ if a radom sample of = 6 observatios
More informationTopic-7. Large Sample Estimation
Topic-7 Large Sample Estimatio TYPES OF INFERENCE Ò Estimatio: É Estimatig or predictig the value of the parameter É What is (are) the most likely values of m or p? Ò Hypothesis Testig: É Decidig about
More informationBasic formula for confidence intervals. Formulas for estimating population variance Normal Uniform Proportion
Basic formula for the Chi-square test (Observed - Expected ) Expected Basic formula for cofidece itervals sˆ x ± Z ' Sample size adjustmet for fiite populatio (N * ) (N + - 1) Formulas for estimatig populatio
More informationSection 3.3 Exercises Part A Simplify the following. 1. (3m 2 ) 5 2. x 7 x 11
123 Sectio 3.3 Exercises Part A Simplify the followig. 1. (3m 2 ) 5 2. x 7 x 11 3. f 12 4. t 8 t 5 f 5 5. 3-4 6. 3x 7 4x 7. 3z 5 12z 3 8. 17 0 9. (g 8 ) -2 10. 14d 3 21d 7 11. (2m 2 5 g 8 ) 7 12. 5x 2
More informationStatistics for Economics & Business
Statistics for Ecoomics & Busiess Cofidece Iterval Estimatio Learig Objectives I this chapter, you lear: To costruct ad iterpret cofidece iterval estimates for the mea ad the proportio How to determie
More informationbetween 1 and 100. The teacher expected this task to take Guass several minutes to an hour to keep him busy but
Sec 5.8 Sec 6.2 Mathematical Modelig (Arithmetic & Geometric Series) Name: Carl Friedrich Gauss is probably oe of the most oted complete mathematicias i history. As the story goes, he was potetially recogiized
More informationChapter 4 - Consumer. Household Demand and Supply. Solving the max-utility problem. Working out consumer responses. The response function
Almost essetial Cosumer: Optimisatio Chapter 4 - Cosumer Osa 2: Household ad supply Cosumer: Welfare Useful, but optioal Firm: Optimisatio Household Demad ad Supply MICROECONOMICS Priciples ad Aalysis
More informationFixed Income Securities
Prof. Stefao Mazzotta Keesaw State Uiversity Fixed Icome Securities Sample First Midterm Exam Last Name: First Name: Studet ID Number: Exam time is: 80 miutes. Total poits for this exam is: 400 poits Prelimiaries
More informationConfidence Intervals. CI for a population mean (σ is known and n > 30 or the variable is normally distributed in the.
Cofidece Itervals A cofidece iterval is a iterval whose purpose is to estimate a parameter (a umber that could, i theory, be calculated from the populatio, if measuremets were available for the whole populatio).
More informationRandom Sequences Using the Divisor Pairs Function
Radom Sequeces Usig the Divisor Pairs Fuctio Subhash Kak Abstract. This paper ivestigates the radomess properties of a fuctio of the divisor pairs of a atural umber. This fuctio, the atecedets of which
More informationSampling Distributions and Estimation
Cotets 40 Samplig Distributios ad Estimatio 40.1 Samplig Distributios 40. Iterval Estimatio for the Variace 13 Learig outcomes You will lear about the distributios which are created whe a populatio is
More informationLecture 4: Probability (continued)
Lecture 4: Probability (cotiued) Desity Curves We ve defied probabilities for discrete variables (such as coi tossig). Probabilities for cotiuous or measuremet variables also are evaluated usig relative
More information0.1 Valuation Formula:
0. Valuatio Formula: 0.. Case of Geeral Trees: q = er S S S 3 S q = er S S 4 S 5 S 4 q 3 = er S 3 S 6 S 7 S 6 Therefore, f (3) = e r [q 3 f (7) + ( q 3 ) f (6)] f () = e r [q f (5) + ( q ) f (4)] = f ()
More informationChapter 4: Time Value of Money
