The Strong Markov Property
|
|
- April Burke
- 7 years ago
- Views:
Transcription
1 The Strong Markov Property Throughout, X := {X } denotes a Lévy process on with triple (σ), and exponent Ψ. And from now on, we let { } denote the natural filtration of X, all the time remembering that, in accord with our earlier convention, { } satisfies the usual conditions. Transition measures and the Markov property Definition 1. The transition measures of X are the probability measures P (A) := P { + X A} defined for all,, and A ( ). In other words, each P ( ) is the law of X started at. We single out the case =by setting µ (A) := P ( A); thus, µ is the distribution of X for all >. Note, in particular, that µ = δ is the point mass at. Proposition 2. For all, and measurable : +, E[(X + ) ]= () P (X d) a.s. 53
2 54 9. The Strong Markov Property Consequently, for all, < 1 < 2 < <, and measurable 1 : +, E ( + X ) (1) =1 = P 1 ( d 1 ) P 2 1 ( 1 d 2 ) P 1 ( 1 d ) ( ) Property (1) is called the Chapman Kolmogorov equation. That property has the following ready consequence: Transition measures determine the finite-dimensional distributions of X uniquely. Definition 3. Any stochastic process {X } that satisfies the Chapman- Kolmogorov equation is called a Markov process. This definition continues to make sense if we replace ( ( )) by any measurable space on which we can construct infinite families of random variables. Thus, Lévy processes are cadlag Markov processes that have special addition properties. In particular, as Exercise below 1 shows, Lévy processes have the important property that the finite-dimensional distributions of X are described not only by {P ( )} but by the much-smaller family {µ ( )}. Note, in particular, that if : + is measurable,, and, then E( + X )= () P (d) = ( + ) µ (d) Therefore, if we define µ (A) := µ ( A) for all and A ( ), where A := { : A}, then we have the following convolution formula, valid for all measurable : +,, and : E( + X )=( µ )() And, more generally, for all measurable : +,, and [Why is this more general?] E[(X + ) ]=( µ )(X ) Proposition 4. The family {µ } of Borel probability measure on is a convolution semigroup in the sense that µ µ = µ + for all. Moreover, ˆµ (ξ) = exp( Ψ(ξ)) for all and ξ. Similarly, { µ } is a convolution semigroup with ˆ µ (ξ) = exp( Ψ( ξ)). a.s. =1
3 The strong Markov property 55 Proof. The assertion about µ follows from the assertion about µ [or you can repeat the following with µ in place of µ]. Since µ is the distribution of X, the characteristic function of X is described by ˆµ (ξ) = exp( Ψ(ξ)). The proposition follows immediately from this, because ˆµ (ξ) ˆµ (ξ) = exp( ( + )Ψ(ξ)) = ˆµ + (ξ). The strong Markov property Theorem 5 (The strong Markov property). Let T be a finite stopping time. Then, the process X T := {X T}, defined by X T := X T+ X T is a Lévy process with exponent Ψ and independent of T. Proof. X T is manifestly cadlag [because X is]. In addition, one checks that whenever < 1 < < and A 1 A ( ), P XT+ X T A T = P X A a.s.; =1 see Exercise 2 on page 32. This readily implies that the finite-dimensional distributions of X T are the same as the finite-dimensional distributions of X, and the result follows. Theorem 5 has a number of deep consequences. The following shows that Lévy processes have the following variation of strong Markov property. The following is attractive, in part because it can be used to study processes that do not have good additivity properties. Corollary 6. For all finite stopping times T, every, and all measurable functions : +. =1 E (X T+ ) T = ()P (X T d) a.s. Let T be a finite stopping time, and then define (T) = { (T) } to be the natural filtration of the Lévy process X T. The following is a useful corollary of the strong Markov property. Corollary 7 (Blumenthal s zero-one law; Blumenthal, 1957). Let T be a finite stopping time. Then (T) is trivial; i.e., P(A) {1} for all A (T).
