Mobile Robotics, Mathematics, Models, and Methods

Size: px
Start display at page:

Download "Mobile Robotics, Mathematics, Models, and Methods"

Transcription

1 Mobile Robotics, Mathematics, Models, and Methods Errata in Initial Revision Prepared by Professor Alonzo Kelly Rev 10, June 0, 015 Locations in the first column are given in several formats: 1 x,y,z means page x, para y, line z x, Fig yz means figure yz on page x 3 x, Eq yz means Equation yz on page x 4 etc Generally, errors in cross references will make it more difficult to follow derivations With a few exceptions, errors in math (both in-line and otherwise) are typically notational A few severe issues are noted in the comments Look for the word severe If your time is limited, I suggest that you use a pencil in your text to fix the severe issues and the cross reference errors Everything else is minor able 1: ypo and In-line Math Edits 8, 8,5 n RA n RA is is Add space 113,6, we have: we have the matrix: clarify quantity is a matrix 118,8,3 Let the set of solutions to the above constraint be parameterized locally by a parameter s to produce the surface Each element of the vector level curve is a 119,1,1 We can conclude that the gradient g x 1467,1, composite vector of unknowns x xs xs Let the set of solutions to the above constraint be parameterized locally in one direction by a parameter s to produce the curve xs xs is called a level curve We can conclude that the gradient g x composite vector of unknowns x his discussion is confusing when applied to n dimensions remove underline move delta symbol

2 able 1: ypo and In-line Math Edits 151 Form the matrix (math font) V whose rows are two arbitrary vectors (math font, underlined) v 1 and (math font, underlined) v in the planeuse constrained optimization to show that the unit vector that maximizes the sum of its squared projections onto both vectors is an eigenvector of (math font) V V 176, 7,1 Consider next two frames of reference that are rotating with respect to each one another 19,4,1 411 Wheel Wheel Steering Control 195,,1 Box 4 WMR Forward Kinematics: Offset Wheels V V Form the matrix whose rows are two arbitrary vectors v 1 and v in the plane Use constrained optimization to show that the unit vector that maximizes the sum of its squared projections onto both vectors is an eigenvector of V Consider next two frames of reference that are rotating with respect to one another 411 Wheel Steering Control Box 4 WMR Forward and Inverse Kinematics: Offset Wheels remove format spec, take V down fromfinal superscript remove word each delete extra word add words 13,8,3 projection that occurs at line 11 projection that occurs at line 1 17,3,8 the eigenvectors associated with the eigenvectors associated with the n 1 largest eigenvalues the n 1 largest eigenvalues of of C C C C move second C down from superscript 17,4, However, later content However, earlier content 4,6,1 he state vector is xt and xt yt t xt he state vector is and xt yt t change theta to psi 51,3,3 counteract the increasing tendency increasing tendency to roll over counteract the increasing tendency to roll over delete double phrase 61,3,1 using the above Jacobian using the Jacobian delete word 6,3,1 the parameters may only be obtainable p the parameters obtainable may only be change letter p to rho 68, Papers see comment move Raol and Wong up to Books

3 able 1: ypo and In-line Math Edits 68, 7 Proceedings of International symposium on Reading Review, 011 Proceedings of International Symposium on Robotics Research, ,3 Spot 51 he exponent is called the Mahalanobis distance (MHD) he exponent x C 1 x x C 1 x is called the (squared) Mahalanobis distance (MHD) add word squared 78,1, when the exponent (the squared Mahalanobis distance) is a constant 90,4, in terms of it evolution over time; 308, 7 It is not possible to invert this relationship to determine R from xx 309 he weighted least squares solution from Equation 579 for H 11 and when the exponent (the squared Mahalanobis distance) is a constant in terms of its evolution over time; It is not possible to invert this relationship to determine R from xx Also note that: xˆ * xx H R 1 z In other words, the solution covariance appears in the formula for the solution itself he weighted least squares solution from Equation 579 for H' 11 and add word squared change it to its Add sentence to clarify add primes to H and R R diag z x is R' diag z x is 310,, Now, suppose that another measurement z 6 was produced at the same time as the first with variance z ,11,5 By substituting Equation 585 into Equation 580, we immediately get: Now, suppose that another measurement z 6 was produced at the same time as the first with variance z 15 We already know that the covariance of the MLE estimate is the first matrix in the solution herefore, from Equation 585 and Equation 584, we can immediately write: Change variance subscript to z Add sentence and reuse earlier result

4 able 1: ypo and In-line Math Edits 313, 11,1 Subsequent measurements have covariances of R 3 diag 1 01 and Subsequent measurements have covariances of R 4 diag R diag 1 01 R 4 diag 01 1 and Change 01 to 1 35,4,3 he complement of the linearized filter is an system dynamics model he complement of the linearized filter is a system dynamics model Change an to a 331,1,1 Let be the position vector of the wheel relative to the body frame and let denote the angular velocity which, is assumed Let be the position vector of the wheel relative to the body frame and let denote the angular velocity, which is assumed move comma 346,3,5 the propo- did not We will denote by sition that event that occur A A the propo- did not We will denote by sition that event occur A A delete second that 353, 5, Bayes, filter [17] Bayes filter [17] remove comma 361,,3 (Figure 548) Figure 548 remove brackets 375, 3,3 However, there may no solution However, there may be no solution add word be 76,, imagine placing the vehicle at the origin imagine placing the vehicle on the hyperbola at y0 401, measure the angle measures the angle add s 401,3,1 Inexpensive devices have bandwidths on the order or Hz 401,3, a rebalance loop that prevent the sensor 403,4,1 neither constant nor entirely a real force Inexpensive devices have bandwidths on the order of Hz a rebalance loop that prevents the sensor neither constant nor entirely associated with a real force or -> of add s add two words 407,5, is fixed with a lasing medium is filled with a lasing medium fixed->filled 414,6, but the above assumes a sequence zyx but the above assumes a sequence zxy 419,3,3 n n Note that R v and R v are very n n Note that v and R v are very different fonts different matrices different matrices

