A. Approximations
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Discriminant Analysis
Chapter 5 Discriminant Analysis This chapter considers discriminant analysis: given p measurements w, we want to correctly classify w into one of G groups or populations. The max- imum likelihood, Bayesian, and Fisher’s discriminant rules are used to show why methods like linear and quadratic discriminant analysis can work well for a wide variety of group distributions. 5.1 Introduction Definition 5.1. In supervised classification, there are G known groups and m test cases to be classified. Each test case is assigned to exactly one group based on its measurements wi. Suppose there are G populations or groups or classes where G 2. Assume ≥ that for each population there is a probability density function (pdf) fj(z) where z is a p 1 vector and j =1,...,G. Hence if the random vector x comes × from population j, then x has pdf fj (z). Assume that there is a random sam- ple of nj cases x1,j,..., xnj ,j for each group. Let (xj , Sj ) denote the sample mean and covariance matrix for each group. Let wi be a new p 1 (observed) random vector from one of the G groups, but the group is unknown.× Usually there are many wi, and discriminant analysis (DA) or classification attempts to allocate the wi to the correct groups. The w1,..., wm are known as the test data. Let πk = the (prior) probability that a randomly selected case wi belongs to the kth group. If x1,1..., xnG,G are a random sample of cases from G the collection of G populations, thenπ ˆk = nk/n where n = i=1 ni. -
Solving Quadratic Equations by Factoring
5.2 Solving Quadratic Equations by Factoring Goals p Factor quadratic expressions and solve quadratic equations by factoring. p Find zeros of quadratic functions. Your Notes VOCABULARY Binomial An expression with two terms, such as x ϩ 1 Trinomial An expression with three terms, such as x2 ϩ x ϩ 1 Factoring A process used to write a polynomial as a product of other polynomials having equal or lesser degree Monomial An expression with one term, such as 3x Quadratic equation in one variable An equation that can be written in the form ax2 ϩ bx ϩ c ϭ 0 where a 0 Zero of a function A number k is a zero of a function f if f(k) ϭ 0. Example 1 Factoring a Trinomial of the Form x2 ؉ bx ؉ c Factor x2 Ϫ 9x ϩ 14. Solution You want x2 Ϫ 9x ϩ 14 ϭ (x ϩ m)(x ϩ n) where mn ϭ 14 and m ϩ n ϭ Ϫ9 . Factors of 14 1, Ϫ1, Ϫ 2, Ϫ2, Ϫ (m, n) 14 14 7 7 Sum of factors Ϫ Ϫ m ؉ n) 15 15 9 9) The table shows that m ϭ Ϫ2 and n ϭ Ϫ7 . So, x2 Ϫ 9x ϩ 14 ϭ ( x Ϫ 2 )( x Ϫ 7 ). Lesson 5.2 • Algebra 2 Notetaking Guide 97 Your Notes Example 2 Factoring a Trinomial of the Form ax2 ؉ bx ؉ c Factor 2x2 ϩ 13x ϩ 6. Solution You want 2x2 ϩ 13x ϩ 6 ϭ (kx ϩ m)(lx ϩ n) where k and l are factors of 2 and m and n are ( positive ) factors of 6 . -
1 Probelms on Implicit Differentiation 2 Problems on Local Linearization
Math-124 Calculus Chapter 3 Review 1 Probelms on Implicit Di®erentiation #1. A function is de¯ned implicitly by x3y ¡ 3xy3 = 3x + 4y + 5: Find y0 in terms of x and y. In problems 2-6, ¯nd the equation of the tangent line to the curve at the given point. x3 + 1 #2. + 2y2 = 1 ¡ 2x + 4y at the point (2; ¡1). y 1 #3. 4ey + 3x = + (y + 1)2 + 5x at the point (1; 0). x #4. (3x ¡ 2y)2 + x3 = y3 ¡ 2x ¡ 4 at the point (1; 2). p #5. xy + x3 = y3=2 ¡ y ¡ x at the point (1; 4). 2 #6. x sin(y ¡ 3) + 2y = 4x3 + at the point (1; 3). x #7. Find y00 for the curve xy + 2y3 = x3 ¡ 22y at the point (3; 1): #8. Find the points at which the curve x3y3 = x + y has a horizontal tangent. 2 Problems on Local Linearization #1. Let f(x) = (x + 2)ex. Find the value of f(0). Use this to approximate f(¡:2). #2. f(2) = 4 and f 0(2) = 7. Use linear approximation to approximate f(2:03). 6x4 #3. f(1) = 9 and f 0(x) = : Use a linear approximation to approximate f(1:02). x2 + 1 #4. A linear approximation is used to approximate y = f(x) at the point (3; 1). When ¢x = :06 and ¢y = :72. Find the equation of the tangent line. 3 Problems on Absolute Maxima and Minima 1 #1. For the function f(x) = x3 ¡ x2 ¡ 8x + 1, ¯nd the x-coordinates of the absolute max and 3 absolute min on the interval ² a) ¡3 · x · 5 ² b) 0 · x · 5 3 #2. -
