Tom Duff Computer Division Lucasfilm Ltd. Technical Memo No Dec 1983
|
|
- Milo Bridges
- 5 years ago
- Views:
Transcription
1 Famles of Local Matrx Splnes Tom Duff Computer Dvson Lucasflm Ltd Techncal Memo No 04 Dec 983 Presented as tutoral notes (by Alvy Ray Smth) at the 983 SIGGRAPH, July 983 Ths document was reentered n Mcrosoft Word on Dec 999 Spellng and punctuaton are generally preserved, but trvally mnor spellng errors are corrected Otherwse addtons or changes made to the orgnal are noted nsde square brackets or n footnotes Abstract Jm Clark's Cardnal splnes are a famly of local nterpolatng splnes wth an adjustable tenson parameter The famly may be descrbed by a matrx whch yelds splne coeffcents as lnear functons of knot values We characterze the Cardnal splne matrx n a way whch suggests a method of addng tenson to B- splnes, and show that ths tenson corresponds to the β parameter of Bran Barsky's β -splnes The smlarty between the tenson parameters of these two splnes suggests lookng for an nterpolatng splne famly whch ncorporates a bas parameter analogous to Barsky's β We demonstrate two such famles wth dfferent contnuty propertes (one s G, the other C ) Fnally, we develop a 0 fve-parameter characterzaton of all C, G translaton nvarant cubc matrx splnes and ndcate that all the famles we have developed are sub-famles of t CR Categores and Subject Descrptors: G [Numercal Analyss] Splne and pecewse polynomal nterpolaton, G [Numercal Analyss] Splne and pecewse polynomal approxmaton, I35 [Computer Graphcs] Curve, surface, sold and object representatons General Terms: Algorthms Addtonal Keywords and Phrases: β -splne, Cardnal splne, matrx splne, local splne, computer aded geometrc desgn Introducton A local splne s one for whch changng the value of a sngle knot affects only a bounded number of splne segments n the knot's vcnty Ths s a partcularly useful property for geometrc desgn systems and computer anmaton systems, snce t means that a desgner can adjust the appearance of a partcular part of hs desgn wthout fear of global complcatons Many popular local splnes may be characterzed by a system of lnear equatons relatng the splne's knots to ts polynomal coeffcents These lnear equatons may be wrtten as a rectangular matrx
2 Dec 83 Matrx Splnes The Matrx Splne Notaton Gven a sequence of control ponts or knots, K 0 < m and an n + by s matrx M we can defne the matrx splne S M of degree n and support s by n s n j M jk + k j= 0 k= 0 S ( t + ) = t M K 0 t <,0 < m s+ We wll usually omt the subscrpt M when the matrx s clear from context, and we wll use the notaton S () t = S ( + t) In computer graphcs we are partcularly concerned wth the case n = 3, s = 4, whch makes M one of the 4 by 4 matrces so famlar to computer graphcs hardware and software In ths case the above equaton may be rewrtten as K 3 K + SM ( t + ) = t t t M 0 t <,0 < m 3 K + K + 3 Most (but not all, see for example [Knuth]) of the well-known local splnes may be expressed n ths form For example, the unform cubc B-splne [Resenfeld] s represented by M Bsplne = Wthout loss of confuson [sc], we wll dentfy matrces wth ther splnes Thus, when we speak of the dervatve of M, we wll mean the dervatve of the splne whose matrx s M The frst use of the matrx splne notaton of whch I am aware s [Catmull] Famles of Splnes Bran Barsky ntroduced a two-parameter famly of matrx splnes called the β -splnes (see [Barsky]) The β -splnes are contnuous and have contnuous unt tangent and curvature vectors In partcular, the β -splnes satsfy S() = S+ (0) β S () = S (0) + βs () + βs () = S + (0) Barsky calls the last two of these condtons G and G They are geometrc generalzatons of the parametrc contnuty condtons C and C The β -splne ma- trx s Here () the rght S s the dervatve from the left and (0) S + s the dervatve at the same pont from Tech Memo 04 Pxar
3 Dec 83 Matrx Splnes β ( β+ β + β + β) ( β+ β+ β+ ) 3 3 6β 3( β + β + β) 3β + 6β 0 M Beta = β + β+ 4β+ 4 β+ 6β 6( β β) 6β 0 3 β β+ 4β + 4β 0 β bases or slews the curve to the left or rght (parametrcally) of the unbased B- splne As β ncreases, the curve becomes more tense and more closely approxmates ts knots Another useful famly of splnes s the Cardnal splnes [Clark] The Cardnal splnes are nterpolatng splnes wth frst dervatve contnuty They are defned by the constrants S (0) = K + S() = K+ S (0) = ck ( K ) + S () = ck ( + 3 K+ ) Solvng these equatons for the splne coeffcents yelds the matrx c c c c c c 3 3 c c c 0 c Usng the notaton lerp( M, N, α) = ( α) M + αn (lerp s an abbrevaton for lnear nterpolant), we can rewrte ths as lerp( MEase, Mc=, c) where M Ease =, M c = = () Thus, each of the Cardnal splnes s a weghted average of the c = splne, whch has S (0) = K+ K and S () = K+ 3 K+, and the Ease splne, whch has S (0) = S () = 0 3 The Cardnal splnes may be thought of as varable-tenson local nterpolatng splnes whch take ther corners more sharply as c 0 An nterestng member of the Cardnal splne famly has c = 5 Ths s the Catmull-Rom cubc splne, descrbed n [Catmull-Rom] and later n [Brewer-Anderson] Equaton () mmedately suggests a method of adjustng the tenson of B- splnes usng Note that lerpng two splne matrces s equvalent to lerpng the coeffcents of the polynomals they generate, whch n turn s equvalent to lerpng the ponts on the splne curves and ther dervatves 3 The Ease splne curve s the polygon that nterpolates ts knots Its velocty decelerates to zero as t passes through each knot Thus, ts moton as a functon of t `eases' n and out of each knot The term ease s taken from the lexcon of conventonal (not computer) anmators Tech Memo 04 Pxar
4 Dec 83 Matrx Splnes 4 M M M () TenseBsplne = lerp( Ease, Bsplne, ) = My exctement at dscoverng ths famly of splnes was tempered by Alvy Ray Smth's revelaton (about an hour later) [Smth] that t s the subfamly of the β - splnes wth β = and β = ( )/ The man advantage to my formulaton s that vares over a ncer range than β In partcular 0 β < corresponds to > 0 The case = 0, whch yelds the Ease splne (the only nterpolatng splne of the famly) s unattanable n the β -splnes The substtuton of for β can be generalzed to other values of β If we 3 substtute β = ( β + β + β+ )( )/ and β = /( + ) nto the β - splne matrx, we get: ( + ) ( + ) ( ) lerp( M, Ease, 3 ) 6 3 3( + + ) 3 ( + ) ) ( + ) 0 Thus, we see that for any fxed β (equvalently ), β (eqv ) has the effect of pckng a matrx (and therefore a curve) that s some weghted average of the Ease splne and the β = 0 (eqv = ) splne, n the same manner that the Cardnal splnes are derved from the c = splne Interpolatng Splnes wth Tenson Control The analogy between the Cardnal splnes and the β parameter of the β - splnes suggests lookng for a generalzaton of the Cardnal splnes that ncorporates a parameter analogous to β There at least two ways of dong ths β expresses the rato of the lengths of a β -splne's dervatve vectors as we approach a knot from ether sde Therefore let us consder the two nterpolatng splnes whose frst dervatves match the c = splne's at one end, and are zero at the other end These have the matrces M SkewLeft = and M SkewRght = M SkewLeft has zero dervatve at the left end, whle M SkewRghtt has zero dervatve at the rght end Thus, Tech Memo 04 Pxar
5 Dec 83 Matrx Splnes 5 lerp( M, M, τ ) (3) SkewLeft SkewRght s a G famly of nterpolatng splnes When τ = 5, formula (3) yelds M CatmullRom Lerpng ths formula wth the Ease matrx we get ττ ( τ ) τ + ττ ( τ) τ ττ ( τ) τ 3 3 ττ ( τ ) τ lerp( M Ease,lerp( M SkewLeft, M SkewRght, τ), τ) = (4) ττ 0 ττ Ths s a two parameter famly of G nterpolatng splnes wth varable tenson and bas, whch we wll call the τ -splnes Instead of applyng the β -splne bas concept drectly to the Cardnal splnes, we could note that the Catmull-Rom cubc splne s defned by S (0) = K + S() = K+ S (0) = lerp( K K, K K, δ ) S () = lerp( K+ K+, K+ 3 K+, δ) where δ = 5 When δ = 0 and δ = the matrces satsfyng (5) are M Left = and 0 0 M Rght = respectvely Varyng δ from 0 to causes the splne's dervatve at K to vary from K + K to K+ K+, causng the curve to slew to the left or the rght The bas that δ causes s C rather than G Lerpng the δ famly of splnes wth the Ease splne produces a two parameter famly of C nterpolatng splnes wth varable tenson and bas The matrx for ths famly, whch we wll call the δ - splnes, s ( δ ) δ δδ ( δ) δ δδ ( δ) δ (3δ ) δ 3 3 δ δδ lerp( M Ease,lerp( M Left, M Rght, δ), δ) = (6) ( δ ) δ ( δ) δ δδ A Fve-Parameter Famly of G Local Splnes For a gven cubc polynomal, S() t s unquely specfed f we know S (0), S (), S (0), and S () Each of these s a lnear combnaton of the coeffcents of (5) Tech Memo 04 Pxar
6 Dec 83 Matrx Splnes 6 S () t, and therefore s a lnear combnaton of the knots If we restrct our attenton to those matrx splnes whch are C, G and translaton nvarant, how are 0 our choces for S (0), S (), S (0), and S () restrcted? By translaton nvarant, we mean that addng some constant D to each of K has no effect on S() t other than to add D to t everywhere 4 That s K + D 3 K+ + D t t t M = S() t + D K+ + D K D 0 The C condton means that S+ (0) = S() Ths mples that S () cannot depend on K, snce K s not one of the knots of S () + t Therefore, S () and S (0) + may be expressed as ak+ + bk+ + ck + 3 Ths mples that S(0) = ak + bk+ + ck+ Translaton ndependence mples that ak ( + D) + bk ( + + D) + ck ( + + D) = ak + bk+ + ck+ + D Snce ths must be true ndependent of K, K +, and K +, t s true when K = K+ = K+ = 0, and therefore a+ b+ c= Smlarly, G mples that S (0) cannot depend on K + 3 and S () cannot depend on K, and