FIN 301 Class Notes Chapter 4: Time Value of Moey The cocept of Time Value of Moey: A amout of moey received today is worth more tha the same dollar value received a year from ow. Why? Do you prefer a
More informationLecture 5: Sampling Distribution
Lecture 5: Samplig Distributio Readigs: Sectios 5.5, 5.6 Itroductio Parameter: describes populatio Statistic: describes the sample; samplig variability Samplig distributio of a statistic: A probability
More informationCHAPTER 2 PRICING OF BONDS
CHAPTER 2 PRICING OF BONDS CHAPTER SUARY This chapter will focus o the time value of moey ad how to calculate the price of a bod. Whe pricig a bod it is ecessary to estimate the expected cash flows ad
More informationOutline. Populations. Defs: A (finite) population is a (finite) set P of elements e. A variable is a function v : P IR. Population and Characteristics
Outlie Populatio Characteristics Types of Samples Sample Characterstics Sample Aalogue Estimatio Populatios Defs: A (fiite) populatio is a (fiite) set P of elemets e. A variable is a fuctio v : P IR. Examples
More informationParametric Density Estimation: Maximum Likelihood Estimation
Parametric Desity stimatio: Maimum Likelihood stimatio C6 Today Itroductio to desity estimatio Maimum Likelihood stimatio Itroducto Bayesia Decisio Theory i previous lectures tells us how to desig a optimal
More informationFINM6900 Finance Theory How Is Asymmetric Information Reflected in Asset Prices?
FINM6900 Fiace Theory How Is Asymmetric Iformatio Reflected i Asset Prices? February 3, 2012 Referece S. Grossma, O the Efficiecy of Competitive Stock Markets where Traders Have Diverse iformatio, Joural
More informationTwitter: @Owe134866 www.mathsfreeresourcelibrary.com Prior Kowledge Check 1) State whether each variable is qualitative or quatitative: a) Car colour Qualitative b) Miles travelled by a cyclist c) Favourite
More informationad covexity Defie Macaulay duratio D Mod = r 1 = ( CF i i k (1 + r k) i ) (1.) (1 + r k) C = ( r ) = 1 ( CF i i(i + 1) (1 + r k) i+ k ) ( ( i k ) CF i
Fixed Icome Basics Cotets Duratio ad Covexity Bod Duratios ar Rate, Spot Rate, ad Forward Rate Flat Forward Iterpolatio Forward rice/yield, Carry, Roll-Dow Example Duratio ad Covexity For a series of cash
More informationFixed Income Securities
Prof. Stefao Mazzotta Keesaw State Uiversity Fixed Icome Securities FIN4320. Fall 2006 Sample First Midterm Exam Last Name: First Name: Studet ID Number: Exam time is: 80 miutes. Total poits for this exam
More informationProblem Set 1a - Oligopoly
Advaced Idustrial Ecoomics Sprig 2014 Joha Steek 6 may 2014 Problem Set 1a - Oligopoly 1 Table of Cotets 2 Price Competitio... 3 2.1 Courot Oligopoly with Homogeous Goods ad Differet Costs... 3 2.2 Bertrad
More informationOnline appendices from Counterparty Risk and Credit Value Adjustment a continuing challenge for global financial markets by Jon Gregory
Olie appedices from Couterparty Risk ad Credit Value Adjustmet a APPENDIX 8A: Formulas for EE, PFE ad EPE for a ormal distributio Cosider a ormal distributio with mea (expected future value) ad stadard
More informationCombining imperfect data, and an introduction to data assimilation Ross Bannister, NCEO, September 2010
Combiig imperfect data, ad a itroductio to data assimilatio Ross Baister, NCEO, September 00 rbaister@readigacuk The probability desity fuctio (PDF prob that x lies betwee x ad x + dx p (x restrictio o
More information11.7 (TAYLOR SERIES) NAME: SOLUTIONS 31 July 2018