Linear Approximation of the Derivative
Section 3.9 - Linear Approximation and the Derivative Idea: When we zoom in on a smooth function and its tangent line, they are very close to- gether. So for a function that is hard to evaluate, we can use the tangent line to approximate values of the derivative. p 3 Example Usep a tangent line to approximatep 8:1 without a calculator. Let f(x) = 3 x = x1=3. We know f(8) = 3 8 = 2. We'll use the tangent line at x = 8. 1 1 1 1 f 0(x) = x−2=3 so f 0(8) = (8)−2=3 = · 3 3 3 4 Our tangent line passes through (8; 2) and has slope 1=12: 1 y = (x − 8) + 2: 12 To approximate f(8:1) we'll find the value of the tangent line at x = 8:1. 1 y = (8:1 − 8) + 2 12 1 = (:1) + 2 12 1 = + 2 120 241 = 120 p 3 241 So 8:1 ≈ 120 : p How accurate was this approximation? We'll use a calculator now: 3 8:1 ≈ 2:00829885. 241 −5 Taking the difference, 2:00829885 − 120 ≈ −3:4483 × 10 , a pretty good approximation! General Formulas for Linear Approximation The tangent line to f(x) at x = a passes through the point (a; f(a)) and has slope f 0(a) so its equation is y = f 0(a)(x − a) + f(a) The tangent line is the best linear approximation to f(x) near x = a so f(x) ≈ f(a) + f 0(a)(x − a) this is called the local linear approximation of f(x) near x = a. -
DYNAMICAL SYSTEMS Contents 1. Introduction 1 2. Linear Systems 5 3
DYNAMICAL SYSTEMS WILLY HU Contents 1. Introduction 1 2. Linear Systems 5 3. Non-linear systems in the plane 8 3.1. The Linearization Theorem 11 3.2. Stability 11 4. Applications 13 4.1. A Model of Animal Conflict 13 4.2. Bifurcations 14 Acknowledgments 15 References 15 Abstract. This paper seeks to establish the foundation for examining dy- namical systems. Dynamical systems are, very broadly, systems that can be modelled by systems of differential equations. In this paper, we will see how to examine the qualitative structure of a system of differential equations and how to model it geometrically, and what information can be gained from such an analysis. We will see what it means for focal points to be stable and unstable, and how we can apply this to examining population growth and evolution, bifurcations, and other applications. 1. Introduction This paper is based on Arrowsmith and Place's book, Dynamical Systems.I have included corresponding references for propositions, theorems, and definitions. The images included in this paper are also from their book. Definition 1.1. (Arrowsmith and Place 1.1.1) Let X(t; x) be a real-valued function of the real variables t and x, with domain D ⊆ R2. A function x(t), with t in some open interval I ⊆ R, which satisfies dx (1.2) x0(t) = = X(t; x(t)) dt is said to be a solution satisfying x0. In other words, x(t) is only a solution if (t; x(t)) ⊆ D for each t 2 I. We take I to be the largest interval for which x(t) satisfies (1.2). -
An Appreciation of Euler's Formula
Rose-Hulman Undergraduate Mathematics Journal Volume 18 Issue 1 Article 17 An Appreciation of Euler's Formula Caleb Larson North Dakota State University Follow this and additional works at: https://scholar.rose-hulman.edu/rhumj Recommended Citation Larson, Caleb (2017) "An Appreciation of Euler's Formula," Rose-Hulman Undergraduate Mathematics Journal: Vol. 18 : Iss. 1 , Article 17. Available at: https://scholar.rose-hulman.edu/rhumj/vol18/iss1/17 Rose- Hulman Undergraduate Mathematics Journal an appreciation of euler's formula Caleb Larson a Volume 18, No. 1, Spring 2017 Sponsored by Rose-Hulman Institute of Technology Department of Mathematics Terre Haute, IN 47803 [email protected] a