LECTURE 10. Integrating Factors. M y = N. ψ x ψ y. = N(x,y).

Size: px
Start display at page:

Download "LECTURE 10. Integrating Factors. M y = N. ψ x ψ y. = N(x,y)."

Transcription

1 LECTURE 10 Integrating Facts Recall that a differential equation of the fm (101) M(x,y)+N(x,y)y =0 issaidtobeexactif (102) = andthatinsuchacase,wecouldalwaysfindanimplicitsolutionofthefm (103) ψ(x,y)=c with (104) = M(x,y) = N(x,y) Evenif(101)isnotexact,itissometimespossiblemultiplyitbyanotherfunctionofxand/ytoobtain anequivalentequationwhichisexact Thatis,onecansometimesfindafunctionµ(x,y)suchthat (105) µ(x,y)m(x,y)+µ(x,y)n(x,y)y =0 isexact Suchafunctionµ(x,y)iscalledanntegratingfact Ifanintegratingfactcanbefound,then the iginal differential equation (101) can be solved by simply constructing a solution to the equivalent exact differential equation(105) E 101 Considerthedifferentialequation This equation is not exact; f (106) andso x 2 y 3 +x ( 1+y 2) dy =0 = = ( x 2 y 3) = 3x ( ( )) 2 x 1+y 2 = 1+y 2 However, if we multiply both sides of the differential equation by µ(x,y)= 1 xy 3 weget x+ 1+y2 dy y 3 =0 which is not only exact, it is also separable The general solution is thus obtained by calculating, H 1 (x) = x= 1 2 x2 H 2 (y) = 1+y 2 y 3 dy= 1 2y 2 +ln y 35

2 10 INTEGRATING FACTORS 36 andthendemandingthatyisrelatedtoxby H 1 (x)+h 2 (y)=c 1 2 x2 1 2y 2 +ln y =C Now, in general, the problem of finding an integrating fact µ(x, y) f a given differential equation is very difficult In certain cases, it is rather easy to find an integrating fact 01 Equations with Integrating Facts that depend only on x Consider a general first der differential equation (107) M(x,y)+N(x,y) dy =0 We shall suppose that there exists an integrating fact f this equation that depends only on x: (108) µ=µ(x) Ifµisteallybeanintegratingfact,then (109) µ(x)m(x,y)+µ(x)n(x,y) dy must be exact; ie, (1010) (µ(x)m(x,y))= (µ(x)n(x,y)) Carryingoutthedifferentiations(usingtheproductrule,andthefactthatµ(x)dependsonlyonx),weget (1011) µ = N+µ = 1 ( ) N µ Now if µ is depends only on x (and not on y), then necessarily depends only on x Thus, the selfconsistency of equations (108) and (1011) requires the right hand side of (1011) to be a function of x alone Wepresumethistobethecaseandset p(x)= 1 ( ) N sothatwecanrewrite(1011)as (1012) +p(x)µ=0 Thisisafirstderlineardifferentialequationfµhatwecansolve! Accdingtothefmuladeveloped in Section 21, the general solution of(1012) is [ ] (1013) µ(x) = A exp p(x) [ ( ) ] 1 =Aexp N Thefmula(1013)thusgivesusanintegratingfactf(107)solongas ( ) 1 N dependsonlyonx

3 10 INTEGRATING FACTORS Equations with Integrating Facts that depend only on y Consider again the general first der differential equation (1014) M(x,y)+N(x,y) dy =0 We shall suppose that there exists an integrating fact f this equation that depends only on y: (1015) µ=µ(y) Ifµisteallybeanintegratingfact,then (1016) µ(y)m(x,y)+µ(y)n(x,y) dy must be exact; ie, (1017) (µ(y)m(x,y))= (µ(y)n(x,y)) Carryingoutthedifferentiations(usingtheproductrule,andthefactthatµ(y)dependsonlyony),weget (1018) dy M+µ =µ dy = 1 ( ) M µ Nowsinceµisdependsonlyony(andnotonx), thennecessarily dy dependsonlyony Thus,theselfconsistency of equations (1015) and (1018) requires the right hand side of (1011) to be a function of y alone Wepresumethistobethecaseandset sothatwecanrewrite(1011)as (1019) p(y)= 1 M ( ) dy +p(y)µ=0 Accding to the fmula developed in Section 21, the general solution of(1019) is [ ] [ ( ) ] 1 (1020) µ(y) = A exp p(y) =Aexp M Thefmula(1020)thusgivesusanintegratingfactf(1014)solongas ( ) 1 M dependsonlyony 03 Summary: Finding Integrating Facts Suppose that (1021) M(x,y)+N(x,y)y =0 is not exact AIf (1022) F 1 = dependsonlyonxthen ( (1023) µ(x) = exp will be an integrating fact f(1021) N ) F 1 (x)