5 able 1: ypo and In-line Math Edits 43,5,6 three to five meter accuracy hree to five meter accuracy capitalize 438,3,1 he trajectory control level considers and entire trajectory 453,1 Provide the details of the transfer function derivation for the cascade controller shown in Figure 714 he trajectory control level considers an entire trajectory Provide the details of the transfer function s (before assuming K vi 0 ) derivation for the cascade controller shown in Figure 714 Its tricky and->an 469,4,6 and an entire terminal state and an entire terminal state xs f xs f x f y f f f x f y f f f add brackets 484,5,1 Much of the forth coming discussion 491,3,1 Of course, there are situations, such as operation in cluttered environments where Much of the forthcoming discussion Of course, there are situations, such as operation in cluttered environments, where join words add comma 503,,3 Most sensors measure anyway Most sensors measure surfaces anyway add word surfaces 503,4, Sampled versus Continuum 503,4,6 In such a representation computational complexity Sampled versus Continuum In such a representation, computational complexity remove space add comma 515,1,1 Although states estimation Although state estimation states->state 530,7, similarity measures 9 similarity measures [9] add [] 536,5,3 same color it is not possible same color, it is not possible add comma 539,4,4 defined by the Bessel function Figure ,5, In the latter case the corners can be very small defined by the Bessel function (Figure 86) In the latter case, the corners can be very small add () add comma 560,5,5 Hence, a camara that is sensitive Hence, a camera that is sensitive camara->camera 56,4,1 identical basic principles to those or sonar identical basic principles to those of sonar or->of 569,3, Section 1310 of Chapter 5 Section of Chapter 5 570,4,4 optics are also relative inexpensive optics are also relatively inexpensive relative->relatively

6 able 1: ypo and In-line Math Edits 69,7,1 Conversely, the residual Conversely, the residual zx b cx can be zx b gx can be added added c -> g 635,4,1 Let the measurement project onto a a few states 637, 3 to locate the landmark Figure ,5 Soundness Feasible Admissible Completeness If any If not Optimality Let the measurement project onto a few states to locate the landmark (Figure 948) Soundness Feasible Admissible Completeness If any If not Optimality delete redundant word add () Indent nd,3rd,4th,5th bullets 651, 3, set of roads and vehicles set of roads, and vehicles 655,5,1 to encode arbitrary gradient to encode arbitrary strategies gradient->strategies 659,1, described in terms of a traversal of a tree that restrict traversal is discovered during the search process described in terms of a traversal of a tree that is discovered during the search process delete restrict traversal 66,,1 the g values nodes the g values 664,,1 is com- he cost estimate puted fx is com- he cost estimate puted fx add space 666,3,6 total costs any new nodes added to OPEN 668,6,6 order to process child nodes is unknown means this total costs of any new nodes added to OPEN order to process child nodes is unknown, means this insert of add comma 669, 8,4 In difficult terrain the details In difficult terrain, the details add comma 671,4,3 (only when headed) (only when needed) headed->needed 679,5,5 Node costs refer to the estimated total cost Node costs refer to the estimated total cost estimate->estimated

7 able : Math and Algorithm Edits 40 Add p subscript f x p + f f x p + f p p x f x p p x f x p 77,Eq severe error, replace diagonal with zeros 95, Eq 96 R1 R B B replace superscript B R BP 1 R B 1 R1 R B1 B R BP 1 R B 1 with B1 97,Eq 97 c k i s k i i i j i j i s k c jak s jbk + a j c k i s j i ak j i j i + c jbk +b j c k i s k i i i j i j i s k c jak s jbk + a j c k i s j i ak j i j i + c jbk +b j move column separators right 14, Eq 37 remove letters mp subject to:c'mpx 0 subject to: cx 0 17, Eq 31 1 no underline on c fx --x Q xx x b 1 + x+ c fx --x Q xx x+ b x+ c 163, Eq 3119 reciprocal k p ---- t k p ---- s r c t 199, Eq 46 s1 s1 severe error, change r c1 d s 1 c 1 r c1 d s 1 c 1 sign of all y coordinates s3 s3 r c3 d s 3 c 3 r c3 c 3 s4 r c4 d s c d s 4 c 4 s r c s4 r c4 d s 3 d s c d s 4 c 4 08,1 severe error, add minus i v v 00cx i + sy i v v i 00cx i + sy i sign to 3rd expression only 10, Eq 494 c x s c L c x s c Lc s c L s c Lc change subscript to x

8 able : Math and Algorithm Edits 4, Eq 4111 v v v w switch right super and v z v z subscript, leave left alone cx 0 cx x x 0 y w y v 7,7 change k+1 to k ---- y t k + 1 y y k + k y t k + 1 y y k + k 7,8 change k+1 to k t y k ---- t + u k + 1 y k + 1 y k + 1 y k u k 1 + y k 4, Eq 415 change theta to psi c t s c t s everywhere s t c s t c 4, 7,1 change theta to psi 00 sv t x t + cv t y t 00 sv t x t + cv t y t everywhere (including 00 cv t x t sv t y t 00 cv t x t sv t y t inline lines above) , 8,1 change theta to psi c t s c t s everywhere s t c s t c 43, Eq 4154 change theta to psi c t s t yt c t s t yt everywhere s t c t xt s t c t xt 43, Eq 4155 change theta to psi c t s t yt c t s t yt everywhere (just like line above) s t c t xt s t c t xt 63,7 Vt cost Vt sint t c 0 s Vt cost Vt sint t,, c 0 s change theta to psi everywhere

9 able : Math and Algorithm Edits 64,1 change theta to psi c c c c everywhere s 1 W s 1 W s 1 W s 1 W 64, change theta to psi v r + v l c v r + v l c everywhere v r + v l s v r + v l s v r v l W v r v l W 65, Eq 41 x t Fx t + Gu t x t Fx t + Gu t missing dot (time derivative) 74, 1 sigma is not inside 1 px x exp square root px x exp 98,10 change k subscript to k- k 1 k k k k 1 k k k k 1 k k k ,3,3 delete first col of nd x i s i x i l i s i matrix c i l + i y i s i l i c i 304,7 add transpose Expt xt 0 xt 0 t Expt xt 0 xt 0 tt y i + l i c i 311,1, he measurements relationship is: z x z H I x' x he measurement relationship is: H I x' Remove s at end of word measurements Add second equality 311, Eq 584 Add primes to H and R x' H R 1 H 1 H R 1 z x' H' R' H' H' R' z 38, Eq 5111 Change theta to psi x x everywhere (3rd col) y y and thetadot to psidot everywhere (3rd row) v v

10 able : Math and Algorithm Edits 38, Eq 511 Change subscripts of last two elements to k+1 k + 1 v k k k + 1 v k + 1 k , Eq 68 change theta to psi x y 380, Writing total differentials: Writing total differentials: change theta to psi x y + + F F G G F F G G 0 391,6 change theta to psi xx 0 xy 0 x 0 xx 0 xy 0 x 0 xy 0 yy 0 y 0 x 0 y 0 0 xy 0 yy 0 y 0 x 0 y ,4 W W W W W O severe, superscript W - S *S O S *S O > O 601,4 601,5 argmin * argmin f 1 --r Zr Z f argmax * argmax f zpred Z f z obs 1 --r Zr Z change * z obs zpred Z change * to to 608, argmin * argmin change to * and Z f 1 --r Zr Z f 1 --r Xr X to X 69, Eq 943 c -> g cx b gx b