4 56 9. The Strong Markov Property The following are nontrivial examples of elements of (T) : X T+ X T Υ 1 := lim inf 1/α = where α> is fixed; X T+ X T Υ 2 := lim sup =1 ; or 2 ln ln(1/) Υ 3 := such that X T+ X T > for all 1 in dimension one, etc. Proof of Blumenthal s zero-one law. The strong Markov property [Corollary 6] reduces the problem to T. And of course we do not need to write () since () is the same object as. For all 1 define to be the completion of the sigma-algebra generated by the collection {X +2 X 2 } [2 ]. By the Markov property, 1 2 are independent sigma-algebras. Their tail sigma-algebra is the smallest sigma-algebra that contains =N for all N 1. Clearly is complete, and Kolmogorov s zero-one law tells us that is trivial. Because =N contains the sigma-algebra generated by all increments of the form X + X where [ ] for some N, and since X as, it follows that contains, where denotes the sigma-algebra generated by {X } []. Since is complete, this implies [in fact, = ] as well, and hence is trivial because is. Thus, for example, take the set Υ 1 introduced earlier. We can apply the Blumenthal zero-one to the Υ 1 and deduce the following: X T+ X T For every α>, P lim inf 1/α = =or 1 You should construct a few more examples of this type. Feller semigroups and resolvents Define a collection {P } of linear operators by (P )() := E( + X )= ()P (d) =( µ )() for. [Since X =, P = δ is point mass at zero.] The preceding is well defined for various measurable functions :. For instance, everything is fine if is nonnegative, and also if (P )() < for all and [in that case, we can write P = P + P ]. The Markov property of X [see, in particular, Proposition 4] tells us that (P + )() =(P (P ))(). In other words, P + = P P = P P for all (2)
5 Feller semigroups and resolvents 57 where P P is shorthand for P (P ) etc. Since P and P commute, in the preceding sense, there is no ambiguity in dropping the parentheses. Definition 8. The family {P } is the semigroup associated with the Lévy process X. The resolvent { λ } λ> of the process X is the family of linear operators defined by ( λ )() := e λ (P )() d = E e λ ( + X ) d (λ >) This can make sense also for λ =, and we write in place of. Finally, λ is called the λ-potential of when λ>; when λ =, we call it the potential of instead. emark 9. It might be good to note that we can cast the strong Markov property in terms of the semigroup {P } as follows: For all, finite stopping times T, and : + measurable, E[(X T+ ) T ]=(P )(X T ) almost surely. Formally speaking, λ = e λ P d (λ ) defines the Laplace transform of the [infinite-dimensional] function P. Once again, λ is defined for all Borel measurable :, if either ; or if λ is well defined. ecall that C ( ) denotes the collection of all continuous : that vanish at infinity [() as ]; C ( ) is a Banach space in norm := sup (). The following are easy to verify: (1) Each P is a contraction [more precisely nonexpansive] on C ( ). That is, P for all ; (2) {P } is a Feller semigroup. That is, each P maps C ( ) to itself and lim P =; (3) If λ>, then λ λ is a contraction [nonexpansive] on C ( ); (4) If λ>, then λ λ maps C ( ) to itself. The preceding describe the smoothness behavior of P and λ for fixed and λ. It is also not hard to describe the smoothness properties of them as functions of and λ. For instance, Proposition 1. For all C ( ), lim sup P + P = and lim λ λ λ =
6 58 9. The Strong Markov Property Proof. We observe that P = sup E( + X ) () sup E ( + X ) () Now every C ( ) is uniformly continuous and bounded on all of. Since X is right continuous and is bounded, it follows from the bounded convergence theorem that lim P =. But the semigroup property implies that P + P = P (P ) P, since P is a contraction on C ( ). This proves the first assertion. The second follows from the first, since λ λ = e P /λ d by a change of variables. Proposition 11. If C ( ) L ( ) for some [1 ), then P L ( ) L ( ) for all and λ λ L ( ) L ( ) for all λ>. In words, the preceding states that P L ( ) for every [1 ) and λ >. and λ λ are contractions on Proof. If C ( ) L ( ), then for all, (P )() d = E( + X ) d E ( + X ) d = () d This proves the assertion about P ; the one about λ is proved similarly. The Hille Yosida theorem One checks directly that for all µ λ, λ µ =(µ λ) λ µ (3) This is called the resolvent equation, and has many consequences. For instance, the resolvent equation implies readily the commutation property µ λ = λ µ. For another consequence of the resolvent eqution, suppose = µ for some C ( ) and µ>. Then, C ( ) and by the resolvent equation, λ =(µ λ) λ. Consequently, = λ, where := +(λ µ) λ C ( ). In other words, µ (C ( )) = λ (C ( )), whence Dom[L] := µ : C ( ) does not depend on µ>. And Dom[L] is dense in C ( ) [Proposition 1]. For yet another application of the resolvent equation, let us suppose that λ for some λ> and C ( ). Then the resolvent equation implies that µ for all µ. Therefore, = lim µ µ µ =. This implies
7 The Hille Yosida theorem 59 that every λ is a one-to-one and onto map from C ( ) to Dom[L]; i.e., it is invertible! Definition 12. The [infinitesimal] generator of X is the linear operator L : Dom[L] C ( ) that is defined uniquely by L := λi λ 1 where I := defines the identity operator I on C ( ). The space Dom[L] is the domain of L. The following is perhaps a better way to think about L; roughly speaking, it asserts that P L for small, and λ λ λ 1 L for λ large. Theorem 13 (Hille XXX, Yosida XXX). If Dom[L], then lim sup λ( λ )() () λ (L)() 1/λ = lim sup (P )() () (L)() = Because = P, the Hille Yosida theorem implies, among other things, that ( / )P = = L, where the partial derivative is really a right derivative. See Exercise 4 for a consequence in partial integro-differential equations. Proof. Thanks to Proposition 1 and the definition of the generator, L = λ 1 for all Dom[L], whence λ λ λ L = λ λ L in C ( ) as λ 1/λ This proves half of the theorem. For the other half recall that Dom[L] is the collection of all functions of the form = λ, where C ( ) and λ>. By the semigroup property, for such λ and we have P λ = e λ P + d = e λ e λ P d = e λ λ e λ P d Consequently, for all = λ Dom[L], P e λ 1 = λ eλ λ λ e λ P d in C ( ) as But λ λ = λ 1 λ = L.