4 10 INTEGRATING FACTORS 38 BIf (1024) F 2 = dependsonlyonythen ( (1025) µ(y) = exp will be an integrating fact f(1021) M ) F 2 (y)dy CIfneitherAnBistrue,thenthereislittlehopeofconstructinganintegratingfact E 102 (1026) (3x 2 y+2xy+y 3 )+(x 2 +y 2 )dy=0 Here Since this equation is not exact M(x,y) = 3x 2 y+2xy+y 3 N(x,y) = x 2 +y 2 =3x2 +2x+3y 2 2x= Weseektofindafunctionµsuchthat is exact Now F 1 F 2 µ(x,y)(3x 2 y+2xy+y 3 )+µ(x,y)(x 2 +y 2 )dy=0 = 3x2 +2x+3y 2 2x N x 2 +y 2 = 3(x2 +y 2 ) x 2 +y 2 =3 = 2x 3x2 2x 3y 2 M 3x 2 y+2xy+y 3 = 3 ( x 2 +y 2) 3x 2 y+2xy+y 3 Since F 2 depends on both x and y, we cannot construct an integrating fact depending only on y from F 2 However,sinceF 1 doesnotdependony,wecanconsistentlyconstructanintegratingfactthatisa function of x alone Applying fmula(1023) we get ( ) µ(x)=exp F 1 (x) [ =exp ] 3 =e 3x Wecannowemploythisµ(x)asanintegratingfacttoconstructageneralsolutionof e 3x (3x 2 +2x+3y 2 )+e 3x (x 2 +y 2 )y =0 which,byconstruction,mustbeexact Soweseekafunctionψsuchthat (1027) = e 3x (3x 2 y+2xy+y 3 ) = e 3x (x 2 +y 2 ) Integratingthefirstequationwithrespecttoxandthesecondequationwithrespecttoyyeilds ψ(x,y) = x 2 ye 3x y3 e 3x +h 1 (y) ψ(x,y) = x 2 ye 3x y3 e 3x +h 2 (x) Comparingtheseexpressionsfψ(x,y)weseethatwemsuttakeh 1 (y)=h 2 (x)=c, aconstant Thus, function ψ satisfying(1027) must be of the fm ψ(x,y)=e 3x x 2 y+e 3x y 3 +C Therefe, the general solution of(1020) is found by solving e 3x x 2 y+e 3x y 3 =C

5 fy 10 INTEGRATING FACTORS 39

MATH 307: Problem Set #3 Solutions

MATH 307: Problem Set #3 Solutions : Problem Set #3 Solutions Due on: May 3, 2015 Problem 1 Autonomous Equations Recall that an equilibrium solution of an autonomous equation is called stable if solutions lying on both sides of it tend

More information

MAP 2302 Midterm 1 Review Solutions 1

MAP 2302 Midterm 1 Review Solutions 1 MAP 2302 Midterm 1 Review Solutions 1 The exam will cover sections 1.2, 1.3, 2.2, 2.3, 2.4, and 2.6. All topics from this review sheet or from the suggested exercises are fair game. 1 Give explicit solutions

More information

First Order Differential Equations

First Order Differential Equations Chapter 2 First Order Differential Equations 2.1 9 10 CHAPTER 2. FIRST ORDER DIFFERENTIAL EQUATIONS 2.2 Separable Equations A first order differential equation = f(x, y) is called separable if f(x, y)

More information

Essential Ordinary Differential Equations

Essential Ordinary Differential Equations MODULE 1: MATHEMATICAL PRELIMINARIES 10 Lecture 2 Essential Ordinary Differential Equations In this lecture, we recall some methods of solving first-order IVP in ODE (separable and linear) and homogeneous

More information

First-Order ODE: Separable Equations, Exact Equations and Integrating Factor

First-Order ODE: Separable Equations, Exact Equations and Integrating Factor First-Order ODE: Separable Equations, Exact Equations and Integrating Factor Department of Mathematics IIT Guwahati REMARK: In the last theorem of the previous lecture, you can change the open interval

More information

Chapter 2: First-Order Differential Equations Part 1

Chapter 2: First-Order Differential Equations Part 1 Chapter 2: First-Order Differential Equations Part 1 王奕翔 Department of Electrical Engineering National Taiwan University ihwang@ntu.edu.tw September 17, 2013 1 Overview 2 3 4 5 6 1 Overview 2 3 4 5 6 First-Order

More information

Lesson 3: Linear differential equations of the first order Solve each of the following differential equations by two methods.

Lesson 3: Linear differential equations of the first order Solve each of the following differential equations by two methods. Lesson 3: Linear differential equations of the first der Solve each of the following differential equations by two methods. Exercise 3.1. Solution. Method 1. It is clear that y + y = 3 e dx = e x is an

More information

Elementary Differential Equations

Elementary Differential Equations Elementary Differential Equations George Voutsadakis 1 1 Mathematics and Computer Science Lake Superior State University LSSU Math 310 George Voutsadakis (LSSU) Differential Equations January 2014 1 /

More information

Elementary ODE Review

Elementary ODE Review Elementary ODE Review First Order ODEs First Order Equations Ordinary differential equations of the fm y F(x, y) () are called first der dinary differential equations. There are a variety of techniques

More information

2.2 Separable Equations

2.2 Separable Equations 2.2 Separable Equations Definition A first-order differential equation that can be written in the form Is said to be separable. Note: the variables of a separable equation can be written as Examples Solve

More information

Lectures on Differential Equations

Lectures on Differential Equations Lectures on Differential Equations Philip Korman Department of Mathematical Sciences University of Cincinnati Cincinnati Ohio 4522-0025 Copyright @ 2008, by Philip L. Korman Chapter First Order Equations.