11 able : Math and Algorithm Edits 66, algorithm expand- NodeDijkstra, line , algorithm expand- NodeAstar, line 05 gnew xg gnew x next g else if ( ) else if ( ) x -> xnext, severe bug if( x next O && fnew xf ) if( x next O && fnew x next f ) x -> xnext, severe bug 665, algorithm expand- NodeAstar, line 08 else else if( x next C && fnew xf ) if( x next C && fnew x next f ) x -> xnext, severe bug able 3: Cross Reference Edits 41,1, Figure 7 Figure 8 139,4, section Figure 314 section 315 Figure -> Section 314-> ,1 Solve Equation 3104 Solve Equation > 5 170,6,1 Substituting this into Equation 3139 above Substituting this into Equation 3140 above 180, 1, (Box 41) Equation 411, Equation 41, and Equation 413 Equation 41, Equation 413, and Equation ,4 Rewrite Equation 440 Rewrite Equation , 6,1 Equation 491 Equation ,10,1 Algorithm 4 Algorithm 41 4,3 Based on Equation 474 Based on Equation > 5, 3 constraint from Figure 46 constraint from Figure > 17 8,1,1 Equation 4114 Equation ,, he transform of our first-order system (Equation 4113) he transform of our first-order system (Equation 4114) 58,1 Section 3 of Chapter 3 Section 33 of Chapter 3 clarify reference 58, 5 the functional in Equation 31 in Chapter 3 the functional in Equation 3133 in Chapter 3 64,, 1 Equation 414 Equation 416

12 able 3: Cross Reference Edits 65,6, Jacobian of the left-hand side of Equation 41 Jacobian of the left-hand side of Equation 4 96,8,3 Equation 558 Equation ,4,3 Equation 561 Equation ,3, Equation 571 Equation ,5,1 Equation 569 Equation ,, Equation 4138 Equation ,3, Equation 580 Equation ,7,1 he result of Equation 587 he result of Equation ,10, It can be derived by returning to Equation 583 and including the above 350,6,4 the computation of the denominator in Equation 514 requires that 361,5,1 Recall Equation 5151 and Equation 515 It can be derived by returning to Equation 584 and including the above the computation of the denominator in Equation 5143 requires that Recall Equation 5150 and Equation Section 456 of Chapter 4 Section 4566 of Chapter 4 more precise xref 415,,3 based on Equation 76 in Chapter 415,3,1 Equivalently, based on Equation 13 in Chapter based on Equation 81 in Chapter Equivalently, based on Equation 133 in Chapter 44,7 first two lines of Equation 657 first two lines of Equation ,3,5 Figure 68 illustrates the equivalent Figure 63 illustrates the equivalent 596,1,1 then we can use Equation 55 then we can use Equation ,3,1 For the camera, we can use Equation 81 For the camera, we can use Equation 8 615,6,3 Figure 538 Figure ,4,1 his is of the form of equation Equation ,1,3 varies along the line of the laser (Figure 948) his is of the form of Equation 937 varies along the line of the laser (Figure 949) delete redundant word

13 able 4: Figure Edits 65, Fig as drawn see comment x,y axes should not be partially erased 416, Fig 630 i i change n sub/sup script n 001 i i e 001 to e n i n c 0 s e i e c 0 s 48, Fig 633 caption two antenna two antennae 503,Fig 744 as drawn see comment increase contrast in left image 598, Fig 916 as drawn see comment increase contrast in right figure to see highlighted fork holes 618, Fig 935 caption Line are approximately 1 pixel wide Lines are approximately 1 pixel wide line -> lines 61, Fig 939 as drawn see comment increase contrast to see feature traces 663, Fig 1019 as drawn see comment increase contrast to see left image

Elementary Statistics for Geographers, 3 rd Edition

Elementary Statistics for Geographers, 3 rd Edition Errata Elementary Statistics for Geographers, 3 rd Edition Chapter 1 p. 31: 1 st paragraph: 1 st line: 20 should be 22 Chapter 2 p. 41: Example 2-1: 1 st paragraph: last line: Chapters 2, 3, and 4 and

More information

Errata for Vector and Geometric Calculus Printings 1-4

Errata for Vector and Geometric Calculus Printings 1-4 October 21, 2017 Errata for Vector and Geometric Calculus Printings 1-4 Note: p. m (n) refers to page m of Printing 4 and page n of Printings 1-3. p. 31 (29), just before Theorem 3.10. f x(h) = [f x][h]

More information

Corrections and Minor Revisions of Mathematical Methods in the Physical Sciences, third edition, by Mary L. Boas (deceased)

Corrections and Minor Revisions of Mathematical Methods in the Physical Sciences, third edition, by Mary L. Boas (deceased) Corrections and Minor Revisions of Mathematical Methods in the Physical Sciences, third edition, by Mary L. Boas (deceased) Updated December 6, 2017 by Harold P. Boas This list includes all errors known

More information

Robot Control Basics CS 685

Robot Control Basics CS 685 Robot Control Basics CS 685 Control basics Use some concepts from control theory to understand and learn how to control robots Control Theory general field studies control and understanding of behavior

More information

1 Introduction. Systems 2: Simulating Errors. Mobile Robot Systems. System Under. Environment

1 Introduction. Systems 2: Simulating Errors. Mobile Robot Systems. System Under. Environment Systems 2: Simulating Errors Introduction Simulating errors is a great way to test you calibration algorithms, your real-time identification algorithms, and your estimation algorithms. Conceptually, the

More information

Errata and suggested changes for Understanding Advanced Statistical Methods, by Westfall and Henning

Errata and suggested changes for Understanding Advanced Statistical Methods, by Westfall and Henning Errata and suggested changes for Understanding Advanced Statistical Methods, by Westfall and Henning Special thanks to Robert Jordan and Arkapravo Sarkar for suggesting and/or catching many of these. p.