scholar.rose-hulman.edu/rhumj North Dakota State University Rose-Hulman Undergraduate Mathematics Journal Volume 18, No. 1, Spring 2017 an appreciation of euler's formula Caleb Larson Abstract. For many mathematicians, a certain characteristic about an area of mathematics will lure him/her to study that area further. That characteristic might be an interesting conclusion, an intricate implication, or an appreciation of the impact that the area has upon mathematics. The particular area that we will be exploring is Euler's Formula, eix = cos x + i sin x, and as a result, Euler's Identity, eiπ + 1 = 0. Throughout this paper, we will develop an appreciation for Euler's Formula as it combines the seemingly unrelated exponential functions, imaginary numbers, and trigonometric functions into a single formula. To appreciate and further understand Euler's Formula, we will give attention to the individual aspects of the formula, and develop the necessary tools to prove it. -
Generalized Finite-Difference Schemes
Generalized Finite-Difference Schemes By Blair Swartz* and Burton Wendroff** Abstract. Finite-difference schemes for initial boundary-value problems for partial differential equations lead to systems of equations which must be solved at each time step. Other methods also lead to systems of equations. We call a method a generalized finite-difference scheme if the matrix of coefficients of the system is sparse. Galerkin's method, using a local basis, provides unconditionally stable, implicit generalized finite-difference schemes for a large class of linear and nonlinear problems. The equations can be generated by computer program. The schemes will, in general, be not more efficient than standard finite-difference schemes when such standard stable schemes exist. We exhibit a generalized finite-difference scheme for Burgers' equation and solve it with a step function for initial data. | 1. Well-Posed Problems and Semibounded Operators. We consider a system of partial differential equations of the following form: (1.1) du/dt = Au + f, where u = (iti(a;, t), ■ ■ -, umix, t)),f = ifxix, t), ■ • -,fmix, t)), and A is a matrix of partial differential operators in x — ixx, • • -, xn), A = Aix,t,D) = JjüiD', D<= id/dXx)h--Yd/dxn)in, aiix, t) = a,-,...i„(a;, t) = matrix . Equation (1.1) is assumed to hold in some n-dimensional region Í2 with boundary An initial condition is imposed in ti, (1.2) uix, 0) = uoix) , xEV. The boundary conditions are that there is a collection (B of operators Bix, D) such that (1.3) Bix,D)u = 0, xEdti,B<E(2>. We assume the operator A satisfies the following condition : There exists a scalar product {,) such that for all sufficiently smooth functions <¡>ix)which satisfy (1.3), (1.4) 2 Re {A4,,<t>) ^ C(<b,<p), 0 < t ^ T , where C is a constant independent of <p.An operator A satisfying (1.4) is called semi- bounded. -
An Introduction to Mathematical Modelling
An Introduction to Mathematical Modelling Glenn Marion, Bioinformatics and Statistics Scotland Given 2008 by Daniel Lawson and Glenn Marion 2008 Contents 1 Introduction 1 1.1 Whatismathematicalmodelling?. .......... 1 1.2 Whatobjectivescanmodellingachieve? . ............ 1 1.3 Classificationsofmodels . ......... 1 1.4 Stagesofmodelling............................... ....... 2 2 Building models 4 2.1 Gettingstarted .................................. ...... 4 2.2 Systemsanalysis ................................. ...... 4 2.2.1 Makingassumptions ............................. .... 4 2.2.2 Flowdiagrams .................................. 6 2.3 Choosingmathematicalequations. ........... 7 2.3.1 Equationsfromtheliterature . ........ 7 2.3.2 Analogiesfromphysics. ...... 8 2.3.3 Dataexploration ............................... .... 8 2.4 Solvingequations................................ ....... 9 2.4.1 Analytically.................................. .... 