More information

MATH 312 Section 2.4: Exact Differential Equations

MATH 312 Section 2.4: Exact Differential Equations MATH 312 Section 2.4: Exact Differential Equations Prof. Jonathan Duncan Walla Walla College Spring Quarter, 2007 Outline 1 Exact Differential Equations 2 Solving an Exact DE 3 Making a DE Exact 4 Conclusion

More information

Lectures on Differential Equations

Lectures on Differential Equations Lectures on Differential Equations Philip Korman Department of Mathematical Sciences University of Cincinnati Cincinnati Ohio 4522-0025 Copyright @ 2008, by Philip L. Korman Contents First Order Equations

More information

Ordinary Differential Equations

Ordinary Differential Equations Ordinary Differential Equations (MA102 Mathematics II) Shyamashree Upadhyay IIT Guwahati Shyamashree Upadhyay ( IIT Guwahati ) Ordinary Differential Equations 1 / 25 First order ODE s We will now discuss

More information

First Order Differential Equations Lecture 3

First Order Differential Equations Lecture 3 First Order Differential Equations Lecture 3 Dibyajyoti Deb 3.1. Outline of Lecture Differences Between Linear and Nonlinear Equations Exact Equations and Integrating Factors 3.. Differences between Linear

More information

1 Solution to Problem 2.1

1 Solution to Problem 2.1 Solution to Problem 2. I incorrectly worked this exercise instead of 2.2, so I decided to include the solution anyway. a) We have X Y /3, which is a - function. It maps the interval, ) where X lives) onto

More information

Math Reading assignment for Chapter 1: Study Sections 1.1 and 1.2.

Math Reading assignment for Chapter 1: Study Sections 1.1 and 1.2. Math 3350 1 Chapter 1 Reading assignment for Chapter 1: Study Sections 1.1 and 1.2. 1.1 Material for Section 1.1 An Ordinary Differential Equation (ODE) is a relation between an independent variable x

More information

Bivariate distributions

Bivariate distributions Bivariate distributions 3 th October 017 lecture based on Hogg Tanis Zimmerman: Probability and Statistical Inference (9th ed.) Bivariate Distributions of the Discrete Type The Correlation Coefficient

More information

Ma 221 Homework Solutions Due Date: January 24, 2012

Ma 221 Homework Solutions Due Date: January 24, 2012 Ma Homewk Solutions Due Date: January, 0. pg. 3 #, 3, 6,, 5, 7 9,, 3;.3 p.5-55 #, 3, 5, 7, 0, 7, 9, (Underlined problems are handed in) In problems, and 5, determine whether the given differential equation

More information

Math 2a Prac Lectures on Differential Equations

Math 2a Prac Lectures on Differential Equations Math 2a Prac Lectures on Differential Equations Prof. Dinakar Ramakrishnan 272 Sloan, 253-37 Caltech Office Hours: Fridays 4 5 PM Based on notes taken in class by Stephanie Laga, with a few added comments

More information

More Techniques. for Solving First Order ODE'S. and. a Classification Scheme for Techniques

More Techniques. for Solving First Order ODE'S. and. a Classification Scheme for Techniques A SERIES OF CLASS NOTES FOR 2005-2006 TO INTRODUCE LINEAR AND NONLINEAR PROBLEMS TO ENGINEERS, SCIENTISTS, AND APPLIED MATHEMATICIANS DE CLASS NOTES 1 A COLLECTION OF HANDOUTS ON FIRST ORDER ORDINARY DIFFERENTIAL

More information

Sampling Distributions

Sampling Distributions Sampling Distributions Mathematics 47: Lecture 9 Dan Sloughter Furman University March 16, 2006 Dan Sloughter (Furman University) Sampling Distributions March 16, 2006 1 / 10 Definition We call the probability

More information

HW2 Solutions. MATH 20D Fall 2013 Prof: Sun Hui TA: Zezhou Zhang (David) October 14, Checklist: Section 2.6: 1, 3, 6, 8, 10, 15, [20, 22]

HW2 Solutions. MATH 20D Fall 2013 Prof: Sun Hui TA: Zezhou Zhang (David) October 14, Checklist: Section 2.6: 1, 3, 6, 8, 10, 15, [20, 22] HW2 Solutions MATH 20D Fall 2013 Prof: Sun Hui TA: Zezhou Zhang (David) October 14, 2013 Checklist: Section 2.6: 1, 3, 6, 8, 10, 15, [20, 22] Section 3.1: 1, 2, 3, 9, 16, 18, 20, 23 Section 3.2: 1, 2,

More information

1.5. Inequalities. Equations and Inequalities. Box (cont.) Sections

1.5. Inequalities. Equations and Inequalities. Box (cont.) Sections 1 Equations and Inequalities Sections 1.5 1.8 2008 Pearson Addison-Wesley. All rights reserved 1 Equations and Inequalities 1.5 Applications and Modeling with Quadratic Equations 1.6 Other Types of Equations

More information

Explicit Solutions of the Heat Equation

Explicit Solutions of the Heat Equation LECTURE 6 Eplicit Solutions of the Heat Equation Recall the -dimensional homogeneous) Heat Equation: ) u t a 2 u. In this lecture our goal is to construct an eplicit solution to the Heat Equation ) on

More information

Solving First Order ODEs. Table of contents

Solving First Order ODEs. Table of contents Solving First Order ODEs Table of contents Solving First Order ODEs............................................... 1 1. Introduction...................................................... 1 Aside: Two ways

More information

Chapter 2: First Order DE 2.6 Exact DE and Integrating Fa

Chapter 2: First Order DE 2.6 Exact DE and Integrating Fa Chapter 2: First Order DE 2.6 Exact DE and Integrating Factor First Order DE Recall the general form of the First Order DEs (FODE): dy dx = f(x, y) (1) (In this section x is the independent variable; not

More information

To calculate the x=moment, we multiply the integrand above by x to get = )