More information

LB 220 Homework 4 Solutions

LB 220 Homework 4 Solutions LB 220 Homework 4 Solutions Section 11.4, # 40: This problem was solved in class on Feb. 03. Section 11.4, # 42: This problem was also solved in class on Feb. 03. Section 11.4, # 43: Also solved in class

More information

2. page 13*, righthand column, last line of text, change 3 to 2,... negative slope of 2 for 1 < t 2: Now... REFLECTOR MITTER. (0, d t ) (d r, d t )

2. page 13*, righthand column, last line of text, change 3 to 2,... negative slope of 2 for 1 < t 2: Now... REFLECTOR MITTER. (0, d t ) (d r, d t ) SP First ERRATA. These are mostly typos, but there are a few crucial mistakes in formulas. Underline is not used in the book, so I ve used it to denote changes. JHMcClellan, February 3, 00. page 0*, Figure

More information

[POLS 8500] Review of Linear Algebra, Probability and Information Theory

[POLS 8500] Review of Linear Algebra, Probability and Information Theory [POLS 8500] Review of Linear Algebra, Probability and Information Theory Professor Jason Anastasopoulos ljanastas@uga.edu January 12, 2017 For today... Basic linear algebra. Basic probability. Programming

More information

L03. PROBABILITY REVIEW II COVARIANCE PROJECTION. NA568 Mobile Robotics: Methods & Algorithms

L03. PROBABILITY REVIEW II COVARIANCE PROJECTION. NA568 Mobile Robotics: Methods & Algorithms L03. PROBABILITY REVIEW II COVARIANCE PROJECTION NA568 Mobile Robotics: Methods & Algorithms Today s Agenda State Representation and Uncertainty Multivariate Gaussian Covariance Projection Probabilistic

More information

Chapter 7 Control. Part State Space Control. Mobile Robotics - Prof Alonzo Kelly, CMU RI

Chapter 7 Control. Part State Space Control. Mobile Robotics - Prof Alonzo Kelly, CMU RI Chapter 7 Control Part 2 7.2 State Space Control 1 7.2 State Space Control Outline 7.2.1 Introduction 7.2.2 State Space Feedback Control 7.2.3 Example: Robot Trajectory Following 7.2.4 Perception Based

More information

Lagrange Multipliers

Lagrange Multipliers Optimization with Constraints As long as algebra and geometry have been separated, their progress have been slow and their uses limited; but when these two sciences have been united, they have lent each

More information

Lagrange Multipliers

Lagrange Multipliers Lagrange Multipliers (Com S 477/577 Notes) Yan-Bin Jia Nov 9, 2017 1 Introduction We turn now to the study of minimization with constraints. More specifically, we will tackle the following problem: minimize

More information

Electrical Engineering Written PhD Qualifier Exam Spring 2014

Electrical Engineering Written PhD Qualifier Exam Spring 2014 Electrical Engineering Written PhD Qualifier Exam Spring 2014 Friday, February 7 th 2014 Please do not write your name on this page or any other page you submit with your work. Instead use the student

More information

Math Precalculus I University of Hawai i at Mānoa Spring

Math Precalculus I University of Hawai i at Mānoa Spring Math 135 - Precalculus I University of Hawai i at Mānoa Spring - 2014 Created for Math 135, Spring 2008 by Lukasz Grabarek and Michael Joyce Send comments and corrections to lukasz@math.hawaii.edu Contents

More information

1 Kalman Filter Introduction

1 Kalman Filter Introduction 1 Kalman Filter Introduction You should first read Chapter 1 of Stochastic models, estimation, and control: Volume 1 by Peter S. Maybec (available here). 1.1 Explanation of Equations (1-3) and (1-4) Equation

More information

Additional corrections to Introduction to Tensor Analysis and the Calculus of Moving Surfaces, Part I.

Additional corrections to Introduction to Tensor Analysis and the Calculus of Moving Surfaces, Part I. Additional corrections to Introduction to Tensor Analysis and the Calculus of Moving Surfaces, Part I. 1) Page 42, equation (4.44): All A s in denominator should be a. 2) Page 72, equation (5.84): The

More information

Course Number 432/433 Title Algebra II (A & B) H Grade # of Days 120

Course Number 432/433 Title Algebra II (A & B) H Grade # of Days 120 Whitman-Hanson Regional High School provides all students with a high- quality education in order to develop reflective, concerned citizens and contributing members of the global community. Course Number

More information

Chapter 0 of Calculus ++, Differential calculus with several variables

Chapter 0 of Calculus ++, Differential calculus with several variables Chapter of Calculus ++, Differential calculus with several variables Background material by Eric A Carlen Professor of Mathematics Georgia Tech Spring 6 c 6 by the author, all rights reserved - Table of

More information

Chapter 3 Numerical Methods

Chapter 3 Numerical Methods Chapter 3 Numerical Methods Part 3 3.4 Differential Algebraic Systems 3.5 Integration of Differential Equations 1 Outline 3.4 Differential Algebraic Systems 3.4.1 Constrained Dynamics 3.4.2 First and Second

More information

Vlad Estivill-Castro. Robots for People --- A project for intelligent integrated systems

Vlad Estivill-Castro. Robots for People --- A project for intelligent integrated systems 1 Vlad Estivill-Castro Robots for People --- A project for intelligent integrated systems V. Estivill-Castro 2 Probabilistic Map-based Localization (Kalman Filter) Chapter 5 (textbook) Based on textbook

More information

MATH 4211/6211 Optimization Constrained Optimization

MATH 4211/6211 Optimization Constrained Optimization MATH 4211/6211 Optimization Constrained Optimization Xiaojing Ye Department of Mathematics & Statistics Georgia State University Xiaojing Ye, Math & Stat, Georgia State University 0 Constrained optimization

More information

Reading Mathematical Expressions & Arithmetic Operations Expression Reads Note

Reading Mathematical Expressions & Arithmetic Operations Expression Reads Note Math 001 - Term 171 Reading Mathematical Expressions & Arithmetic Operations Expression Reads Note x A x belongs to A,x is in A Between an element and a set. A B A is a subset of B Between two sets. φ

More information

Chapter 2 Math Fundamentals

Chapter 2 Math Fundamentals Chapter 2 Math Fundamentals Part 1 2.1 Conventions and Definitions 2.2 Matrices 2.3 Fundamentals of Rigid Transforms 1 Outline 2.1 Conventions and Definitions 2.2 Matrices 2.3 Fundamentals of Rigid Transforms

More information

Math 291-2: Final Exam Solutions Northwestern University, Winter 2016

Math 291-2: Final Exam Solutions Northwestern University, Winter 2016 Math 29-2: Final Exam Solutions Northwestern University, Winter 206 Determine whether each of the following statements is true or false f it is true, explain why; if it is false, give a counterexample

More information

Exercises for Multivariable Differential Calculus XM521

Exercises for Multivariable Differential Calculus XM521 This document lists all the exercises for XM521. The Type I (True/False) exercises will be given, and should be answered, online immediately following each lecture. The Type III exercises are to be done

More information

154 Chapter 9 Hints, Answers, and Solutions The particular trajectories are highlighted in the phase portraits below.

154 Chapter 9 Hints, Answers, and Solutions The particular trajectories are highlighted in the phase portraits below. 54 Chapter 9 Hints, Answers, and Solutions 9. The Phase Plane 9.. 4. The particular trajectories are highlighted in the phase portraits below... 3. 4. 9..5. Shown below is one possibility with x(t) and