8 6 9. The Strong Markov Property The form of the generator Let denote the collection of all rapidly-decreasing test functions :. That is, if and only if C ( ), and and all of its partial derivatives vanish faster than any polynomial. In other words, if D is a differential operator [of finite order] and 1, then sup (1 + ) (D)() <. It is easy to see that L 1 ( ) C ( ) and is dense in C ( ). And it is well known that if, then ˆ as well, and vice versa. It is possible to see that if ˆ L 1 ( ), then for all and λ>, P (ξ) =e Ψ( ξ)ˆ(ξ) λ (ξ) = ˆ(ξ) λ + Ψ( ξ) for all ξ (4) Therefore, it follows fairly readily that when Dom[L] L 1 ( ), L L 1 ( ), and ˆ L 1 ( ), then we have L(ξ) = Ψ( ξ)ˆ(ξ) for every ξ (5) It follows immediately from these calculations that: (i) Every P and λ map to ; and (ii) Therefore, is dense in Dom[L]. Therefore, we can try to understand L better by trying to compute L not for all Dom[L], but rather for all in the dense subcollection. But the formula for the Fourier transform of L [together with the estimate Ψ(ξ) = O(ξ 2 )] shows that L : and (L)() = 1 (2π) e ξ Ψ( ξ)ˆ(ξ) dξ for all and Consider the simplest case that the process X satisfies X = for some ; i.e., Ψ(ξ) = ( ξ). In that case, we have (L)() = 1 (2π) ( ξ)e ξ ˆ(ξ) dξ = 1 (2π) e ξ ( (ξ)) dξ = ( )() thanks to the inversion formula. The very same computation works in the more general setting, and yields Theorem 14. If, then L = C + J, where (C)() = ( )()+ 1 (σ 2 σ) () 2 and (J)() := 1 ( + ) () ( )()1l [1) () (d) for all Moreover, J is the generator of the pure-jump part; and C = σ σ is the generator of the continuous/gaussian part.
9 Problems for Lecture 9 61 Here are some examples: If X is Brownian motion on, then L = 1 2 is one-half of the Laplace operator [on ]; If X is the Poisson process on with intensity λ ( ), then (L)() =λ[( + 1) ()] for [might be easier to check Fourier transforms]; If X is the isotropic stable process with index α ( 2), then for all, ( + ) () ( )()1l[1] () (L)() =const d +α Since L(ξ) ˆ(ξ) ξ α, L is called the fractional Laplacian with fractional power α/2. It is sometimes written as L = ( ) α/2 ; the notation is justified [and explained] by the symbolic calculus of pseudo-differential operators. Problems for Lecture 9 1. Prove that P (A)=P (A ) for all,, and A ( ), where A := { : A}. Conclude that the Chapman Kolmogorov equation is equivalent to the following formula for E =1 ( + X ): P 1 (d 1 ) P 2 1 (d 2 ) P 1 (d ) ( + + ) using the same notation as Proposition Suppose Y L 1 (P) is measurable with respect to σ({x } ) for a fixed nonrandom. Prove that E(Y )=E(Y X ) a.s. 3. Verify that X := { X } is a Lévy process; compute its transition measures P (d) and verify the following duality relationship: For all measurable : + and, () d () P (d) = () d () P (d) 4. Prove that () := (P )() solves [weakly] the partial integro-differential equation ()=(L)() for all > and subject to ( )= (). 5. Derive the resolvent equation (3). 6. Verify (4) and (5). =1
10 62 9. The Strong Markov Property 7. First, improve Proposition 11 in the case =2as follows: Prove that there exists a unique continuous extension of P to all of L 2 ( ). Denote that by P still. Next, define Dom 2 [L] := L 2 ( ): Ψ(ξ) 2 ˆ(ξ) 2 dξ< Then prove that lim 1 (P ) exists, as a limit in L 2 ( ), for all Dom 2 [L]. Identify the limit when C ( ).
MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.436J/15.085J Fall 2008 Lecture 5 9/17/2008 RANDOM VARIABLES
MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.436J/15.085J Fall 2008 Lecture 5 9/17/2008 RANDOM VARIABLES Contents 1. Random variables and measurable functions 2. Cumulative distribution functions 3. Discrete
More informationThe sample space for a pair of die rolls is the set. The sample space for a random number between 0 and 1 is the interval [0, 1].
Probability Theory Probability Spaces and Events Consider a random experiment with several possible outcomes. For example, we might roll a pair of dice, flip a coin three times, or choose a random real
More informationBANACH AND HILBERT SPACE REVIEW
BANACH AND HILBET SPACE EVIEW CHISTOPHE HEIL These notes will briefly review some basic concepts related to the theory of Banach and Hilbert spaces. We are not trying to give a complete development, but
More informationFUNCTIONAL ANALYSIS LECTURE NOTES: QUOTIENT SPACES
FUNCTIONAL ANALYSIS LECTURE NOTES: QUOTIENT SPACES CHRISTOPHER HEIL 1. Cosets and the Quotient Space Any vector space is an abelian group under the operation of vector addition. So, if you are have studied
More information1 if 1 x 0 1 if 0 x 1
Chapter 3 Continuity In this chapter we begin by defining the fundamental notion of continuity for real valued functions of a single real variable. When trying to decide whether a given function is or
More informationMathematics Course 111: Algebra I Part IV: Vector Spaces
Mathematics Course 111: Algebra I Part IV: Vector Spaces D. R. Wilkins Academic Year 1996-7 9 Vector Spaces A vector space over some field K is an algebraic structure consisting of a set V on which are
More informationHOMEWORK 5 SOLUTIONS. n!f n (1) lim. ln x n! + xn x. 1 = G n 1 (x). (2) k + 1 n. (n 1)!