therefore, for some d, e, f, and S (0) = ( )( dk + ek+ + fk+ ) S () = ( dk+ + ek+ + fk + 3) Translaton ndependence requres that S () not change when D s added to the knots Therefore ( dk ( + D) + ek ( + D) + f( K + D)) = ( dk + ek + fk ) and thus d + e+ f = 0 Therefore, we can substtute a = ι( σ) b = ισ to yeld c= a b= ι d = τδ ( ) + + e = τ( δ) f = d e= τδ S (0) = lerp( K,lerp( K, K, σ), ι) S() = lerp( K+,lerp( K+, K+ 3, σ), ι) (7) S (0) = ( )lerp(0,lerp( K K, K K, δ), τ) S () = lerp(0,lerp( K+ K+, K+ 3 K+, δ), τ) 0 It should be clear from the dervaton that ths famly ncludes all the C, G, translaton nvarant cubc matrx splnes The ntutve functons of the fve parameters are as follows: 4 Note that all matrx splnes are nvarant under scales and rotatons about the orgn Tech Memo 04 Pxar
7 Dec 83 Matrx Splnes 7 ι controls how close S() t comes to nterpolatng K + and K + When ι = 0, S(0) = K +, and S() = K + When ι =, S (0) and S () are ndependent of K + and K +, respectvely σ controls how much S (0) and S () slew parallel to the lnes ( K, K + ) and ( K+, K+ 3) When σ = 5, S (0) wll le symmetrcally between K and K +, as wll S () between K + and K + 3 controls the geometrcty of the splne When = 5, the splne wll be C For other values of, the splne wll be gven a kck n the drecton of ts tangent as t passes through S (0) τ controls the tenson on the splne When τ = 0, S () t goes to zero as the curve passes each knot As τ get larger, the curve gets less tense, passng ts knots at greater speed δ controls the drecton the curve heads as t passes each knot When δ = 0, S (0) heads left, parallel to the lne ( K, K + ) When δ =, S (0) heads rght, parallel to ( K+, K+ ) The matrx for ths famly of splnes, whch we wll call the M-splnes 5, s δτ+ δτ+ τ ισ + ι τ 3δτ δτ τ + ισ 4ι+ τ + 3δτ + δτ + τ + ισ + ι δτ ισ δτ δτ τ+ 3ισ 3ι+ τ 5δτ+ 4 δτ+ 3 τ 3ισ+ 6ι τ 3 4δτ δτ τ 3ισ 3ι+ 3 3ισ δτ τ( δ+ δ+ ) τ(δ δ + ) δτ( ) 0 ι( σ) ι ισ 0 All the splne famles we have dscussed above are subfamles of the M-splnes Our tense B-splne famly has σ = 5, ι= /3, = 5, δ = 5, and τ = The Cardnal splnes are the subfamly wth ι = 0, = 5, δ = 5, and τ = 4c (The value of σ s rrelevant when ι = 0 ) The τ -splnes are the class wth ι = 0, = τ, = 5, and τ = τ The δ -splnes have ι = 0, = 5, δ = δ, and τ = δ Examples Fgure shows several members of each of the splne famles developed n ths paper These pctures were drawn by a '50s Formca boomerang desgn system wrtten n Ideal [VanWyk] and Emacs Each of the llustratons uses the same set of sx knots and vares one of the parameters of the famly from 0 to n steps of /4 Curves drawn wth longer dashed lnes correspond to larger parameter values, wth the sold curve showng the parameter set to The upper left llustraton shows tensed B-splnes wth = 0(5) The upper rght shows Cardnal splnes wth c = 0(5) The lower left shows τ -splnes wth τ = 0(5) and τ = 5 The lower rght shows δ -splnes wth δ = 0(5) and δ = 5 The llustratons clearly show the effects of the tenson and bas parameters As or c approaches zero, the curves more closely approach the polygon connectng the knots As τ or vares from zero to one, the curves slew from left to rght Conclusons The orgnal observaton on whch ths work s based s that the Cardnal splne tenson parameter s equvalent to lerpng the c = and Ease splnes Ths 5 M s for Matrx, snce ths class ncludes most of the useful cubc matrx splnes Tech Memo 04 Pxar
8 Dec 83 Matrx Splnes 8 suggested an analogous method of applyng tenson to B-splnes, whch Alvy Ray Smth demonstrated was equvalent to the β parameter of Barsky's β - splnes Ths n turn suggested lookng for an extenson of the Cardnal splnes that have a bas parameter analogous to β There are two famles of nterpolatng splnes wth bas and tenson controls, one of them G (the τ -splnes) and the other C (the δ -splnes) We have constructed a fve-parameter G famly whch subsumes the β -, c-, τ -, and δ - splnes and whch exhausts the translaton nvarant G cubc matrx splnes All of ths work was made easy by the matrx splne notaton Lnear condtons on polynomals and ther dervatves are equvalent to correspondng condtons on ther coeffcents (because the dervatve s a lnear operator) Snce the coeffcents of a matrx splne are lnear combnatons of ts knots, these condtons are therefore equvalent to correspondng condtons on the matrx Ths equvalence makes the proofs of contnuty crtera trval (to the pont where I haven't bothered ncludng any n ths paper), and frees the magnaton n ts search for splnes wth nterestng and useful propertes Acknowledgements Ths work owes much to conversatons wth Alvy Ray Smth durng the sprng of 983 He dscovered the mappng β = ( )/ whch shows that my tense B-splnes are just a sub-class of the β -splnes He also encouraged the research by ncludng parts of t n hs