More information

MATH 31 - ADDITIONAL PRACTICE PROBLEMS FOR FINAL

MATH 31 - ADDITIONAL PRACTICE PROBLEMS FOR FINAL MATH 3 - ADDITIONAL PRACTICE PROBLEMS FOR FINAL MAIN TOPICS FOR THE FINAL EXAM:. Vectors. Dot product. Cross product. Geometric applications. 2. Row reduction. Null space, column space, row space, left

More information

2015 School Year. Graduate School Entrance Examination Problem Booklet. Mathematics

2015 School Year. Graduate School Entrance Examination Problem Booklet. Mathematics 2015 School Year Graduate School Entrance Examination Problem Booklet Mathematics Examination Time: 10:00 to 12:30 Instructions 1. Do not open this problem booklet until the start of the examination is

More information

PHYS 705: Classical Mechanics. Rigid Body Motion Introduction + Math Review

PHYS 705: Classical Mechanics. Rigid Body Motion Introduction + Math Review 1 PHYS 705: Classical Mechanics Rigid Body Motion Introduction + Math Review 2 How to describe a rigid body? Rigid Body - a system of point particles fixed in space i r ij j subject to a holonomic constraint:

More information

Robotics I. February 6, 2014

Robotics I. February 6, 2014 Robotics I February 6, 214 Exercise 1 A pan-tilt 1 camera sensor, such as the commercial webcams in Fig. 1, is mounted on the fixed base of a robot manipulator and is used for pointing at a (point-wise)

More information

Math 118, Fall 2014 Final Exam

Math 118, Fall 2014 Final Exam Math 8, Fall 4 Final Exam True or false Please circle your choice; no explanation is necessary True There is a linear transformation T such that T e ) = e and T e ) = e Solution Since T is linear, if T

More information

Typos/errors in Numerical Methods Using Matlab, 4th edition by Mathews and Fink

Typos/errors in Numerical Methods Using Matlab, 4th edition by Mathews and Fink MAT487 Fall 2004 David Hiebeler University of Maine http://www.math.umaine.edu/faculty/hiebeler Typos/errors in Numerical Methods Using Matlab, 4th edition by Mathews and Fink Please let me know if you

More information

Mobile Robotics 1. A Compact Course on Linear Algebra. Giorgio Grisetti

Mobile Robotics 1. A Compact Course on Linear Algebra. Giorgio Grisetti Mobile Robotics 1 A Compact Course on Linear Algebra Giorgio Grisetti SA-1 Vectors Arrays of numbers They represent a point in a n dimensional space 2 Vectors: Scalar Product Scalar-Vector Product Changes

More information

Errata for the book A First Course in Numerical Methods, by Uri Ascher and Chen Greif

Errata for the book A First Course in Numerical Methods, by Uri Ascher and Chen Greif Errata for the book A First Course in Numerical Methods, Uri Ascher and Chen Greif February 8, 016 In this file we have collected various changes to be made to the first edition of our book. Several have

More information

Diagonalization. P. Danziger. u B = A 1. B u S.

Diagonalization. P. Danziger. u B = A 1. B u S. 7., 8., 8.2 Diagonalization P. Danziger Change of Basis Given a basis of R n, B {v,..., v n }, we have seen that the matrix whose columns consist of these vectors can be thought of as a change of basis

More information

EKF and SLAM. McGill COMP 765 Sept 18 th, 2017

EKF and SLAM. McGill COMP 765 Sept 18 th, 2017 EKF and SLAM McGill COMP 765 Sept 18 th, 2017 Outline News and information Instructions for paper presentations Continue on Kalman filter: EKF and extension to mapping Example of a real mapping system:

More information

GPS Geodesy - LAB 7. Neglecting the propagation, multipath, and receiver errors, eq.(1) becomes:

GPS Geodesy - LAB 7. Neglecting the propagation, multipath, and receiver errors, eq.(1) becomes: GPS Geodesy - LAB 7 GPS pseudorange position solution The pseudorange measurements j R i can be modeled as: j R i = j ρ i + c( j δ δ i + ΔI + ΔT + MP + ε (1 t = time of epoch j R i = pseudorange measurement

More information

Extremal Trajectories for Bounded Velocity Differential Drive Robots

Extremal Trajectories for Bounded Velocity Differential Drive Robots Extremal Trajectories for Bounded Velocity Differential Drive Robots Devin J. Balkcom Matthew T. Mason Robotics Institute and Computer Science Department Carnegie Mellon University Pittsburgh PA 523 Abstract

More information

REVIEW OF DIFFERENTIAL CALCULUS

REVIEW OF DIFFERENTIAL CALCULUS REVIEW OF DIFFERENTIAL CALCULUS DONU ARAPURA 1. Limits and continuity To simplify the statements, we will often stick to two variables, but everything holds with any number of variables. Let f(x, y) be

More information

Math Precalculus I University of Hawai i at Mānoa Spring

Math Precalculus I University of Hawai i at Mānoa Spring Math 135 - Precalculus I University of Hawai i at Mānoa Spring - 2013 Created for Math 135, Spring 2008 by Lukasz Grabarek and Michael Joyce Send comments and corrections to lukasz@math.hawaii.edu Contents

More information

Calculus Volume 1 Release Notes 2018

Calculus Volume 1 Release Notes 2018 Calculus Volume 1 Release Notes 2018 Publish Date: March 16, 2018 Revision Number: C1-2016-003(03/18)-MJ Page Count Difference: In the latest edition of Calculus Volume 1, there are 873 pages compared

More information

Errata for First Printing of Numerical Methods with Matlab: Implementations and Applications

Errata for First Printing of Numerical Methods with Matlab: Implementations and Applications Errata for First Printing of Numerical Methods with Matlab: Implementations and Applications Gerald Recktenwald gerry@me.pdx.edu February 14, 2005 This document lists only the technical errors in the mathematics

More information

Statistics 992 Continuous-time Markov Chains Spring 2004

Statistics 992 Continuous-time Markov Chains Spring 2004 Summary Continuous-time finite-state-space Markov chains are stochastic processes that are widely used to model the process of nucleotide substitution. This chapter aims to present much of the mathematics

More information

8. Diagonalization.

8. Diagonalization. 8. Diagonalization 8.1. Matrix Representations of Linear Transformations Matrix of A Linear Operator with Respect to A Basis We know that every linear transformation T: R n R m has an associated standard

More information

Pattern Recognition Prof. P. S. Sastry Department of Electronics and Communication Engineering Indian Institute of Science, Bangalore