Math 7 Fall 205 HOMEWORK 5 SOLUTIONS Problem. 2008 B2 Let F 0 x = ln x. For n 0 and x > 0, let F n+ x = 0 F ntdt. Evaluate n!f n lim n ln n. By directly computing F n x for small n s, we obtain the following
More informationMATH 4330/5330, Fourier Analysis Section 11, The Discrete Fourier Transform
MATH 433/533, Fourier Analysis Section 11, The Discrete Fourier Transform Now, instead of considering functions defined on a continuous domain, like the interval [, 1) or the whole real line R, we wish
More informationMoreover, under the risk neutral measure, it must be the case that (5) r t = µ t.
LECTURE 7: BLACK SCHOLES THEORY 1. Introduction: The Black Scholes Model In 1973 Fisher Black and Myron Scholes ushered in the modern era of derivative securities with a seminal paper 1 on the pricing
More information1 Sets and Set Notation.
LINEAR ALGEBRA MATH 27.6 SPRING 23 (COHEN) LECTURE NOTES Sets and Set Notation. Definition (Naive Definition of a Set). A set is any collection of objects, called the elements of that set. We will most
More informationMath 4310 Handout - Quotient Vector Spaces
Math 4310 Handout - Quotient Vector Spaces Dan Collins The textbook defines a subspace of a vector space in Chapter 4, but it avoids ever discussing the notion of a quotient space. This is understandable
More information3. Reaction Diffusion Equations Consider the following ODE model for population growth
3. Reaction Diffusion Equations Consider the following ODE model for population growth u t a u t u t, u 0 u 0 where u t denotes the population size at time t, and a u plays the role of the population dependent
More informationLecture 13 - Basic Number Theory.
Lecture 13 - Basic Number Theory. Boaz Barak March 22, 2010 Divisibility and primes Unless mentioned otherwise throughout this lecture all numbers are non-negative integers. We say that A divides B, denoted
More informationMATH10212 Linear Algebra. Systems of Linear Equations. Definition. An n-dimensional vector is a row or a column of n numbers (or letters): a 1.
MATH10212 Linear Algebra Textbook: D. Poole, Linear Algebra: A Modern Introduction. Thompson, 2006. ISBN 0-534-40596-7. Systems of Linear Equations Definition. An n-dimensional vector is a row or a column
More informationMathematical Finance
Mathematical Finance Option Pricing under the Risk-Neutral Measure Cory Barnes Department of Mathematics University of Washington June 11, 2013 Outline 1 Probability Background 2 Black Scholes for European
More information26 Integers: Multiplication, Division, and Order
26 Integers: Multiplication, Division, and Order Integer multiplication and division are extensions of whole number multiplication and division. In multiplying and dividing integers, the one new issue
More informationLinear Maps. Isaiah Lankham, Bruno Nachtergaele, Anne Schilling (February 5, 2007)
MAT067 University of California, Davis Winter 2007 Linear Maps Isaiah Lankham, Bruno Nachtergaele, Anne Schilling (February 5, 2007) As we have discussed in the lecture on What is Linear Algebra? one of
More informationSimilarity and Diagonalization. Similar Matrices
MATH022 Linear Algebra Brief lecture notes 48 Similarity and Diagonalization Similar Matrices Let A and B be n n matrices. We say that A is similar to B if there is an invertible n n matrix P such that
More informationINDISTINGUISHABILITY OF ABSOLUTELY CONTINUOUS AND SINGULAR DISTRIBUTIONS
INDISTINGUISHABILITY OF ABSOLUTELY CONTINUOUS AND SINGULAR DISTRIBUTIONS STEVEN P. LALLEY AND ANDREW NOBEL Abstract. It is shown that there are no consistent decision rules for the hypothesis testing problem
More informationScalar Valued Functions of Several Variables; the Gradient Vector
Scalar Valued Functions of Several Variables; the Gradient Vector Scalar Valued Functions vector valued function of n variables: Let us consider a scalar (i.e., numerical, rather than y = φ(x = φ(x 1,
More information36 CHAPTER 1. LIMITS AND CONTINUITY. Figure 1.17: At which points is f not continuous?
36 CHAPTER 1. LIMITS AND CONTINUITY 1.3 Continuity Before Calculus became clearly de ned, continuity meant that one could draw the graph of a function without having to lift the pen and pencil. While this
More informationLEARNING OBJECTIVES FOR THIS CHAPTER
CHAPTER 2 American mathematician Paul Halmos (1916 2006), who in 1942 published the first modern linear algebra book. The title of Halmos s book was the same as the title of this chapter. Finite-Dimensional
More informationContinued Fractions and the Euclidean Algorithm
Continued Fractions and the Euclidean Algorithm Lecture notes prepared for MATH 326, Spring 997 Department of Mathematics and Statistics University at Albany William F Hammond Table of Contents Introduction
More informationSo let us begin our quest to find the holy grail of real analysis.