To calculate the x=moment, we multiply the integrand above by x to get = ) . ( points) The region shown below is the area between the curves y = 3x and y = x. Find the center of mass of this region. 8 6 3 We must calculate the area, x-moment, and y-moment to find the center of

More information

Introduction to Computational Finance and Financial Econometrics Probability Review - Part 2

Introduction to Computational Finance and Financial Econometrics Probability Review - Part 2 You can t see this text! Introduction to Computational Finance and Financial Econometrics Probability Review - Part 2 Eric Zivot Spring 2015 Eric Zivot (Copyright 2015) Probability Review - Part 2 1 /

More information

Chapter 3 sections. SKIP: 3.10 Markov Chains. SKIP: pages Chapter 3 - continued

Chapter 3 sections. SKIP: 3.10 Markov Chains. SKIP: pages Chapter 3 - continued Chapter 3 sections Chapter 3 - continued 3.1 Random Variables and Discrete Distributions 3.2 Continuous Distributions 3.3 The Cumulative Distribution Function 3.4 Bivariate Distributions 3.5 Marginal Distributions

More information

Mathematical Methods - Lecture 7

Mathematical Methods - Lecture 7 Mathematical Methods - Lecture 7 Yuliya Tarabalka Inria Sophia-Antipolis Méditerranée, Titane team, http://www-sop.inria.fr/members/yuliya.tarabalka/ Tel.: +33 (0)4 92 38 77 09 email: yuliya.tarabalka@inria.fr

More information

Joint Probability Distributions and Random Samples (Devore Chapter Five)

Joint Probability Distributions and Random Samples (Devore Chapter Five) Joint Probability Distributions and Random Samples (Devore Chapter Five) 1016-345-01: Probability and Statistics for Engineers Spring 2013 Contents 1 Joint Probability Distributions 2 1.1 Two Discrete

More information

First Order Linear Differential Equations

First Order Linear Differential Equations LECTURE 6 First Orer Linear Differential Equations A linear first orer orinary ifferential equation is a ifferential equation of the form ( a(xy + b(xy = c(x. Here y represents the unknown function, y

More information

Linear First-Order Equations

Linear First-Order Equations 5 Linear First-Orer Equations Linear first-orer ifferential equations make up another important class of ifferential equations that commonly arise in applications an are relatively easy to solve (in theory)

More information

Lecture 19: Solving linear ODEs + separable techniques for nonlinear ODE s

Lecture 19: Solving linear ODEs + separable techniques for nonlinear ODE s Lecture 19: Solving linear ODEs + separable techniques for nonlinear ODE s Geoffrey Cowles Department of Fisheries Oceanography School for Marine Science and Technology University of Massachusetts-Dartmouth

More information

Review: mostly probability and some statistics

Review: mostly probability and some statistics Review: mostly probability and some statistics C2 1 Content robability (should know already) Axioms and properties Conditional probability and independence Law of Total probability and Bayes theorem Random

More information

One important way that you can classify differential equations is as linear or nonlinear.

One important way that you can classify differential equations is as linear or nonlinear. In This Chapter Chapter 1 Looking Closely at Linear First Order Differential Equations Knowing what a first order linear differential equation looks like Finding solutions to first order differential equations

More information

Lecture 4. Continuous Random Variables and Transformations of Random Variables

Lecture 4. Continuous Random Variables and Transformations of Random Variables Math 408 - Mathematical Statistics Lecture 4. Continuous Random Variables and Transformations of Random Variables January 25, 2013 Konstantin Zuev (USC) Math 408, Lecture 4 January 25, 2013 1 / 13 Agenda

More information

Chapter 4 Multiple Random Variables

Chapter 4 Multiple Random Variables Review for the previous lecture Theorems and Examples: How to obtain the pmf (pdf) of U = g ( X Y 1 ) and V = g ( X Y) Chapter 4 Multiple Random Variables Chapter 43 Bivariate Transformations Continuous

More information

Ordinary differential equations Notes for FYS3140

Ordinary differential equations Notes for FYS3140 Ordinary differential equations Notes for FYS3140 Susanne Viefers, Dept of Physics, University of Oslo April 4, 2018 Abstract Ordinary differential equations show up in many places in physics, and these

More information

Chapter 3 sections. SKIP: 3.10 Markov Chains. SKIP: pages Chapter 3 - continued

Chapter 3 sections. SKIP: 3.10 Markov Chains. SKIP: pages Chapter 3 - continued Chapter 3 sections 3.1 Random Variables and Discrete Distributions 3.2 Continuous Distributions 3.3 The Cumulative Distribution Function 3.4 Bivariate Distributions 3.5 Marginal Distributions 3.6 Conditional

More information

MATH 151, FINAL EXAM Winter Quarter, 21 March, 2014

MATH 151, FINAL EXAM Winter Quarter, 21 March, 2014 Time: 3 hours, 8:3-11:3 Instructions: MATH 151, FINAL EXAM Winter Quarter, 21 March, 214 (1) Write your name in blue-book provided and sign that you agree to abide by the honor code. (2) The exam consists

More information

A Concise Introduction to Ordinary Differential Equations. David Protas

A Concise Introduction to Ordinary Differential Equations. David Protas A Concise Introduction to Ordinary Differential Equations David Protas A Concise Introduction to Ordinary Differential Equations 1 David Protas California State University, Northridge Please send any

More information

ODE classification. February 7, Nasser M. Abbasi. compiled on Wednesday February 07, 2018 at 11:18 PM