notes for a tutoral on splnes gven at Sggraph '83 [Smth] Tom Porter's crtcal readng of multple drafts of ths paper aded the presentaton greatly Bblography [Barsky] Bran A Barsky, The Beta-Splne: A Local Representaton Based on Shape Parameters and Fundamental Geometrc Measures, PhD dssertaton, Department of Computer Scence, Unversty of Utah, Salt Lake Cty, Dec 98 [Brewer-Anderson] J A Brewer and D C Anderson, Vsual Interacton wth Overhauser Curves and Surfaces, Computer Graphcs, Vol, No (Sggraph '77 Proceedngs), Summer 977, 3-37 [Catmull] Edwn Catmull, A Subdvson Algorthm for Computer Dsplay of Curved Surfaces, PhD dssertaton, Department of Computer Scence, Unversty of Utah, Salt Lake Cty, Dec 974 [Catmull-Rom] Edwn Catmull and Raphael Rom, A Class of Local Interpolatng Splnes, Computer Aded Geometrc Desgn, edted by Robert E Barnhll and Rchard F Resenfeld, Academc Press, San Francsco, 974, [Clark] James H Clark, Parametrc Curves, Surfaces and Volumes n Computer Graphcs and Computer-Aded Geometrc Desgn, Techncal Report, Computer Systems Laboratory, Stanford Unversty, Palo Alto, Calforna, Nov 98 [Knuth] Donald E Knuth, Mathematcal Typography, Bulletn (New Seres) of the Amercan Mathematcal Socety, Mar 979, [Resenfeld] Rchard Resenfeld, Applcatons of B-splne Approxmaton to Geometrc Problems of Computer-Aded Desgn, Techncal Report UTEC-Csc-73-6, Department of Computer Scence, Unversty of Utah, Salt Lake Cty, Mar 973 Tech Memo 04 Pxar
9 Dec 83 Matrx Splnes 9 [Smth] Alvy Ray Smth, personal communcaton, Apr 983 [Smth] Alvy Ray Smth, Splne Tutoral Notes, Techncal Memo #77, Computer Graphcs Project, Lucasflm Ltd, Introducton to Computer Anmaton Tutoral Notes, Sggraph '83, Jul 983, [VanWyk] Chrstopher J Van Wyk, Ideal User's Manual, Computer Scence Techncal Report #03, Bell Laboratores, Murray Hll, NJ, Dec 7, 98 Fg Examples of Splne Famles Tech Memo 04 Pxar
Convexity preserving interpolation by splines of arbitrary degree
Computer Scence Journal of Moldova, vol.18, no.1(52), 2010 Convexty preservng nterpolaton by splnes of arbtrary degree Igor Verlan Abstract In the present paper an algorthm of C 2 nterpolaton of dscrete
More informationModeling curves. Graphs: y = ax+b, y = sin(x) Implicit ax + by + c = 0, x 2 +y 2 =r 2 Parametric:
Modelng curves Types of Curves Graphs: y = ax+b, y = sn(x) Implct ax + by + c = 0, x 2 +y 2 =r 2 Parametrc: x = ax + bxt x = cos t y = ay + byt y = snt Parametrc are the most common mplct are also used,
More informationUsing T.O.M to Estimate Parameter of distributions that have not Single Exponential Family
IOSR Journal of Mathematcs IOSR-JM) ISSN: 2278-5728. Volume 3, Issue 3 Sep-Oct. 202), PP 44-48 www.osrjournals.org Usng T.O.M to Estmate Parameter of dstrbutons that have not Sngle Exponental Famly Jubran
More informationDEGREE REDUCTION OF BÉZIER CURVES USING CONSTRAINED CHEBYSHEV POLYNOMIALS OF THE SECOND KIND
ANZIAM J. 45(003), 195 05 DEGREE REDUCTION OF BÉZIER CURVES USING CONSTRAINED CHEBYSHEV POLYNOMIALS OF THE SECOND KIND YOUNG JOON AHN 1 (Receved 3 August, 001; revsed 7 June, 00) Abstract In ths paper
More informationLeast squares cubic splines without B-splines S.K. Lucas
Least squares cubc splnes wthout B-splnes S.K. Lucas School of Mathematcs and Statstcs, Unversty of South Australa, Mawson Lakes SA 595 e-mal: stephen.lucas@unsa.edu.au Submtted to the Gazette of the Australan
More informationThe Quadratic Trigonometric Bézier Curve with Single Shape Parameter
J. Basc. Appl. Sc. Res., (3541-546, 01 01, TextRoad Publcaton ISSN 090-4304 Journal of Basc and Appled Scentfc Research www.textroad.com The Quadratc Trgonometrc Bézer Curve wth Sngle Shape Parameter Uzma
More informationMore metrics on cartesian products
More metrcs on cartesan products If (X, d ) are metrc spaces for 1 n, then n Secton II4 of the lecture notes we defned three metrcs on X whose underlyng topologes are the product topology The purpose of
More informationPolynomial Regression Models
LINEAR REGRESSION ANALYSIS MODULE XII Lecture - 6 Polynomal Regresson Models Dr. Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur Test of sgnfcance To test the sgnfcance
More informationMathematical Preparations
1 Introducton Mathematcal Preparatons The theory of relatvty was developed to explan experments whch studed the propagaton of electromagnetc radaton n movng coordnate systems. Wthn expermental error the
More informationLecture 12: Discrete Laplacian
Lecture 12: Dscrete Laplacan Scrbe: Tanye Lu Our goal s to come up wth a dscrete verson of Laplacan operator for trangulated surfaces, so that we can use t n practce to solve related problems We are mostly
More informationDifference Equations
Dfference Equatons c Jan Vrbk 1 Bascs Suppose a sequence of numbers, say a 0,a 1,a,a 3,... s defned by a certan general relatonshp between, say, three consecutve values of the sequence, e.g. a + +3a +1