.7 (TAYLOR SERIES NAME: SOLUTIONS 3 July 08 TAYLOR SERIES ( The power series T(x f ( (c (x c is called the Taylor Series for f(x cetered at x c. If c 0, this is called a Maclauri series. ( The N-th partial
More informationFirst determine the payments under the payment system
Corporate Fiace February 5, 2008 Problem Set # -- ANSWERS Klick. You wi a judgmet agaist a defedat worth $20,000,000. Uder state law, the defedat has the right to pay such a judgmet out over a 20 year
More informationLecture 4: Parameter Estimation and Confidence Intervals. GENOME 560 Doug Fowler, GS
Lecture 4: Parameter Estimatio ad Cofidece Itervals GENOME 560 Doug Fowler, GS (dfowler@uw.edu) 1 Review: Probability Distributios Discrete: Biomial distributio Hypergeometric distributio Poisso distributio
More information5 Statistical Inference
5 Statistical Iferece 5.1 Trasitio from Probability Theory to Statistical Iferece 1. We have ow more or less fiished the probability sectio of the course - we ow tur attetio to statistical iferece. I statistical
More informationOn the Composition of 2-Prover Commitments and Multi-Round Relativistic Commitments. CWI Amsterdam
O the Compositio of 2-Prover Commitmets ad Multi-Roud Relativistic Commitmets Serge Fehr Max Filliger CWI Amsterdam Warig My talk: maily classical Still iterestig for you: topic (relativistic commitmets)
More informationOutline. Plotting discrete-time signals. Sampling Process. Discrete-Time Signal Representations Important D-T Signals Digital Signals
Outlie Discrete-Time Sigals-Itroductio. Plottig discrete-time sigals. Samplig Process. Discrete-Time Sigal Represetatios Importat D-T Sigals Digital Sigals Discrete-Time Sigals-Itroductio The time variable
More informationMath of Finance Math 111: College Algebra Academic Systems
Math of Fiace Math 111: College Algebra Academic Systems Writte By Bria Hoga Mathematics Istructor Highlie Commuity College Edited ad Revised by Dusty Wilso Mathematics Istructor Highlie Commuity College
More informationChapter 8. Confidence Interval Estimation. Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 8, Slide 1
Chapter 8 Cofidece Iterval Estimatio Copyright 2015, 2012, 2009 Pearso Educatio, Ic. Chapter 8, Slide 1 Learig Objectives I this chapter, you lear: To costruct ad iterpret cofidece iterval estimates for
More informationCalculation of the Annual Equivalent Rate (AER)
Appedix to Code of Coduct for the Advertisig of Iterest Bearig Accouts. (31/1/0) Calculatio of the Aual Equivalet Rate (AER) a) The most geeral case of the calculatio is the rate of iterest which, if applied
More informationDiscriminating Between The Log-normal and Gamma Distributions
Discrimiatig Betwee The Log-ormal ad Gamma Distributios Debasis Kudu & Aubhav Maglick Abstract For a give data set the problem of selectig either log-ormal or gamma distributio with ukow shape ad scale
More informationCHAPTER : ARITHMETIC PROGRESSION CONTENTS. Idetify characteristics of arithmetic progressio PAGE 2.2 Determie whether a give sequece is a arithmetic p
ADDITIONAL MATHEMATICS FORM 5 MODULE ARITHMETIC PROGRESSION CHAPTER : ARITHMETIC PROGRESSION CONTENTS. Idetify characteristics of arithmetic progressio PAGE 2.2 Determie whether a give sequece is a arithmetic
More informationChapter 13 Binomial Trees. Options, Futures, and Other Derivatives, 9th Edition, Copyright John C. Hull
Chapter 13 Biomial Trees 1 A Simple Biomial Model! A stock price is curretly $20! I 3 moths it will be either $22 or $18 Stock price $20 Stock Price $22 Stock Price $18 2 A Call Optio (Figure 13.1, page
More informationThe material in this chapter is motivated by Experiment 9.