9 2.4.2 Numerically................................... 10 3 Studying models 12 3.1 Dimensionlessform............................... ....... 12 3.2 Asymptoticbehaviour ............................. ....... 12 3.3 Sensitivityanalysis . ......... 14 3.4 Modellingmodeloutput . ....... 16 4 Testing models 18 4.1 Testingtheassumptions . ........ 18 4.2 Modelstructure.................................. ...... 18 i 4.3 Predictionofpreviouslyunuseddata . ............ 18 4.3.1 Reasonsforpredictionerrors . ........ 20 4.4 Estimatingmodelparameters . ......... 20 4.5 Comparingtwomodelsforthesamesystem . ......... -
Alicia Dickenstein
A WORLD OF BINOMIALS ——————————– ALICIA DICKENSTEIN Universidad de Buenos Aires FOCM 2008, Hong Kong - June 21, 2008 A. Dickenstein - FoCM 2008, Hong Kong – p.1/39 ORIGINAL PLAN OF THE TALK BASICS ON BINOMIALS A. Dickenstein - FoCM 2008, Hong Kong – p.2/39 ORIGINAL PLAN OF THE TALK BASICS ON BINOMIALS COUNTING SOLUTIONS TO BINOMIAL SYSTEMS A. Dickenstein - FoCM 2008, Hong Kong – p.2/39 ORIGINAL PLAN OF THE TALK BASICS ON BINOMIALS COUNTING SOLUTIONS TO BINOMIAL SYSTEMS DISCRIMINANTS (DUALS OF BINOMIAL VARIETIES) A. Dickenstein - FoCM 2008, Hong Kong – p.2/39 ORIGINAL PLAN OF THE TALK BASICS ON BINOMIALS COUNTING SOLUTIONS TO BINOMIAL SYSTEMS DISCRIMINANTS (DUALS OF BINOMIAL VARIETIES) BINOMIALS AND RECURRENCE OF HYPERGEOMETRIC COEFFICIENTS A. Dickenstein - FoCM 2008, Hong Kong – p.2/39 ORIGINAL PLAN OF THE TALK BASICS ON BINOMIALS COUNTING SOLUTIONS TO BINOMIAL SYSTEMS DISCRIMINANTS (DUALS OF BINOMIAL VARIETIES) BINOMIALS AND RECURRENCE OF HYPERGEOMETRIC COEFFICIENTS BINOMIALS AND MASS ACTION KINETICS DYNAMICS A. Dickenstein - FoCM 2008, Hong Kong – p.2/39 RESCHEDULED PLAN OF THE TALK BASICS ON BINOMIALS - How binomials “sit” in the polynomial world and the main “secrets” about binomials A. Dickenstein - FoCM 2008, Hong Kong – p.3/39 RESCHEDULED PLAN OF THE TALK BASICS ON BINOMIALS - How binomials “sit” in the polynomial world and the main “secrets” about binomials COUNTING SOLUTIONS TO BINOMIAL SYSTEMS - with a touch of complexity A. Dickenstein - FoCM 2008, Hong Kong – p.3/39 RESCHEDULED PLAN OF THE TALK BASICS ON BINOMIALS - How binomials “sit” in the polynomial world and the main “secrets” about binomials COUNTING SOLUTIONS TO BINOMIAL SYSTEMS - with a touch of complexity (Mixed) DISCRIMINANTS (DUALS OF BINOMIAL VARIETIES) and an application to real roots A. -
Example 10.8 Testing the Steady-State Approximation. ⊕ a B C C a B C P + → → + → D Dt K K K K K K [ ]
Example 10.8 Testing the steady-state approximation. Å The steady-state approximation contains an apparent contradiction: we set the time derivative of the concentration of some species (a reaction intermediate) equal to zero — implying that it is a constant — and then derive a formula showing how it changes with time. Actually, there is no contradiction since all that is required it that the rate of change of the "steady" species be small compared to the rate of reaction (as measured by the rate of disappearance of the reactant or appearance of the product). But exactly when (in a practical sense) is this approximation appropriate? It is often applied as a matter of convenience and justified ex post facto — that is, if the resulting rate law fits the data then the approximation is considered justified. But as this example demonstrates, such reasoning is dangerous and possible erroneous. We examine the mechanism A + B ¾¾1® C C ¾¾2® A + B (10.12) 3 C® P (Note that the second reaction is the reverse of the first, so we have a reversible second-order reaction followed by an irreversible first-order reaction.) The rate constants are k1 for the forward reaction of the first step, k2 for the reverse of the first step, and k3 for the second step. This mechanism is readily solved for with the steady-state approximation to give d[A] k1k3 = - ke[A][B] with ke = (10.13) dt k2 + k3 (TEXT Eq. (10.38)). With initial concentrations of A and B equal, hence [A] = [B] for all times, this equation integrates to 1 1 = + ket (10.14) [A] A0 where A0 is the initial concentration (equal to 1 in work to follow). -