1 Section 5.2 The Complete Ordered Field: Purpose of Section We present an axiomatic description of the real numbers as a complete ordered field. The axioms which describe the arithmetic of the real numbers
More information1 The Brownian bridge construction
The Brownian bridge construction The Brownian bridge construction is a way to build a Brownian motion path by successively adding finer scale detail. This construction leads to a relatively easy proof
More informationNOTES ON LINEAR TRANSFORMATIONS
NOTES ON LINEAR TRANSFORMATIONS Definition 1. Let V and W be vector spaces. A function T : V W is a linear transformation from V to W if the following two properties hold. i T v + v = T v + T v for all
More informationMATRIX ALGEBRA AND SYSTEMS OF EQUATIONS. + + x 2. x n. a 11 a 12 a 1n b 1 a 21 a 22 a 2n b 2 a 31 a 32 a 3n b 3. a m1 a m2 a mn b m
MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS 1. SYSTEMS OF EQUATIONS AND MATRICES 1.1. Representation of a linear system. The general system of m equations in n unknowns can be written a 11 x 1 + a 12 x 2 +
More information4.5 Linear Dependence and Linear Independence
4.5 Linear Dependence and Linear Independence 267 32. {v 1, v 2 }, where v 1, v 2 are collinear vectors in R 3. 33. Prove that if S and S are subsets of a vector space V such that S is a subset of S, then
More informationI. GROUPS: BASIC DEFINITIONS AND EXAMPLES
I GROUPS: BASIC DEFINITIONS AND EXAMPLES Definition 1: An operation on a set G is a function : G G G Definition 2: A group is a set G which is equipped with an operation and a special element e G, called
More informationNotes on metric spaces
Notes on metric spaces 1 Introduction The purpose of these notes is to quickly review some of the basic concepts from Real Analysis, Metric Spaces and some related results that will be used in this course.
More informationMetric Spaces Joseph Muscat 2003 (Last revised May 2009)
1 Distance J Muscat 1 Metric Spaces Joseph Muscat 2003 (Last revised May 2009) (A revised and expanded version of these notes are now published by Springer.) 1 Distance A metric space can be thought of
More informationLS.6 Solution Matrices
LS.6 Solution Matrices In the literature, solutions to linear systems often are expressed using square matrices rather than vectors. You need to get used to the terminology. As before, we state the definitions
More informationLectures 5-6: Taylor Series
Math 1d Instructor: Padraic Bartlett Lectures 5-: Taylor Series Weeks 5- Caltech 213 1 Taylor Polynomials and Series As we saw in week 4, power series are remarkably nice objects to work with. In particular,
More informationUndergraduate Notes in Mathematics. Arkansas Tech University Department of Mathematics
Undergraduate Notes in Mathematics Arkansas Tech University Department of Mathematics An Introductory Single Variable Real Analysis: A Learning Approach through Problem Solving Marcel B. Finan c All Rights
More informationLINEAR ALGEBRA W W L CHEN
LINEAR ALGEBRA W W L CHEN c W W L Chen, 1997, 2008 This chapter is available free to all individuals, on understanding that it is not to be used for financial gain, and may be downloaded and/or photocopied,
More informationSolving Linear Systems, Continued and The Inverse of a Matrix
, Continued and The of a Matrix Calculus III Summer 2013, Session II Monday, July 15, 2013 Agenda 1. The rank of a matrix 2. The inverse of a square matrix Gaussian Gaussian solves a linear system by reducing
More informationMatrix Representations of Linear Transformations and Changes of Coordinates
Matrix Representations of Linear Transformations and Changes of Coordinates 01 Subspaces and Bases 011 Definitions A subspace V of R n is a subset of R n that contains the zero element and is closed under
More informationAdaptive Online Gradient Descent
Adaptive Online Gradient Descent Peter L Bartlett Division of Computer Science Department of Statistics UC Berkeley Berkeley, CA 94709 bartlett@csberkeleyedu Elad Hazan IBM Almaden Research Center 650
More informationCHAPTER II THE LIMIT OF A SEQUENCE OF NUMBERS DEFINITION OF THE NUMBER e.
CHAPTER II THE LIMIT OF A SEQUENCE OF NUMBERS DEFINITION OF THE NUMBER e. This chapter contains the beginnings of the most important, and probably the most subtle, notion in mathematical analysis, i.e.,
More informationMATRIX ALGEBRA AND SYSTEMS OF EQUATIONS
MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS Systems of Equations and Matrices Representation of a linear system The general system of m equations in n unknowns can be written a x + a 2 x 2 + + a n x n b a
More informationLet H and J be as in the above lemma. The result of the lemma shows that the integral
Let and be as in the above lemma. The result of the lemma shows that the integral ( f(x, y)dy) dx is well defined; we denote it by f(x, y)dydx. By symmetry, also the integral ( f(x, y)dx) dy is well defined;
More information9.4. The Scalar Product. Introduction. Prerequisites. Learning Style. Learning Outcomes
The Scalar Product 9.4 Introduction There are two kinds of multiplication involving vectors. The first is known as the scalar product or dot product. This is so-called because when the scalar product of
More information10.2 Series and Convergence
10.2 Series and Convergence Write sums using sigma notation Find the partial sums of series and determine convergence or divergence of infinite series Find the N th partial sums of geometric series and
More informationUnified Lecture # 4 Vectors
Fall 2005 Unified Lecture # 4 Vectors These notes were written by J. Peraire as a review of vectors for Dynamics 16.07. They have been adapted for Unified Engineering by R. Radovitzky. References [1] Feynmann,
More informationMEASURE AND INTEGRATION. Dietmar A. Salamon ETH Zürich
MEASURE AND INTEGRATION Dietmar A. Salamon ETH Zürich 12 May 2016 ii Preface This book is based on notes for the lecture course Measure and Integration held at ETH Zürich in the spring semester 2014. Prerequisites
More informationThe degree of a polynomial function is equal to the highest exponent found on the independent variables.