Pattern Recognition Prof. P. S. Sastry Department of Electronics and Communication Engineering Indian Institute of Science, Bangalore Pattern Recognition Prof. P. S. Sastry Department of Electronics and Communication Engineering Indian Institute of Science, Bangalore Lecture - 27 Multilayer Feedforward Neural networks with Sigmoidal

More information

Section 2.4: Add and Subtract Rational Expressions

Section 2.4: Add and Subtract Rational Expressions CHAPTER Section.: Add and Subtract Rational Expressions Section.: Add and Subtract Rational Expressions Objective: Add and subtract rational expressions with like and different denominators. You will recall

More information

Review (probability, linear algebra) CE-717 : Machine Learning Sharif University of Technology

Review (probability, linear algebra) CE-717 : Machine Learning Sharif University of Technology Review (probability, linear algebra) CE-717 : Machine Learning Sharif University of Technology M. Soleymani Fall 2012 Some slides have been adopted from Prof. H.R. Rabiee s and also Prof. R. Gutierrez-Osuna

More information

Linear Algebra (MATH ) Spring 2011 Final Exam Practice Problem Solutions

Linear Algebra (MATH ) Spring 2011 Final Exam Practice Problem Solutions Linear Algebra (MATH 4) Spring 2 Final Exam Practice Problem Solutions Instructions: Try the following on your own, then use the book and notes where you need help. Afterwards, check your solutions with

More information

Math 263 Assignment #4 Solutions. 0 = f 1 (x,y,z) = 2x 1 0 = f 2 (x,y,z) = z 2 0 = f 3 (x,y,z) = y 1

Math 263 Assignment #4 Solutions. 0 = f 1 (x,y,z) = 2x 1 0 = f 2 (x,y,z) = z 2 0 = f 3 (x,y,z) = y 1 Math 263 Assignment #4 Solutions 1. Find and classify the critical points of each of the following functions: (a) f(x,y,z) = x 2 + yz x 2y z + 7 (c) f(x,y) = e x2 y 2 (1 e x2 ) (b) f(x,y) = (x + y) 3 (x

More information

3.7 Constrained Optimization and Lagrange Multipliers

3.7 Constrained Optimization and Lagrange Multipliers 3.7 Constrained Optimization and Lagrange Multipliers 71 3.7 Constrained Optimization and Lagrange Multipliers Overview: Constrained optimization problems can sometimes be solved using the methods of the

More information

Relativistic Electrodynamics

Relativistic Electrodynamics Relativistic Electrodynamics Notes (I will try to update if typos are found) June 1, 2009 1 Dot products The Pythagorean theorem says that distances are given by With time as a fourth direction, we find

More information

Vector Calculus, Maths II

Vector Calculus, Maths II Section A Vector Calculus, Maths II REVISION (VECTORS) 1. Position vector of a point P(x, y, z) is given as + y and its magnitude by 2. The scalar components of a vector are its direction ratios, and represent

More information

Phase space, Tangent-Linear and Adjoint Models, Singular Vectors, Lyapunov Vectors and Normal Modes

Phase space, Tangent-Linear and Adjoint Models, Singular Vectors, Lyapunov Vectors and Normal Modes Phase space, Tangent-Linear and Adjoint Models, Singular Vectors, Lyapunov Vectors and Normal Modes Assume a phase space of dimension N where Autonomous governing equations with initial state: = is a state

More information

Chapter 1: Systems of linear equations and matrices. Section 1.1: Introduction to systems of linear equations

Chapter 1: Systems of linear equations and matrices. Section 1.1: Introduction to systems of linear equations Chapter 1: Systems of linear equations and matrices Section 1.1: Introduction to systems of linear equations Definition: A linear equation in n variables can be expressed in the form a 1 x 1 + a 2 x 2

More information

Math 0320 Final Exam Review

Math 0320 Final Exam Review Math 0320 Final Exam Review SHORT ANSWER. Write the word or phrase that best completes each statement or answers the question. Factor out the GCF using the Distributive Property. 1) 6x 3 + 9x 1) Objective:

More information

Linear Algebra Review. Fei-Fei Li

Linear Algebra Review. Fei-Fei Li Linear Algebra Review Fei-Fei Li 1 / 37 Vectors Vectors and matrices are just collections of ordered numbers that represent something: movements in space, scaling factors, pixel brightnesses, etc. A vector

More information

ERRATA AND SUGGESTION SHEETS Advanced Calculus, Second Edition

ERRATA AND SUGGESTION SHEETS Advanced Calculus, Second Edition ERRATA AND SUGGESTION SHEETS Advanced Calculus, Second Edition Prof. Patrick Fitzpatrick February 6, 2013 Page 11, line 38: b 2 < r should be b 2 < c Page 13, line 1: for... number a and b, write... numbers

More information

Sensors Fusion for Mobile Robotics localization. M. De Cecco - Robotics Perception and Action

Sensors Fusion for Mobile Robotics localization. M. De Cecco - Robotics Perception and Action Sensors Fusion for Mobile Robotics localization 1 Until now we ve presented the main principles and features of incremental and absolute (environment referred localization systems) could you summarize

More information

UNIT 4 NOTES: PROPERTIES & EXPRESSIONS

UNIT 4 NOTES: PROPERTIES & EXPRESSIONS UNIT 4 NOTES: PROPERTIES & EXPRESSIONS Vocabulary Mathematics: (from Greek mathema, knowledge, study, learning ) Is the study of quantity, structure, space, and change. Algebra: Is the branch of mathematics

More information

Assignment 1 Math 5341 Linear Algebra Review. Give complete answers to each of the following questions. Show all of your work.

Assignment 1 Math 5341 Linear Algebra Review. Give complete answers to each of the following questions. Show all of your work. Assignment 1 Math 5341 Linear Algebra Review Give complete answers to each of the following questions Show all of your work Note: You might struggle with some of these questions, either because it has

More information

Definition: A binary relation R from a set A to a set B is a subset R A B. Example:

Definition: A binary relation R from a set A to a set B is a subset R A B. Example: Chapter 9 1 Binary Relations Definition: A binary relation R from a set A to a set B is a subset R A B. Example: Let A = {0,1,2} and B = {a,b} {(0, a), (0, b), (1,a), (2, b)} is a relation from A to B.