ODE classification. February 7, Nasser M. Abbasi. compiled on Wednesday February 07, 2018 at 11:18 PM ODE classification Nasser M. Abbasi February 7, 2018 compiled on Wednesday February 07, 2018 at 11:18 PM 1 2 first order b(x)y + c(x)y = f(x) Integrating factor or separable (see detailed flow chart for

More information

Math 2233 Homework Set 8

Math 2233 Homework Set 8 Math 2233 Homewk Set 8. Determine the lower bound f the radius of convergence of series solutions about each given point xo. (a) y +4y +6xy =,x = Since the coefficient functions 4 6x are perfectly analytic

More information

Chapter 4. Chapter 4 sections

Chapter 4. Chapter 4 sections Chapter 4 sections 4.1 Expectation 4.2 Properties of Expectations 4.3 Variance 4.4 Moments 4.5 The Mean and the Median 4.6 Covariance and Correlation 4.7 Conditional Expectation SKIP: 4.8 Utility Expectation

More information

Math 112 Section 10 Lecture notes, 1/7/04

Math 112 Section 10 Lecture notes, 1/7/04 Math 11 Section 10 Lecture notes, 1/7/04 Section 7. Integration by parts To integrate the product of two functions, integration by parts is used when simpler methods such as substitution or simplifying

More information

matrix-free Elements of Probability Theory 1 Random Variables and Distributions Contents Elements of Probability Theory 2

matrix-free Elements of Probability Theory 1 Random Variables and Distributions Contents Elements of Probability Theory 2 Short Guides to Microeconometrics Fall 2018 Kurt Schmidheiny Unversität Basel Elements of Probability Theory 2 1 Random Variables and Distributions Contents Elements of Probability Theory matrix-free 1

More information

First Order Differential Equations

First Order Differential Equations First Order Differential Equations Linear Equations Philippe B. Laval KSU Philippe B. Laval (KSU) 1st Order Linear Equations 1 / 11 Introduction We are still looking at 1st order equations. In today s

More information

Practice Midterm Solutions

Practice Midterm Solutions Practice Midterm Solutions Math 4B: Ordinary Differential Equations Winter 20 University of California, Santa Barbara TA: Victoria Kala DO NOT LOOK AT THESE SOLUTIONS UNTIL YOU HAVE ATTEMPTED EVERY PROBLEM

More information

Calculus IV - HW 2 MA 214. Due 6/29

Calculus IV - HW 2 MA 214. Due 6/29 Calculus IV - HW 2 MA 214 Due 6/29 Section 2.5 1. (Problems 3 and 5 from B&D) The following problems involve differential equations of the form dy = f(y). For each, sketch the graph of f(y) versus y, determine

More information

Problem Y is an exponential random variable with parameter λ = 0.2. Given the event A = {Y < 2},

Problem Y is an exponential random variable with parameter λ = 0.2. Given the event A = {Y < 2}, ECE32 Spring 25 HW Solutions April 6, 25 Solutions to HW Note: Most of these solutions were generated by R. D. Yates and D. J. Goodman, the authors of our textbook. I have added comments in italics where

More information

MAS113 CALCULUS II SPRING 2008, QUIZ 5 SOLUTIONS. x 2 dx = 3y + y 3 = x 3 + c. It can be easily verified that the differential equation is exact, as

MAS113 CALCULUS II SPRING 2008, QUIZ 5 SOLUTIONS. x 2 dx = 3y + y 3 = x 3 + c. It can be easily verified that the differential equation is exact, as MAS113 CALCULUS II SPRING 008, QUIZ 5 SOLUTIONS Quiz 5a Solutions (1) Solve the differential equation y = x 1 + y. (1 + y )y = x = (1 + y ) = x = 3y + y 3 = x 3 + c. () Solve the differential equation

More information

Math 416 Lecture 2 DEFINITION. Here are the multivariate versions: X, Y, Z iff P(X = x, Y = y, Z =z) = p(x, y, z) of X, Y, Z iff for all sets A, B, C,

Math 416 Lecture 2 DEFINITION. Here are the multivariate versions: X, Y, Z iff P(X = x, Y = y, Z =z) = p(x, y, z) of X, Y, Z iff for all sets A, B, C, Math 416 Lecture 2 DEFINITION. Here are the multivariate versions: PMF case: p(x, y, z) is the joint Probability Mass Function of X, Y, Z iff P(X = x, Y = y, Z =z) = p(x, y, z) PDF case: f(x, y, z) is

More information

Tangent Plane. Linear Approximation. The Gradient

Tangent Plane. Linear Approximation. The Gradient Calculus 3 Lia Vas Tangent Plane. Linear Approximation. The Gradient The tangent plane. Let z = f(x, y) be a function of two variables with continuous partial derivatives. Recall that the vectors 1, 0,

More information

S. Ghorai 1. Lecture IV Linear equations, Bernoulli equations, Orthogonal trajectories, Oblique trajectories. (p(x)y r(x))dx + dy = 0.