Chapter 5 Optimal Auctios The material i this chapter is motivated by Experimet 9. We wish to aalyze the decisio of a seller who sets a reserve price whe auctioig off a item to a group of bidders. We begi
More informationSolution to Tutorial 6
Solutio to Tutorial 6 2012/2013 Semester I MA4264 Game Theory Tutor: Xiag Su October 12, 2012 1 Review Static game of icomplete iformatio The ormal-form represetatio of a -player static Bayesia game: {A
More information43. A 000 par value 5-year bod with 8.0% semiaual coupos was bought to yield 7.5% covertible semiaually. Determie the amout of premium amortized i the 6 th coupo paymet. (A).00 (B).08 (C).5 (D).5 (E).34
More informationOverlapping Generations
Eco. 53a all 996 C. Sims. troductio Overlappig Geeratios We wat to study how asset markets allow idividuals, motivated by the eed to provide icome for their retiremet years, to fiace capital accumulatio
More informationAPPLICATION OF GEOMETRIC SEQUENCES AND SERIES: COMPOUND INTEREST AND ANNUITIES
APPLICATION OF GEOMETRIC SEQUENCES AND SERIES: COMPOUND INTEREST AND ANNUITIES Example: Brado s Problem Brado, who is ow sixtee, would like to be a poker champio some day. At the age of twety-oe, he would
More informationAppendix 1 to Chapter 5
Appedix 1 to Chapter 5 Models of Asset Pricig I Chapter 4, we saw that the retur o a asset (such as a bod) measures how much we gai from holdig that asset. Whe we make a decisio to buy a asset, we are
More informationECON 5350 Class Notes Maximum Likelihood Estimation
ECON 5350 Class Notes Maximum Likelihood Estimatio 1 Maximum Likelihood Estimatio Example #1. Cosider the radom sample {X 1 = 0.5, X 2 = 2.0, X 3 = 10.0, X 4 = 1.5, X 5 = 7.0} geerated from a expoetial
More informationMS-E2114 Investment Science Exercise 2/2016, Solutions
MS-E24 Ivestmet Sciece Exercise 2/206, Solutios 26.2.205 Perpetual auity pays a xed sum periodically forever. Suppose a amout A is paid at the ed of each period, ad suppose the per-period iterest rate
More informationResearch Article The Probability That a Measurement Falls within a Range of n Standard Deviations from an Estimate of the Mean
Iteratioal Scholarly Research Network ISRN Applied Mathematics Volume 0, Article ID 70806, 8 pages doi:0.540/0/70806 Research Article The Probability That a Measuremet Falls withi a Rage of Stadard Deviatios
More informationof Asset Pricing R e = expected return
Appedix 1 to Chapter 5 Models of Asset Pricig EXPECTED RETURN I Chapter 4, we saw that the retur o a asset (such as a bod) measures how much we gai from holdig that asset. Whe we make a decisio to buy
More information5 Decision Theory: Basic Concepts
5 Decisio Theory: Basic Cocepts Poit estimatio of a ukow parameter is geerally cosidered the most basic iferece problem. Speakig geerically, if θ is some ukow parameter takig values i a suitable parameter
More information18.S096 Problem Set 5 Fall 2013 Volatility Modeling Due Date: 10/29/2013
18.S096 Problem Set 5 Fall 2013 Volatility Modelig Due Date: 10/29/2013 1. Sample Estimators of Diffusio Process Volatility ad Drift Let {X t } be the price of a fiacial security that follows a geometric
More informationSELECTING THE NUMBER OF CHANGE-POINTS IN SEGMENTED LINE REGRESSION
1 SELECTING THE NUMBER OF CHANGE-POINTS IN SEGMENTED LINE REGRESSION Hyue-Ju Kim 1,, Bibig Yu 2, ad Eric J. Feuer 3 1 Syracuse Uiversity, 2 Natioal Istitute of Agig, ad 3 Natioal Cacer Istitute Supplemetary
More informationWhen you click on Unit V in your course, you will see a TO DO LIST to assist you in starting your course.
UNIT V STUDY GUIDE Percet Notatio Course Learig Outcomes for Uit V Upo completio of this uit, studets should be able to: 1. Write three kids of otatio for a percet. 2. Covert betwee percet otatio ad decimal
More informationFinancial Analysis. Lecture 4 (4/12/2017)
Fiacial Aalysis Lecture 4 (4/12/217) Fiacial Aalysis Evaluates maagemet alteratives based o fiacial profitability; Evaluates the opportuity costs of alteratives; Cash flows of costs ad reveues; The timig
More information