More information

MOL410/510 Problem Set 1 - Linear Algebra - Due Friday Sept. 30

MOL410/510 Problem Set 1 - Linear Algebra - Due Friday Sept. 30 MOL40/50 Problem Set - Linear Algebra - Due Friday Sept. 30 Use lab notes to help solve these problems. Problems marked MUST DO are required for full credit. For the remainder of the problems, do as many

More information

Math 0031, Final Exam Study Guide December 7, 2015

Math 0031, Final Exam Study Guide December 7, 2015 Math 0031, Final Exam Study Guide December 7, 2015 Chapter 1. Equations of a line: (a) Standard Form: A y + B x = C. (b) Point-slope Form: y y 0 = m (x x 0 ), where m is the slope and (x 0, y 0 ) is a

More information

Answers to Sample Exam Problems

Answers to Sample Exam Problems Math Answers to Sample Exam Problems () Find the absolute value, reciprocal, opposite of a if a = 9; a = ; Absolute value: 9 = 9; = ; Reciprocal: 9 ; ; Opposite: 9; () Commutative law; Associative law;

More information

Robotics. Mobile Robotics. Marc Toussaint U Stuttgart

Robotics. Mobile Robotics. Marc Toussaint U Stuttgart Robotics Mobile Robotics State estimation, Bayes filter, odometry, particle filter, Kalman filter, SLAM, joint Bayes filter, EKF SLAM, particle SLAM, graph-based SLAM Marc Toussaint U Stuttgart DARPA Grand

More information

In the derivation of Optimal Interpolation, we found the optimal weight matrix W that minimizes the total analysis error variance.

In the derivation of Optimal Interpolation, we found the optimal weight matrix W that minimizes the total analysis error variance. hree-dimensional variational assimilation (3D-Var) In the derivation of Optimal Interpolation, we found the optimal weight matrix W that minimizes the total analysis error variance. Lorenc (1986) showed

More information

Jordan normal form notes (version date: 11/21/07)

Jordan normal form notes (version date: 11/21/07) Jordan normal form notes (version date: /2/7) If A has an eigenbasis {u,, u n }, ie a basis made up of eigenvectors, so that Au j = λ j u j, then A is diagonal with respect to that basis To see this, let

More information

Linear Algebra March 16, 2019

Linear Algebra March 16, 2019 Linear Algebra March 16, 2019 2 Contents 0.1 Notation................................ 4 1 Systems of linear equations, and matrices 5 1.1 Systems of linear equations..................... 5 1.2 Augmented

More information

Optimal Control and Estimation MAE 546, Princeton University Robert Stengel, Preliminaries!

Optimal Control and Estimation MAE 546, Princeton University Robert Stengel, Preliminaries! Optimal Control and Estimation MAE 546, Princeton University Robert Stengel, 2018 Copyright 2018 by Robert Stengel. All rights reserved. For educational use only. http://www.princeton.edu/~stengel/mae546.html

More information

Chain Rule. MATH 311, Calculus III. J. Robert Buchanan. Spring Department of Mathematics

Chain Rule. MATH 311, Calculus III. J. Robert Buchanan. Spring Department of Mathematics 3.33pt Chain Rule MATH 311, Calculus III J. Robert Buchanan Department of Mathematics Spring 2019 Single Variable Chain Rule Suppose y = g(x) and z = f (y) then dz dx = d (f (g(x))) dx = f (g(x))g (x)

More information

Errata for Robot Vision

Errata for Robot Vision Errata for Robot Vision This is a list of known nontrivial bugs in Robot Vision 1986) by B.K.P. Horn, MIT Press, Cambridge, MA ISBN 0-6-08159-8 and McGraw-Hill, New York, NY ISBN 0-07-030349-5. Thanks

More information

TI89 Titanium Exercises - Part 10.

TI89 Titanium Exercises - Part 10. TI89 Titanium Exercises - Part. Entering matrices directly in the HOME screen s entry line ) Enter your nxm matrix using this format: [[ a, a 2,,a m ][a 2, a 22, a 2m ] [a n, a n2,, a nm ]] This is a matrix

More information

FFTs in Graphics and Vision. The Laplace Operator

FFTs in Graphics and Vision. The Laplace Operator FFTs in Graphics and Vision The Laplace Operator 1 Outline Math Stuff Symmetric/Hermitian Matrices Lagrange Multipliers Diagonalizing Symmetric Matrices The Laplacian Operator 2 Linear Operators Definition:

More information

Introduction to Mobile Robotics Compact Course on Linear Algebra. Wolfram Burgard, Bastian Steder

Introduction to Mobile Robotics Compact Course on Linear Algebra. Wolfram Burgard, Bastian Steder Introduction to Mobile Robotics Compact Course on Linear Algebra Wolfram Burgard, Bastian Steder Reference Book Thrun, Burgard, and Fox: Probabilistic Robotics Vectors Arrays of numbers Vectors represent

More information

What s an HMM? Extraction with Finite State Machines e.g. Hidden Markov Models (HMMs) Hidden Markov Models (HMMs) for Information Extraction

What s an HMM? Extraction with Finite State Machines e.g. Hidden Markov Models (HMMs) Hidden Markov Models (HMMs) for Information Extraction Hidden Markov Models (HMMs) for Information Extraction Daniel S. Weld CSE 454 Extraction with Finite State Machines e.g. Hidden Markov Models (HMMs) standard sequence model in genomics, speech, NLP, What

More information

1 Linear Regression and Correlation

1 Linear Regression and Correlation Math 10B with Professor Stankova Worksheet, Discussion #27; Tuesday, 5/1/2018 GSI name: Roy Zhao 1 Linear Regression and Correlation 1.1 Concepts 1. Often when given data points, we want to find the line

More information

Chapter 3 Numerical Methods

Chapter 3 Numerical Methods Chapter 3 Numerical Methods Part 2 3.2 Systems of Equations 3.3 Nonlinear and Constrained Optimization 1 Outline 3.2 Systems of Equations 3.3 Nonlinear and Constrained Optimization Summary 2 Outline 3.2

More information

A Introduction to Matrix Algebra and the Multivariate Normal Distribution

A Introduction to Matrix Algebra and the Multivariate Normal Distribution A Introduction to Matrix Algebra and the Multivariate Normal Distribution PRE 905: Multivariate Analysis Spring 2014 Lecture 6 PRE 905: Lecture 7 Matrix Algebra and the MVN Distribution Today s Class An

More information

Finite Math - J-term Section Systems of Linear Equations in Two Variables Example 1. Solve the system

Finite Math - J-term Section Systems of Linear Equations in Two Variables Example 1. Solve the system Finite Math - J-term 07 Lecture Notes - //07 Homework Section 4. - 9, 0, 5, 6, 9, 0,, 4, 6, 0, 50, 5, 54, 55, 56, 6, 65 Section 4. - Systems of Linear Equations in Two Variables Example. Solve the system

More information

Math 312 Final Exam Jerry L. Kazdan May 5, :00 2:00

Math 312 Final Exam Jerry L. Kazdan May 5, :00 2:00 Math 32 Final Exam Jerry L. Kazdan May, 204 2:00 2:00 Directions This exam has three parts. Part A has shorter questions, (6 points each), Part B has 6 True/False questions ( points each), and Part C has