More informationAppendix for Causal Interaction in Factorial Experiments: Application to Conjoint Analysis
A Appendx for Causal Interacton n Factoral Experments: Applcaton to Conjont Analyss Mathematcal Appendx: Proofs of Theorems A. Lemmas Below, we descrbe all the lemmas, whch are used to prove the man theorems
More informationAffine transformations and convexity
Affne transformatons and convexty The purpose of ths document s to prove some basc propertes of affne transformatons nvolvng convex sets. Here are a few onlne references for background nformaton: http://math.ucr.edu/
More informationTR/95 February Splines G. H. BEHFOROOZ* & N. PAPAMICHAEL
TR/9 February 980 End Condtons for Interpolatory Quntc Splnes by G. H. BEHFOROOZ* & N. PAPAMICHAEL *Present address: Dept of Matematcs Unversty of Tabrz Tabrz Iran. W9609 A B S T R A C T Accurate end condtons
More informationInner Product. Euclidean Space. Orthonormal Basis. Orthogonal
Inner Product Defnton 1 () A Eucldean space s a fnte-dmensonal vector space over the reals R, wth an nner product,. Defnton 2 (Inner Product) An nner product, on a real vector space X s a symmetrc, blnear,
More informationAPPENDIX A Some Linear Algebra
APPENDIX A Some Lnear Algebra The collecton of m, n matrces A.1 Matrces a 1,1,..., a 1,n A = a m,1,..., a m,n wth real elements a,j s denoted by R m,n. If n = 1 then A s called a column vector. Smlarly,
More informationLinear Regression Analysis: Terminology and Notation
ECON 35* -- Secton : Basc Concepts of Regresson Analyss (Page ) Lnear Regresson Analyss: Termnology and Notaton Consder the generc verson of the smple (two-varable) lnear regresson model. It s represented
More informationLectures - Week 4 Matrix norms, Conditioning, Vector Spaces, Linear Independence, Spanning sets and Basis, Null space and Range of a Matrix
Lectures - Week 4 Matrx norms, Condtonng, Vector Spaces, Lnear Independence, Spannng sets and Bass, Null space and Range of a Matrx Matrx Norms Now we turn to assocatng a number to each matrx. We could
More informationReport on Image warping
Report on Image warpng Xuan Ne, Dec. 20, 2004 Ths document summarzed the algorthms of our mage warpng soluton for further study, and there s a detaled descrpton about the mplementaton of these algorthms.
More informationFoundations of Arithmetic
Foundatons of Arthmetc Notaton We shall denote the sum and product of numbers n the usual notaton as a 2 + a 2 + a 3 + + a = a, a 1 a 2 a 3 a = a The notaton a b means a dvdes b,.e. ac = b where c s an
More informationThe Minimum Universal Cost Flow in an Infeasible Flow Network
Journal of Scences, Islamc Republc of Iran 17(2): 175-180 (2006) Unversty of Tehran, ISSN 1016-1104 http://jscencesutacr The Mnmum Unversal Cost Flow n an Infeasble Flow Network H Saleh Fathabad * M Bagheran
More informationCollege of Computer & Information Science Fall 2009 Northeastern University 20 October 2009
College of Computer & Informaton Scence Fall 2009 Northeastern Unversty 20 October 2009 CS7880: Algorthmc Power Tools Scrbe: Jan Wen and Laura Poplawsk Lecture Outlne: Prmal-dual schema Network Desgn:
More informationLecture 10 Support Vector Machines II
Lecture 10 Support Vector Machnes II 22 February 2016 Taylor B. Arnold Yale Statstcs STAT 365/665 1/28 Notes: Problem 3 s posted and due ths upcomng Frday There was an early bug n the fake-test data; fxed
More informationNice plotting of proteins II
Nce plottng of protens II Fnal remark regardng effcency: It s possble to wrte the Newton representaton n a way that can be computed effcently, usng smlar bracketng that we made for the frst representaton
More informationCHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE
CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE Analytcal soluton s usually not possble when exctaton vares arbtrarly wth tme or f the system s nonlnear. Such problems can be solved by numercal tmesteppng
More informationEcon107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4)
I. Classcal Assumptons Econ7 Appled Econometrcs Topc 3: Classcal Model (Studenmund, Chapter 4) We have defned OLS and studed some algebrac propertes of OLS. In ths topc we wll study statstcal propertes
More informationNumber of cases Number of factors Number of covariates Number of levels of factor i. Value of the dependent variable for case k
ANOVA Model and Matrx Computatons Notaton The followng notaton s used throughout ths chapter unless otherwse stated: N F CN Y Z j w W Number of cases Number of factors Number of covarates Number of levels
More informationLecture 20: Lift and Project, SDP Duality. Today we will study the Lift and Project method. Then we will prove the SDP duality theorem.