DETAILED SOLUTIONS AND CONCEPTS - POLYNOMIAL FUNCTIONS Prepared by Ingrid Stewart, Ph.D., College of Southern Nevada Please Send Questions and Comments to ingrid.stewart@csn.edu. Thank you! PLEASE NOTE
More informationProbability Theory. Florian Herzog. A random variable is neither random nor variable. Gian-Carlo Rota, M.I.T..
Probability Theory A random variable is neither random nor variable. Gian-Carlo Rota, M.I.T.. Florian Herzog 2013 Probability space Probability space A probability space W is a unique triple W = {Ω, F,
More informationLecture 13: Martingales
Lecture 13: Martingales 1. Definition of a Martingale 1.1 Filtrations 1.2 Definition of a martingale and its basic properties 1.3 Sums of independent random variables and related models 1.4 Products of
More information1 Error in Euler s Method
1 Error in Euler s Method Experience with Euler s 1 method raises some interesting questions about numerical approximations for the solutions of differential equations. 1. What determines the amount of
More information11 Ideals. 11.1 Revisiting Z
11 Ideals The presentation here is somewhat different than the text. In particular, the sections do not match up. We have seen issues with the failure of unique factorization already, e.g., Z[ 5] = O Q(
More informationStudent Outcomes. Lesson Notes. Classwork. Discussion (10 minutes)
NYS COMMON CORE MATHEMATICS CURRICULUM Lesson 5 8 Student Outcomes Students know the definition of a number raised to a negative exponent. Students simplify and write equivalent expressions that contain
More informationMATH 551 - APPLIED MATRIX THEORY
MATH 55 - APPLIED MATRIX THEORY FINAL TEST: SAMPLE with SOLUTIONS (25 points NAME: PROBLEM (3 points A web of 5 pages is described by a directed graph whose matrix is given by A Do the following ( points
More informationMATH 425, PRACTICE FINAL EXAM SOLUTIONS.
MATH 45, PRACTICE FINAL EXAM SOLUTIONS. Exercise. a Is the operator L defined on smooth functions of x, y by L u := u xx + cosu linear? b Does the answer change if we replace the operator L by the operator
More information1 Norms and Vector Spaces
008.10.07.01 1 Norms and Vector Spaces Suppose we have a complex vector space V. A norm is a function f : V R which satisfies (i) f(x) 0 for all x V (ii) f(x + y) f(x) + f(y) for all x,y V (iii) f(λx)
More informationRate of growth of D-frequently hypercyclic functions
Rate of growth of D-frequently hypercyclic functions A. Bonilla Departamento de Análisis Matemático Universidad de La Laguna Hypercyclic Definition A (linear and continuous) operator T in a topological
More information160 CHAPTER 4. VECTOR SPACES
160 CHAPTER 4. VECTOR SPACES 4. Rank and Nullity In this section, we look at relationships between the row space, column space, null space of a matrix and its transpose. We will derive fundamental results
More informationSection 1.3 P 1 = 1 2. = 1 4 2 8. P n = 1 P 3 = Continuing in this fashion, it should seem reasonable that, for any n = 1, 2, 3,..., = 1 2 4.
Difference Equations to Differential Equations Section. The Sum of a Sequence This section considers the problem of adding together the terms of a sequence. Of course, this is a problem only if more than
More information3. Mathematical Induction
3. MATHEMATICAL INDUCTION 83 3. Mathematical Induction 3.1. First Principle of Mathematical Induction. Let P (n) be a predicate with domain of discourse (over) the natural numbers N = {0, 1,,...}. If (1)
More information5.1 Radical Notation and Rational Exponents
Section 5.1 Radical Notation and Rational Exponents 1 5.1 Radical Notation and Rational Exponents We now review how exponents can be used to describe not only powers (such as 5 2 and 2 3 ), but also roots
More information1. Prove that the empty set is a subset of every set.
1. Prove that the empty set is a subset of every set. Basic Topology Written by Men-Gen Tsai email: b89902089@ntu.edu.tw Proof: For any element x of the empty set, x is also an element of every set since
More informationSome stability results of parameter identification in a jump diffusion model
Some stability results of parameter identification in a jump diffusion model D. Düvelmeyer Technische Universität Chemnitz, Fakultät für Mathematik, 09107 Chemnitz, Germany Abstract In this paper we discuss
More informationA Coefficient of Variation for Skewed and Heavy-Tailed Insurance Losses. Michael R. Powers[ 1 ] Temple University and Tsinghua University
A Coefficient of Variation for Skewed and Heavy-Tailed Insurance Losses Michael R. Powers[ ] Temple University and Tsinghua University Thomas Y. Powers Yale University [June 2009] Abstract We propose a
More informationCHAPTER IV - BROWNIAN MOTION
CHAPTER IV - BROWNIAN MOTION JOSEPH G. CONLON 1. Construction of Brownian Motion There are two ways in which the idea of a Markov chain on a discrete state space can be generalized: (1) The discrete time
More informationThis asserts two sets are equal iff they have the same elements, that is, a set is determined by its elements.