More information

Errata for Instructor s Solutions Manual for Gravity, An Introduction to Einstein s General Relativity 1st printing

Errata for Instructor s Solutions Manual for Gravity, An Introduction to Einstein s General Relativity 1st printing Errata for Instructor s Solutions Manual for Gravity, An Introduction to Einstein s General Relativity st printing Updated 7/7/003 (hanks to Scott Fraser who provided almost all of these.) Statement of

More information

Section Summary. Relations and Functions Properties of Relations. Combining Relations

Section Summary. Relations and Functions Properties of Relations. Combining Relations Chapter 9 Chapter Summary Relations and Their Properties n-ary Relations and Their Applications (not currently included in overheads) Representing Relations Closures of Relations (not currently included

More information

Math 110: Worksheet 1 Solutions

Math 110: Worksheet 1 Solutions Math 110: Worksheet 1 Solutions August 30 Thursday Aug. 4 1. Determine whether or not the following sets form vector spaces over the given fields. (a) The set V of all matrices of the form where a, b R,

More information

Nonlinear Optimization

Nonlinear Optimization Nonlinear Optimization (Com S 477/577 Notes) Yan-Bin Jia Nov 7, 2017 1 Introduction Given a single function f that depends on one or more independent variable, we want to find the values of those variables

More information

1 Solutions to selected problems

1 Solutions to selected problems Solutions to selected problems Section., #a,c,d. a. p x = n for i = n : 0 p x = xp x + i end b. z = x, y = x for i = : n y = y + x i z = zy end c. y = (t x ), p t = a for i = : n y = y(t x i ) p t = p

More information

Homework For each of the following matrices, find the minimal polynomial and determine whether the matrix is diagonalizable.

Homework For each of the following matrices, find the minimal polynomial and determine whether the matrix is diagonalizable. Math 5327 Fall 2018 Homework 7 1. For each of the following matrices, find the minimal polynomial and determine whether the matrix is diagonalizable. 3 1 0 (a) A = 1 2 0 1 1 0 x 3 1 0 Solution: 1 x 2 0

More information

ERRATA Discrete-Time Signal Processing, 3e A. V. Oppenheim and R. W. Schafer

ERRATA Discrete-Time Signal Processing, 3e A. V. Oppenheim and R. W. Schafer ERRATA Discrete-Time Signal Processing, 3e A. V. Oppenheim and R. W. Schafer The following were corrected in the second printing; i.e., they are errors found only in the first printing. p.181 In the third

More information

11 a 12 a 21 a 11 a 22 a 12 a 21. (C.11) A = The determinant of a product of two matrices is given by AB = A B 1 1 = (C.13) and similarly.

11 a 12 a 21 a 11 a 22 a 12 a 21. (C.11) A = The determinant of a product of two matrices is given by AB = A B 1 1 = (C.13) and similarly. C PROPERTIES OF MATRICES 697 to whether the permutation i 1 i 2 i N is even or odd, respectively Note that I =1 Thus, for a 2 2 matrix, the determinant takes the form A = a 11 a 12 = a a 21 a 11 a 22 a

More information

1 Functions of Several Variables 2019 v2

1 Functions of Several Variables 2019 v2 1 Functions of Several Variables 2019 v2 11 Notation The subject of this course is the study of functions f : R n R m The elements of R n, for n 2, will be called vectors so, if m > 1, f will be said to

More information

A primer on matrices

A primer on matrices A primer on matrices Stephen Boyd August 4, 2007 These notes describe the notation of matrices, the mechanics of matrix manipulation, and how to use matrices to formulate and solve sets of simultaneous

More information

Lecture 5: Likelihood ratio tests, Neyman-Pearson detectors, ROC curves, and sufficient statistics. 1 Executive summary

Lecture 5: Likelihood ratio tests, Neyman-Pearson detectors, ROC curves, and sufficient statistics. 1 Executive summary ECE 830 Spring 207 Instructor: R. Willett Lecture 5: Likelihood ratio tests, Neyman-Pearson detectors, ROC curves, and sufficient statistics Executive summary In the last lecture we saw that the likelihood

More information

1 Bayesian Linear Regression (BLR)

1 Bayesian Linear Regression (BLR) Statistical Techniques in Robotics (STR, S15) Lecture#10 (Wednesday, February 11) Lecturer: Byron Boots Gaussian Properties, Bayesian Linear Regression 1 Bayesian Linear Regression (BLR) In linear regression,

More information

Lecture 6 Positive Definite Matrices

Lecture 6 Positive Definite Matrices Linear Algebra Lecture 6 Positive Definite Matrices Prof. Chun-Hung Liu Dept. of Electrical and Computer Engineering National Chiao Tung University Spring 2017 2017/6/8 Lecture 6: Positive Definite Matrices

More information

Introduction to Matrix Algebra and the Multivariate Normal Distribution

Introduction to Matrix Algebra and the Multivariate Normal Distribution Introduction to Matrix Algebra and the Multivariate Normal Distribution Introduction to Structural Equation Modeling Lecture #2 January 18, 2012 ERSH 8750: Lecture 2 Motivation for Learning the Multivariate

More information

10-725/36-725: Convex Optimization Prerequisite Topics

10-725/36-725: Convex Optimization Prerequisite Topics 10-725/36-725: Convex Optimization Prerequisite Topics February 3, 2015 This is meant to be a brief, informal refresher of some topics that will form building blocks in this course. The content of the

More information

Bayesian Linear Regression [DRAFT - In Progress]

Bayesian Linear Regression [DRAFT - In Progress] Bayesian Linear Regression [DRAFT - In Progress] David S. Rosenberg Abstract Here we develop some basics of Bayesian linear regression. Most of the calculations for this document come from the basic theory

More information

Gaussian Models (9/9/13)

Gaussian Models (9/9/13) STA561: Probabilistic machine learning Gaussian Models (9/9/13) Lecturer: Barbara Engelhardt Scribes: Xi He, Jiangwei Pan, Ali Razeen, Animesh Srivastava 1 Multivariate Normal Distribution The multivariate

More information

Activity 8: Eigenvectors and Linear Transformations

Activity 8: Eigenvectors and Linear Transformations Activity 8: Eigenvectors and Linear Transformations MATH 20, Fall 2010 November 1, 2010 Names: In this activity, we will explore the effects of matrix diagonalization on the associated linear transformation.

More information

Trust Regions. Charles J. Geyer. March 27, 2013

Trust Regions. Charles J. Geyer. March 27, 2013 Trust Regions Charles J. Geyer March 27, 2013 1 Trust Region Theory We follow Nocedal and Wright (1999, Chapter 4), using their notation. Fletcher (1987, Section 5.1) discusses the same algorithm, but

More information