prnceton u. sp 02 cos 598B: algorthms and complexty Lecture 20: Lft and Project, SDP Dualty Lecturer: Sanjeev Arora Scrbe:Yury Makarychev Today we wll study the Lft and Project method. Then we wll prove
More informationFormulas for the Determinant
page 224 224 CHAPTER 3 Determnants e t te t e 2t 38 A = e t 2te t e 2t e t te t 2e 2t 39 If 123 A = 345, 456 compute the matrx product A adj(a) What can you conclude about det(a)? For Problems 40 43, use
More informationWeek3, Chapter 4. Position and Displacement. Motion in Two Dimensions. Instantaneous Velocity. Average Velocity
Week3, Chapter 4 Moton n Two Dmensons Lecture Quz A partcle confned to moton along the x axs moves wth constant acceleraton from x =.0 m to x = 8.0 m durng a 1-s tme nterval. The velocty of the partcle
More informationCubic Trigonometric B-Spline Applied to Linear Two-Point Boundary Value Problems of Order Two
World Academy of Scence Engneerng and echnology Internatonal Journal of Mathematcal and omputatonal Scences Vol: No:0 00 ubc rgonometrc B-Splne Appled to Lnear wo-pont Boundary Value Problems of Order
More informationOn the Multicriteria Integer Network Flow Problem
BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 5, No 2 Sofa 2005 On the Multcrtera Integer Network Flow Problem Vassl Vasslev, Marana Nkolova, Maryana Vassleva Insttute of
More informationWorkshop: Approximating energies and wave functions Quantum aspects of physical chemistry
Workshop: Approxmatng energes and wave functons Quantum aspects of physcal chemstry http://quantum.bu.edu/pltl/6/6.pdf Last updated Thursday, November 7, 25 7:9:5-5: Copyrght 25 Dan Dll (dan@bu.edu) Department
More informationYong Joon Ryang. 1. Introduction Consider the multicommodity transportation problem with convex quadratic cost function. 1 2 (x x0 ) T Q(x x 0 )
Kangweon-Kyungk Math. Jour. 4 1996), No. 1, pp. 7 16 AN ITERATIVE ROW-ACTION METHOD FOR MULTICOMMODITY TRANSPORTATION PROBLEMS Yong Joon Ryang Abstract. The optmzaton problems wth quadratc constrants often
More informationBézier curves. Michael S. Floater. September 10, These notes provide an introduction to Bézier curves. i=0
Bézer curves Mchael S. Floater September 1, 215 These notes provde an ntroducton to Bézer curves. 1 Bernsten polynomals Recall that a real polynomal of a real varable x R, wth degree n, s a functon of
More informationModule 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur
Module 3 LOSSY IMAGE COMPRESSION SYSTEMS Verson ECE IIT, Kharagpur Lesson 6 Theory of Quantzaton Verson ECE IIT, Kharagpur Instructonal Objectves At the end of ths lesson, the students should be able to:
More informationELASTIC WAVE PROPAGATION IN A CONTINUOUS MEDIUM
ELASTIC WAVE PROPAGATION IN A CONTINUOUS MEDIUM An elastc wave s a deformaton of the body that travels throughout the body n all drectons. We can examne the deformaton over a perod of tme by fxng our look
More informationFinite Element Modelling of truss/cable structures
Pet Schreurs Endhoven Unversty of echnology Department of Mechancal Engneerng Materals echnology November 3, 214 Fnte Element Modellng of truss/cable structures 1 Fnte Element Analyss of prestressed structures
More informationU.C. Berkeley CS294: Beyond Worst-Case Analysis Luca Trevisan September 5, 2017
U.C. Berkeley CS94: Beyond Worst-Case Analyss Handout 4s Luca Trevsan September 5, 07 Summary of Lecture 4 In whch we ntroduce semdefnte programmng and apply t to Max Cut. Semdefnte Programmng Recall that
More informatione - c o m p a n i o n
OPERATIONS RESEARCH http://dxdoorg/0287/opre007ec e - c o m p a n o n ONLY AVAILABLE IN ELECTRONIC FORM 202 INFORMS Electronc Companon Generalzed Quantty Competton for Multple Products and Loss of Effcency
More informationChapter 5. Solution of System of Linear Equations. Module No. 6. Solution of Inconsistent and Ill Conditioned Systems
Numercal Analyss by Dr. Anta Pal Assstant Professor Department of Mathematcs Natonal Insttute of Technology Durgapur Durgapur-713209 emal: anta.bue@gmal.com 1 . Chapter 5 Soluton of System of Lnear Equatons
More informationANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U)
Econ 413 Exam 13 H ANSWERS Settet er nndelt 9 deloppgaver, A,B,C, som alle anbefales å telle lkt for å gøre det ltt lettere å stå. Svar er gtt . Unfortunately, there s a prntng error n the hnt of
More informationPerfect Competition and the Nash Bargaining Solution
Perfect Competton and the Nash Barganng Soluton Renhard John Department of Economcs Unversty of Bonn Adenauerallee 24-42 53113 Bonn, Germany emal: rohn@un-bonn.de May 2005 Abstract For a lnear exchange
More informationStructure and Drive Paul A. Jensen Copyright July 20, 2003
Structure and Drve Paul A. Jensen Copyrght July 20, 2003 A system s made up of several operatons wth flow passng between them. The structure of the system descrbes the flow paths from nputs to outputs.