3. Axioms of Set theory Before presenting the axioms of set theory, we first make a few basic comments about the relevant first order logic. We will give a somewhat more detailed discussion later, but
More informationElements of probability theory
2 Elements of probability theory Probability theory provides mathematical models for random phenomena, that is, phenomena which under repeated observations yield di erent outcomes that cannot be predicted
More informationNormal distribution. ) 2 /2σ. 2π σ
Normal distribution The normal distribution is the most widely known and used of all distributions. Because the normal distribution approximates many natural phenomena so well, it has developed into a
More informationLinear Algebra Notes for Marsden and Tromba Vector Calculus
Linear Algebra Notes for Marsden and Tromba Vector Calculus n-dimensional Euclidean Space and Matrices Definition of n space As was learned in Math b, a point in Euclidean three space can be thought of
More informationARBITRAGE-FREE OPTION PRICING MODELS. Denis Bell. University of North Florida
ARBITRAGE-FREE OPTION PRICING MODELS Denis Bell University of North Florida Modelling Stock Prices Example American Express In mathematical finance, it is customary to model a stock price by an (Ito) stochatic
More information1.7 Graphs of Functions
64 Relations and Functions 1.7 Graphs of Functions In Section 1.4 we defined a function as a special type of relation; one in which each x-coordinate was matched with only one y-coordinate. We spent most
More informationTHE DIMENSION OF A VECTOR SPACE
THE DIMENSION OF A VECTOR SPACE KEITH CONRAD This handout is a supplementary discussion leading up to the definition of dimension and some of its basic properties. Let V be a vector space over a field
More informationMarkov random fields and Gibbs measures
Chapter Markov random fields and Gibbs measures 1. Conditional independence Suppose X i is a random element of (X i, B i ), for i = 1, 2, 3, with all X i defined on the same probability space (.F, P).
More informationMicroeconomic Theory: Basic Math Concepts
Microeconomic Theory: Basic Math Concepts Matt Van Essen University of Alabama Van Essen (U of A) Basic Math Concepts 1 / 66 Basic Math Concepts In this lecture we will review some basic mathematical concepts
More informationChapter 5. Banach Spaces
9 Chapter 5 Banach Spaces Many linear equations may be formulated in terms of a suitable linear operator acting on a Banach space. In this chapter, we study Banach spaces and linear operators acting on
More informationAn Introduction to Partial Differential Equations in the Undergraduate Curriculum
An Introduction to Partial Differential Equations in the Undergraduate Curriculum J. Tolosa & M. Vajiac LECTURE 11 Laplace s Equation in a Disk 11.1. Outline of Lecture The Laplacian in Polar Coordinates
More informationOverview of Violations of the Basic Assumptions in the Classical Normal Linear Regression Model
Overview of Violations of the Basic Assumptions in the Classical Normal Linear Regression Model 1 September 004 A. Introduction and assumptions The classical normal linear regression model can be written
More information9.2 Summation Notation
9. Summation Notation 66 9. Summation Notation In the previous section, we introduced sequences and now we shall present notation and theorems concerning the sum of terms of a sequence. We begin with a
More informationNotes on Factoring. MA 206 Kurt Bryan
The General Approach Notes on Factoring MA 26 Kurt Bryan Suppose I hand you n, a 2 digit integer and tell you that n is composite, with smallest prime factor around 5 digits. Finding a nontrivial factor
More informationInverse Functions and Logarithms
Section 3. Inverse Functions and Logarithms 1 Kiryl Tsishchanka Inverse Functions and Logarithms DEFINITION: A function f is called a one-to-one function if it never takes on the same value twice; that
More informationLinear Algebra. A vector space (over R) is an ordered quadruple. such that V is a set; 0 V ; and the following eight axioms hold:
Linear Algebra A vector space (over R) is an ordered quadruple (V, 0, α, µ) such that V is a set; 0 V ; and the following eight axioms hold: α : V V V and µ : R V V ; (i) α(α(u, v), w) = α(u, α(v, w)),
More information1. (First passage/hitting times/gambler s ruin problem:) Suppose that X has a discrete state space and let i be a fixed state. Let
Copyright c 2009 by Karl Sigman 1 Stopping Times 1.1 Stopping Times: Definition Given a stochastic process X = {X n : n 0}, a random time τ is a discrete random variable on the same probability space as
More informationCONTINUED FRACTIONS AND FACTORING. Niels Lauritzen
CONTINUED FRACTIONS AND FACTORING Niels Lauritzen ii NIELS LAURITZEN DEPARTMENT OF MATHEMATICAL SCIENCES UNIVERSITY OF AARHUS, DENMARK EMAIL: niels@imf.au.dk URL: http://home.imf.au.dk/niels/ Contents
More informationLecture L3 - Vectors, Matrices and Coordinate Transformations
S. Widnall 16.07 Dynamics Fall 2009 Lecture notes based on J. Peraire Version 2.0 Lecture L3 - Vectors, Matrices and Coordinate Transformations By using vectors and defining appropriate operations between
More informationA positive exponent means repeated multiplication. A negative exponent means the opposite of repeated multiplication, which is repeated
Eponents Dealing with positive and negative eponents and simplifying epressions dealing with them is simply a matter of remembering what the definition of an eponent is. division. A positive eponent means
More informationIEOR 6711: Stochastic Models I Fall 2012, Professor Whitt, Tuesday, September 11 Normal Approximations and the Central Limit Theorem
IEOR 6711: Stochastic Models I Fall 2012, Professor Whitt, Tuesday, September 11 Normal Approximations and the Central Limit Theorem Time on my hands: Coin tosses. Problem Formulation: Suppose that I have
More informationRepresentation of functions as power series
Representation of functions as power series Dr. Philippe B. Laval Kennesaw State University November 9, 008 Abstract This document is a summary of the theory and techniques used to represent functions
More informationReview of Fundamental Mathematics
Review of Fundamental Mathematics As explained in the Preface and in Chapter 1 of your textbook, managerial economics applies microeconomic theory to business decision making. The decision-making tools
More informationRandom variables P(X = 3) = P(X = 3) = 1 8, P(X = 1) = P(X = 1) = 3 8.