S. Ghorai 1. Lecture IV Linear equations, Bernoulli equations, Orthogonal trajectories, Oblique trajectories. (p(x)y r(x))dx + dy = 0. S. Ghorai 1 Lecture IV Linear equations, Bernoulli equations, Orthogonal trajectories, Oblique trajectories 1 Linear equations A first order linear equations is of the form This can be written as Here

More information

ECE 4400:693 - Information Theory

ECE 4400:693 - Information Theory ECE 4400:693 - Information Theory Dr. Nghi Tran Lecture 8: Differential Entropy Dr. Nghi Tran (ECE-University of Akron) ECE 4400:693 Lecture 1 / 43 Outline 1 Review: Entropy of discrete RVs 2 Differential

More information

Bayesian decision theory Introduction to Pattern Recognition. Lectures 4 and 5: Bayesian decision theory

Bayesian decision theory Introduction to Pattern Recognition. Lectures 4 and 5: Bayesian decision theory Bayesian decision theory 8001652 Introduction to Pattern Recognition. Lectures 4 and 5: Bayesian decision theory Jussi Tohka jussi.tohka@tut.fi Institute of Signal Processing Tampere University of Technology

More information

A note on the convex infimum convolution inequality

A note on the convex infimum convolution inequality A note on the convex infimum convolution inequality Naomi Feldheim, Arnaud Marsiglietti, Piotr Nayar, Jing Wang Abstract We characterize the symmetric measures which satisfy the one dimensional convex

More information

Algebraic Fractions. Mark Scheme 1. Equations, Formulae and Identities Algebraic Fractions(Algebraic manipulation) Booklet Mark Scheme 1

Algebraic Fractions. Mark Scheme 1. Equations, Formulae and Identities Algebraic Fractions(Algebraic manipulation) Booklet Mark Scheme 1 Algebraic Fractions Mark Scheme 1 Level IGCSE Subject Maths Exam Board Edexcel Topic Equations, Fmulae and Identities Sub Topic Algebraic Fractions(Algebraic manipulation) Booklet Mark Scheme 1 Time Allowed:

More information

SOLUTIONS FOR THE PRACTICE EXAM FOR THE SECOND MIDTERM EXAM

SOLUTIONS FOR THE PRACTICE EXAM FOR THE SECOND MIDTERM EXAM SOLUTIONS FOR THE PRACTICE EXAM FOR THE SECOND MIDTERM EXAM ANDREW J. BLUMBERG 1. Notes This practice exam has me problems than the real exam will. To make most effective use of this document, take the

More information

Five regular or nearly-regular ternary quadratic forms

Five regular or nearly-regular ternary quadratic forms ACTA ARITHMETICA LXXVII.4 (1996) Five regular nearly-regular ternary quadratic fms by William C. Jagy (Berkeley, Calif.) 1. Introduction. In a recent article [6], the positive definite ternary quadratic

More information

Stat 366 A1 (Fall 2006) Midterm Solutions (October 23) page 1

Stat 366 A1 (Fall 2006) Midterm Solutions (October 23) page 1 Stat 366 A1 Fall 6) Midterm Solutions October 3) page 1 1. The opening prices per share Y 1 and Y measured in dollars) of two similar stocks are independent random variables, each with a density function

More information

Will Landau. Feb 21, 2013

Will Landau. Feb 21, 2013 Iowa State University Feb 21, 2013 Iowa State University Feb 21, 2013 1 / 31 Outline Iowa State University Feb 21, 2013 2 / 31 random variables Two types of random variables: Discrete random variable:

More information

Math Applied Differential Equations

Math Applied Differential Equations Math 256 - Applied Differential Equations Notes Basic Definitions and Concepts A differential equation is an equation that involves one or more of the derivatives (first derivative, second derivative,

More information

Colligative Properties of Solutions

Colligative Properties of Solutions CHAPTER 16. Colligative Properties of Solutions 16-1. The number of moles of KMnO 4 (aq) is ( ) 1 mol KMnO4 moles KMnO 4 (5.25 g KMnO 4 ) 0.0332 mol 158.04 g KMnO 4 molality m 0.0332 mol 0.250 kg 0.133

More information

Introduction to Computational Finance and Financial Econometrics Matrix Algebra Review

Introduction to Computational Finance and Financial Econometrics Matrix Algebra Review You can t see this text! Introduction to Computational Finance and Financial Econometrics Matrix Algebra Review Eric Zivot Spring 2015 Eric Zivot (Copyright 2015) Matrix Algebra Review 1 / 54 Outline 1

More information

B. Differential Equations A differential equation is an equation of the form

B. Differential Equations A differential equation is an equation of the form B Differential Equations A differential equation is an equation of the form ( n) F[ t; x(, xʹ (, x ʹ ʹ (, x ( ; α] = 0 dx d x ( n) d x where x ʹ ( =, x ʹ ʹ ( =,, x ( = n A differential equation describes

More information

Stat 100a, Introduction to Probability.

Stat 100a, Introduction to Probability. Stat 100a, Introduction to Probability. Outline for the day: 1. Geometric random variables. 2. Negative binomial random variables. 3. Moment generating functions. 4. Poisson random variables. 5. Continuous

More information

Homework 10 (due December 2, 2009)

Homework 10 (due December 2, 2009) Homework (due December, 9) Problem. Let X and Y be independent binomial random variables with parameters (n, p) and (n, p) respectively. Prove that X + Y is a binomial random variable with parameters (n

More information

MTH 3311 Test #1. order 3, linear. The highest order of derivative of y is 2. Furthermore, y and its derivatives are all raised to the

MTH 3311 Test #1. order 3, linear. The highest order of derivative of y is 2. Furthermore, y and its derivatives are all raised to the MTH 3311 Test #1 F 018 Pat Rossi Name Show CLEARLY how you arrive at your answers. 1. Classify the following according to order and linearity. If an equation is not linear, eplain why. (a) y + y y = 4

More information

ABSTRACT ALGEBRA 1, LECTURE NOTES 4: DEFINITIONS AND EXAMPLES OF MONOIDS AND GROUPS.