More informationLinear Approximation with Regularization and Moving Least Squares
Lnear Approxmaton wth Regularzaton and Movng Least Squares Igor Grešovn May 007 Revson 4.6 (Revson : March 004). 5 4 3 0.5 3 3.5 4 Contents: Lnear Fttng...4. Weghted Least Squares n Functon Approxmaton...
More informationCOMPLEX NUMBERS AND QUADRATIC EQUATIONS
COMPLEX NUMBERS AND QUADRATIC EQUATIONS INTRODUCTION We know that x 0 for all x R e the square of a real number (whether postve, negatve or ero) s non-negatve Hence the equatons x, x, x + 7 0 etc are not
More informationConstruction of Serendipity Shape Functions by Geometrical Probability
J. Basc. Appl. Sc. Res., ()56-56, 0 0, TextRoad Publcaton ISS 00-0 Journal of Basc and Appled Scentfc Research www.textroad.com Constructon of Serendpty Shape Functons by Geometrcal Probablty Kamal Al-Dawoud
More informationDiscussion of Extensions of the Gauss-Markov Theorem to the Case of Stochastic Regression Coefficients Ed Stanek
Dscusson of Extensons of the Gauss-arkov Theorem to the Case of Stochastc Regresson Coeffcents Ed Stanek Introducton Pfeffermann (984 dscusses extensons to the Gauss-arkov Theorem n settngs where regresson
More informationFuzzy Boundaries of Sample Selection Model
Proceedngs of the 9th WSES Internatonal Conference on ppled Mathematcs, Istanbul, Turkey, May 7-9, 006 (pp309-34) Fuzzy Boundares of Sample Selecton Model L. MUHMD SFIIH, NTON BDULBSH KMIL, M. T. BU OSMN
More informationThe equation of motion of a dynamical system is given by a set of differential equations. That is (1)
Dynamcal Systems Many engneerng and natural systems are dynamcal systems. For example a pendulum s a dynamcal system. State l The state of the dynamcal system specfes t condtons. For a pendulum n the absence
More informationLinear, affine, and convex sets and hulls In the sequel, unless otherwise specified, X will denote a real vector space.
Lnear, affne, and convex sets and hulls In the sequel, unless otherwse specfed, X wll denote a real vector space. Lnes and segments. Gven two ponts x, y X, we defne xy = {x + t(y x) : t R} = {(1 t)x +
More informationMA 323 Geometric Modelling Course Notes: Day 13 Bezier Curves & Bernstein Polynomials
MA 323 Geometrc Modellng Course Notes: Day 13 Bezer Curves & Bernsten Polynomals Davd L. Fnn Over the past few days, we have looked at de Casteljau s algorthm for generatng a polynomal curve, and we have
More informationModule 1 : The equation of continuity. Lecture 1: Equation of Continuity
1 Module 1 : The equaton of contnuty Lecture 1: Equaton of Contnuty 2 Advanced Heat and Mass Transfer: Modules 1. THE EQUATION OF CONTINUITY : Lectures 1-6 () () () (v) (v) Overall Mass Balance Momentum
More informationn α j x j = 0 j=1 has a nontrivial solution. Here A is the n k matrix whose jth column is the vector for all t j=0
MODULE 2 Topcs: Lnear ndependence, bass and dmenson We have seen that f n a set of vectors one vector s a lnear combnaton of the remanng vectors n the set then the span of the set s unchanged f that vector
More informationThe Geometry of Logit and Probit
The Geometry of Logt and Probt Ths short note s meant as a supplement to Chapters and 3 of Spatal Models of Parlamentary Votng and the notaton and reference to fgures n the text below s to those two chapters.
More informationModule 3: Element Properties Lecture 1: Natural Coordinates
Module 3: Element Propertes Lecture : Natural Coordnates Natural coordnate system s bascally a local coordnate system whch allows the specfcaton of a pont wthn the element by a set of dmensonless numbers
More informationLOW BIAS INTEGRATED PATH ESTIMATORS. James M. Calvin
Proceedngs of the 007 Wnter Smulaton Conference S G Henderson, B Bller, M-H Hseh, J Shortle, J D Tew, and R R Barton, eds LOW BIAS INTEGRATED PATH ESTIMATORS James M Calvn Department of Computer Scence
More informationBezier curves. Michael S. Floater. August 25, These notes provide an introduction to Bezier curves. i=0
Bezer curves Mchael S. Floater August 25, 211 These notes provde an ntroducton to Bezer curves. 1 Bernsten polynomals Recall that a real polynomal of a real varable x R, wth degree n, s a functon of the
More informationApplication of B-Spline to Numerical Solution of a System of Singularly Perturbed Problems
Mathematca Aeterna, Vol. 1, 011, no. 06, 405 415 Applcaton of B-Splne to Numercal Soluton of a System of Sngularly Perturbed Problems Yogesh Gupta Department of Mathematcs Unted College of Engneerng &
More informationSpin-rotation coupling of the angularly accelerated rigid body
Spn-rotaton couplng of the angularly accelerated rgd body Loua Hassan Elzen Basher Khartoum, Sudan. Postal code:11123 E-mal: louaelzen@gmal.com November 1, 2017 All Rghts Reserved. Abstract Ths paper s
More informationCubic Trigonometric Rational Wat Bezier Curves