Random variables Remark on Notations 1. When X is a number chosen uniformly from a data set, What I call P(X = k) is called Freq[k, X] in the courseware. 2. When X is a random variable, what I call F ()
More informationHow To Know If A Domain Is Unique In An Octempo (Euclidean) Or Not (Ecl)
Subsets of Euclidean domains possessing a unique division algorithm Andrew D. Lewis 2009/03/16 Abstract Subsets of a Euclidean domain are characterised with the following objectives: (1) ensuring uniqueness
More informationSystems of Linear Equations
Systems of Linear Equations Beifang Chen Systems of linear equations Linear systems A linear equation in variables x, x,, x n is an equation of the form a x + a x + + a n x n = b, where a, a,, a n and
More informationLecture 7: Finding Lyapunov Functions 1
Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science 6.243j (Fall 2003): DYNAMICS OF NONLINEAR SYSTEMS by A. Megretski Lecture 7: Finding Lyapunov Functions 1
More informationCartesian Products and Relations
Cartesian Products and Relations Definition (Cartesian product) If A and B are sets, the Cartesian product of A and B is the set A B = {(a, b) :(a A) and (b B)}. The following points are worth special
More informationVieta s Formulas and the Identity Theorem
Vieta s Formulas and the Identity Theorem This worksheet will work through the material from our class on 3/21/2013 with some examples that should help you with the homework The topic of our discussion
More informationChapter 4 Lecture Notes
Chapter 4 Lecture Notes Random Variables October 27, 2015 1 Section 4.1 Random Variables A random variable is typically a real-valued function defined on the sample space of some experiment. For instance,
More informationNotes on Determinant
ENGG2012B Advanced Engineering Mathematics Notes on Determinant Lecturer: Kenneth Shum Lecture 9-18/02/2013 The determinant of a system of linear equations determines whether the solution is unique, without
More informationCONSTANT-SIGN SOLUTIONS FOR A NONLINEAR NEUMANN PROBLEM INVOLVING THE DISCRETE p-laplacian. Pasquale Candito and Giuseppina D Aguí
Opuscula Math. 34 no. 4 2014 683 690 http://dx.doi.org/10.7494/opmath.2014.34.4.683 Opuscula Mathematica CONSTANT-SIGN SOLUTIONS FOR A NONLINEAR NEUMANN PROBLEM INVOLVING THE DISCRETE p-laplacian Pasquale
More informationProbability and Statistics Prof. Dr. Somesh Kumar Department of Mathematics Indian Institute of Technology, Kharagpur
Probability and Statistics Prof. Dr. Somesh Kumar Department of Mathematics Indian Institute of Technology, Kharagpur Module No. #01 Lecture No. #15 Special Distributions-VI Today, I am going to introduce
More information1 Short Introduction to Time Series
ECONOMICS 7344, Spring 202 Bent E. Sørensen January 24, 202 Short Introduction to Time Series A time series is a collection of stochastic variables x,.., x t,.., x T indexed by an integer value t. The
More informationGod created the integers and the rest is the work of man. (Leopold Kronecker, in an after-dinner speech at a conference, Berlin, 1886)
Chapter 2 Numbers God created the integers and the rest is the work of man. (Leopold Kronecker, in an after-dinner speech at a conference, Berlin, 1886) God created the integers and the rest is the work
More informationRow Ideals and Fibers of Morphisms
Michigan Math. J. 57 (2008) Row Ideals and Fibers of Morphisms David Eisenbud & Bernd Ulrich Affectionately dedicated to Mel Hochster, who has been an inspiration to us for many years, on the occasion
More information0 <β 1 let u(x) u(y) kuk u := sup u(x) and [u] β := sup
456 BRUCE K. DRIVER 24. Hölder Spaces Notation 24.1. Let Ω be an open subset of R d,bc(ω) and BC( Ω) be the bounded continuous functions on Ω and Ω respectively. By identifying f BC( Ω) with f Ω BC(Ω),
More information