ABSTRACT ALGEBRA 1, LECTURE NOTES 4: DEFINITIONS AND EXAMPLES OF MONOIDS AND GROUPS. ABSTRACT ALGEBRA 1, LECTURE NOTES 4: DEFINITIONS AND EXAMPLES OF MONOIDS AND GROUPS. ANDREW SALCH 1. Monoids. Definition 1.1. A monoid is a set M together with a function µ : M M M satisfying the following

More information

STAT 430/510: Lecture 15

STAT 430/510: Lecture 15 STAT 430/510: Lecture 15 James Piette June 23, 2010 Updates HW4 is up on my website. It is due next Mon. (June 28th). Starting today back at section 6.4... Conditional Distribution: Discrete Def: The conditional

More information

Backprojection. Projections. Projections " $ & cosθ & Bioengineering 280A Principles of Biomedical Imaging. Fall Quarter 2014 CT/Fourier Lecture 2

Backprojection. Projections. Projections  $ & cosθ & Bioengineering 280A Principles of Biomedical Imaging. Fall Quarter 2014 CT/Fourier Lecture 2 Backprojection Bioengineering 280A Principles of Biomedical Imaging 3 0 0 0 0 3 0 0 0 0 0 3 0 0 3 0 3 Fall Quarter 2014 CT/Fourier Lecture 2 0 0 0 1 1 1 0 0 0 1 0 0 1 2 1 0 0 1 1 1 0 1 3 1 0 1 1 1 1 1

More information

Quantitative Methods in Economics Conditional Expectations

Quantitative Methods in Economics Conditional Expectations Quantitative Methods in Economics Conditional Expectations Maximilian Kasy Harvard University, fall 2016 1 / 19 Roadmap, Part I 1. Linear predictors and least squares regression 2. Conditional expectations

More information

Problem Set. Assignment #1. Math 3350, Spring Feb. 6, 2004 ANSWERS

Problem Set. Assignment #1. Math 3350, Spring Feb. 6, 2004 ANSWERS Problem Set Assignment #1 Math 3350, Spring 2004 Feb. 6, 2004 ANSWERS i Problem 1. [Section 1.4, Problem 4] A rocket is shot straight up. During the initial stages of flight is has acceleration 7t m /s

More information

B Ordinary Differential Equations Review

B Ordinary Differential Equations Review B Ordinary Differential Equations Review The profound study of nature is the most fertile source of mathematical discoveries. - Joseph Fourier (1768-1830) B.1 First Order Differential Equations Before

More information

The Geometric Random Walk: More Applications to Gambling

The Geometric Random Walk: More Applications to Gambling MATH 540 The Geometric Random Walk: More Applications to Gambling Dr. Neal, Spring 2008 We now shall study properties of a random walk process with only upward or downward steps that is stopped after the

More information

Math Exam III - Spring

Math Exam III - Spring Math 3 - Exam III - Spring 8 This exam contains 5 multiple choice questions and hand graded questions. The multiple choice questions are worth 5 points each and the hand graded questions are worth a total

More information

DIFFERENTIAL EQUATIONS

DIFFERENTIAL EQUATIONS DIFFERENTIAL EQUATIONS Basic Terminology A differential equation is an equation that contains an unknown function together with one or more of its derivatives. 1 Examples: 1. y = 2x + cos x 2. dy dt =

More information

Section 2.2 Homogeneous DEs (related to separable DEs) Key Terms: Homogeneous Functions. Degree of a term. First Order DEs of Homogeneous type

Section 2.2 Homogeneous DEs (related to separable DEs) Key Terms: Homogeneous Functions. Degree of a term. First Order DEs of Homogeneous type Section. Homogeneous DEs (related to separable DEs) Key Terms: Homogeneous Functions Degree of a term First Order DEs of Homogeneous type From experiences in your study of mathematics you have encountered

More information

Extra Problems and Examples

Extra Problems and Examples Extra Problems and Examples Steven Bellenot October 11, 2007 1 Separation of Variables Find the solution u(x, y) to the following equations by separating variables. 1. u x + u y = 0 2. u x u y = 0 answer:

More information

Math 308, Sections 301, 302, Summer 2008 Review before Test I 06/09/2008

Math 308, Sections 301, 302, Summer 2008 Review before Test I 06/09/2008 Math 308, Sections 301, 302, Summer 2008 Review before Test I 06/09/2008 Chapter 1. Introduction Section 1.1 Background Definition Equation that contains some derivatives of an unknown function is called

More information

S&S S&S S&S. Signals and Systems (18-396) Spring Semester, Department of Electrical and Computer Engineering

S&S S&S S&S. Signals and Systems (18-396) Spring Semester, Department of Electrical and Computer Engineering S&S S&S S&S Signals Systems (-96) Spring Semester, 2009 Department of Electrical Computer Engineering SOLUTION OF DIFFERENTIAL AND DIFFERENCE EQUATIONS Note: These notes summarize the comments from the

More information

CS434a/541a: Pattern Recognition Prof. Olga Veksler. Lecture 1

CS434a/541a: Pattern Recognition Prof. Olga Veksler. Lecture 1 CS434a/541a: Pattern Recognition Prof. Olga Veksler Lecture 1 1 Outline of the lecture Syllabus Introduction to Pattern Recognition Review of Probability/Statistics 2 Syllabus Prerequisite Analysis of