Newton and Leibniz: the Development of Calculus Isaac Newton (1642-1727)
Newton and Leibniz: The development of calculus Isaac Newton (1642-1727) Isaac Newton was born on Christmas day in 1642, the same year that Galileo died. This coincidence seemed to be symbolic and in many ways, Newton developed both mathematics and physics from where Galileo had left off. A few months before his birth, his father died and his mother had remarried and Isaac was raised by his grandmother. His uncle recognized Newton’s mathematical abilities and suggested he enroll in Trinity College in Cambridge. Newton at Trinity College At Trinity, Newton keenly studied Euclid, Descartes, Kepler, Galileo, Viete and Wallis. He wrote later to Robert Hooke, “If I have seen farther, it is because I have stood on the shoulders of giants.” Shortly after he received his Bachelor’s degree in 1665, Cambridge University was closed due to the bubonic plague and so he went to his grandmother’s house where he dived deep into his mathematics and physics without interruption. During this time, he made four major discoveries: (a) the binomial theorem; (b) calculus ; (c) the law of universal gravitation and (d) the nature of light. The binomial theorem, as we discussed, was of course known to the Chinese, the Indians, and was re-discovered by Blaise Pascal. But Newton’s innovation is to discuss it for fractional powers. The binomial theorem Newton’s notation in many places is a bit clumsy and he would write his version of the binomial theorem as: In modern notation, the left hand side is (P+PQ)m/n and the first term on the right hand side is Pm/n and the other terms are: The binomial theorem as a Taylor series What we see here is the Taylor series expansion of the function (1+Q)m/n. -
Mathematics (MATH)
Course Descriptions MATH 1080 QL MATH 1210 QL Mathematics (MATH) Precalculus Calculus I 5 5 MATH 100R * Prerequisite(s): Within the past two years, * Prerequisite(s): One of the following within Math Leap one of the following: MAT 1000 or MAT 1010 the past two years: (MATH 1050 or MATH 1 with a grade of B or better or an appropriate 1055) and MATH 1060, each with a grade of Is part of UVU’s math placement process; for math placement score. C or higher; OR MATH 1080 with a grade of C students who desire to review math topics Is an accelerated version of MATH 1050 or higher; OR appropriate placement by math in order to improve placement level before and MATH 1060. Includes functions and placement test. beginning a math course. Addresses unique their graphs including polynomial, rational, Covers limits, continuity, differentiation, strengths and weaknesses of students, by exponential, logarithmic, trigonometric, and applications of differentiation, integration, providing group problem solving activities along inverse trigonometric functions. Covers and applications of integration, including with an individual assessment and study inequalities, systems of linear and derivatives and integrals of polynomial plan for mastering target material. Requires nonlinear equations, matrices, determinants, functions, rational functions, exponential mandatory class attendance and a minimum arithmetic and geometric sequences, the functions, logarithmic functions, trigonometric number of hours per week logged into a Binomial Theorem, the unit circle, right functions, inverse trigonometric functions, and preparation module, with progress monitored triangle trigonometry, trigonometric equations, hyperbolic functions. Is a prerequisite for by a mentor. May be repeated for a maximum trigonometric identities, the Law of Sines, the calculus-based sciences.