More information

EXAM. Exam #1. Math 3350 Summer II, July 21, 2000 ANSWERS

EXAM. Exam #1. Math 3350 Summer II, July 21, 2000 ANSWERS EXAM Exam #1 Math 3350 Summer II, 2000 July 21, 2000 ANSWERS i 100 pts. Problem 1. 1. In each part, find the general solution of the differential equation. dx = x2 e y We use the following sequence of

More information

Chapter 4. Section Derivatives of Exponential and Logarithmic Functions

Chapter 4. Section Derivatives of Exponential and Logarithmic Functions Chapter 4 Section 4.2 - Derivatives of Exponential and Logarithmic Functions Objectives: The student will be able to calculate the derivative of e x and of lnx. The student will be able to compute the

More information

Nonparameteric Regression:

Nonparameteric Regression: Nonparameteric Regression: Nadaraya-Watson Kernel Regression & Gaussian Process Regression Seungjin Choi Department of Computer Science and Engineering Pohang University of Science and Technology 77 Cheongam-ro,

More information

DIFFERENTIAL EQUATIONS

DIFFERENTIAL EQUATIONS DIFFERENTIAL EQUATIONS DIFFERENTIAL EQUATIONS NON-LINEAR LINEAR (in y) LINEAR W/ CST COEFFs (in y) FIRST- ORDER 4(y ) 2 +x cos y = x 2 4x 2 y + y cos x = x 2 4y + 3y = cos x ORDINARY DIFF EQs SECOND- ORDER

More information

2009 GCE A Level H1 Mathematics Solution

2009 GCE A Level H1 Mathematics Solution 2009 GCE A Level H1 Mathematics Solution 1) x + 2y = 3 x = 3 2y Substitute x = 3 2y into x 2 + xy = 2: (3 2y) 2 + (3 2y)y = 2 9 12y + 4y 2 + 3y 2y 2 = 2 2y 2 9y + 7 = 0 (2y 7)(y 1) = 0 y = 7 2, 1 x = 4,

More information

If y = f (u) is a differentiable function of u and u = g(x) is a differentiable function of x then dy dx = dy. du du. If y = f (u) then y = f (u) u

If y = f (u) is a differentiable function of u and u = g(x) is a differentiable function of x then dy dx = dy. du du. If y = f (u) then y = f (u) u Section 3 4B Lecture The Chain Rule If y = f (u) is a differentiable function of u and u = g(x) is a differentiable function of x then dy dx = dy du du dx or If y = f (u) then y = f (u) u The Chain Rule

More information

Elements of Probability Theory

Elements of Probability Theory Short Guides to Microeconometrics Fall 2016 Kurt Schmidheiny Unversität Basel Elements of Probability Theory Contents 1 Random Variables and Distributions 2 1.1 Univariate Random Variables and Distributions......

More information

Lecture 7: Differential Equations

Lecture 7: Differential Equations Math 94 Professor: Padraic Bartlett Lecture 7: Differential Equations Week 7 UCSB 205 This is the seventh week of the Mathematics Subject Test GRE prep course; here, we review various techniques used to

More information

Advanced Eng. Mathematics

Advanced Eng. Mathematics Koya University Faculty of Engineering Petroleum Engineering Department Advanced Eng. Mathematics Lecture 6 Prepared by: Haval Hawez E-mail: haval.hawez@koyauniversity.org 1 Second Order Linear Ordinary

More information

Lecture Notes on the Gaussian Distribution

Lecture Notes on the Gaussian Distribution Lecture Notes on the Gaussian Distribution Hairong Qi The Gaussian distribution is also referred to as the normal distribution or the bell curve distribution for its bell-shaped density curve. There s

More information

M343 Homework 3 Enrique Areyan May 17, 2013

M343 Homework 3 Enrique Areyan May 17, 2013 M343 Homework 3 Enrique Areyan May 17, 013 Section.6 3. Consider the equation: (3x xy + )dx + (6y x + 3)dy = 0. Let M(x, y) = 3x xy + and N(x, y) = 6y x + 3. Since: y = x = N We can conclude that this

More information

Introduction to Probability and Stocastic Processes - Part I

Introduction to Probability and Stocastic Processes - Part I Introduction to Probability and Stocastic Processes - Part I Lecture 2 Henrik Vie Christensen vie@control.auc.dk Department of Control Engineering Institute of Electronic Systems Aalborg University Denmark

More information

MIDTERM 1 PRACTICE PROBLEM SOLUTIONS

MIDTERM 1 PRACTICE PROBLEM SOLUTIONS MIDTERM 1 PRACTICE PROBLEM SOLUTIONS Problem 1. Give an example of: (a) an ODE of the form y (t) = f(y) such that all solutions with y(0) > 0 satisfy y(t) = +. lim t + (b) an ODE of the form y (t) = f(y)

More information

Legendre s Equation. PHYS Southern Illinois University. October 18, 2016

Legendre s Equation. PHYS Southern Illinois University. October 18, 2016 Legendre s Equation PHYS 500 - Southern Illinois University October 18, 2016 PHYS 500 - Southern Illinois University Legendre s Equation October 18, 2016 1 / 11 Legendre s Equation Recall We are trying

More information

Explicit Bounds for the Distribution Function of the Sum of Dependent Normally Distributed Random Variables

Explicit Bounds for the Distribution Function of the Sum of Dependent Normally Distributed Random Variables Explicit Bounds for the Distribution Function of the Sum of Dependent Normally Distributed Random Variables Walter Schneider July 26, 20 Abstract In this paper an analytic expression is given for the bounds

More information