Cubc Trgonometrc Ratonal Wat Bezer Curves Urvash Mshra Department of Mathematcs Mata Gujr Mahla Mahavdyalaya Jabalpur Madhya Pradesh Inda Abstract- A new knd of Ratonal cubc Bézer bass functon by the blendng
More informationKernel Methods and SVMs Extension
Kernel Methods and SVMs Extenson The purpose of ths document s to revew materal covered n Machne Learnng 1 Supervsed Learnng regardng support vector machnes (SVMs). Ths document also provdes a general
More informationWeighted Fifth Degree Polynomial Spline
Pure and Appled Mathematcs Journal 5; 4(6): 69-74 Publshed onlne December, 5 (http://www.scencepublshnggroup.com/j/pamj) do:.648/j.pamj.546.8 ISSN: 36-979 (Prnt); ISSN: 36-98 (Onlne) Weghted Ffth Degree
More information5 The Rational Canonical Form
5 The Ratonal Canoncal Form Here p s a monc rreducble factor of the mnmum polynomal m T and s not necessarly of degree one Let F p denote the feld constructed earler n the course, consstng of all matrces
More informationNON-CENTRAL 7-POINT FORMULA IN THE METHOD OF LINES FOR PARABOLIC AND BURGERS' EQUATIONS
IJRRAS 8 (3 September 011 www.arpapress.com/volumes/vol8issue3/ijrras_8_3_08.pdf NON-CENTRAL 7-POINT FORMULA IN THE METHOD OF LINES FOR PARABOLIC AND BURGERS' EQUATIONS H.O. Bakodah Dept. of Mathematc
More informationVolume 18 Figure 1. Notation 1. Notation 2. Observation 1. Remark 1. Remark 2. Remark 3. Remark 4. Remark 5. Remark 6. Theorem A [2]. Theorem B [2].
Bulletn of Mathematcal Scences and Applcatons Submtted: 016-04-07 ISSN: 78-9634, Vol. 18, pp 1-10 Revsed: 016-09-08 do:10.1805/www.scpress.com/bmsa.18.1 Accepted: 016-10-13 017 ScPress Ltd., Swtzerland
More informationSalmon: Lectures on partial differential equations. Consider the general linear, second-order PDE in the form. ,x 2
Salmon: Lectures on partal dfferental equatons 5. Classfcaton of second-order equatons There are general methods for classfyng hgher-order partal dfferental equatons. One s very general (applyng even to
More informationMonotonic Interpolating Curves by Using Rational. Cubic Ball Interpolation
Appled Mathematcal Scences, vol. 8, 204, no. 46, 7259 7276 HIKARI Ltd, www.m-hkar.com http://dx.do.org/0.2988/ams.204.47554 Monotonc Interpolatng Curves by Usng Ratonal Cubc Ball Interpolaton Samsul Arffn
More informationNUMERICAL DIFFERENTIATION
NUMERICAL DIFFERENTIATION 1 Introducton Dfferentaton s a method to compute the rate at whch a dependent output y changes wth respect to the change n the ndependent nput x. Ths rate of change s called the
More informationAssortment Optimization under MNL
Assortment Optmzaton under MNL Haotan Song Aprl 30, 2017 1 Introducton The assortment optmzaton problem ams to fnd the revenue-maxmzng assortment of products to offer when the prces of products are fxed.
More informationSTAT 3008 Applied Regression Analysis
STAT 3008 Appled Regresson Analyss Tutoral : Smple Lnear Regresson LAI Chun He Department of Statstcs, The Chnese Unversty of Hong Kong 1 Model Assumpton To quantfy the relatonshp between two factors,
More informationNorms, Condition Numbers, Eigenvalues and Eigenvectors
Norms, Condton Numbers, Egenvalues and Egenvectors 1 Norms A norm s a measure of the sze of a matrx or a vector For vectors the common norms are: N a 2 = ( x 2 1/2 the Eucldean Norm (1a b 1 = =1 N x (1b
More information9 Characteristic classes
THEODORE VORONOV DIFFERENTIAL GEOMETRY. Sprng 2009 [under constructon] 9 Characterstc classes 9.1 The frst Chern class of a lne bundle Consder a complex vector bundle E B of rank p. We shall construct
More informationPHYS 705: Classical Mechanics. Calculus of Variations II
1 PHYS 705: Classcal Mechancs Calculus of Varatons II 2 Calculus of Varatons: Generalzaton (no constrant yet) Suppose now that F depends on several dependent varables : We need to fnd such that has a statonary
More informationPhysics 5153 Classical Mechanics. D Alembert s Principle and The Lagrangian-1
P. Guterrez Physcs 5153 Classcal Mechancs D Alembert s Prncple and The Lagrangan 1 Introducton The prncple of vrtual work provdes a method of solvng problems of statc equlbrum wthout havng to consder the
More informationTransfer Functions. Convenient representation of a linear, dynamic model. A transfer function (TF) relates one input and one output: ( ) system
Transfer Functons Convenent representaton of a lnear, dynamc model. A transfer functon (TF) relates one nput and one output: x t X s y t system Y s The followng termnology s used: x y nput output forcng
More informationThe Finite Element Method
The Fnte Element Method GENERAL INTRODUCTION Read: Chapters 1 and 2 CONTENTS Engneerng and analyss Smulaton of a physcal process Examples mathematcal model development Approxmate solutons and methods of
More informationALGORITHM FOR THE CALCULATION OF THE TWO VARIABLES CUBIC SPLINE FUNCTION
ANALELE ŞTIINŢIFICE ALE UNIVERSITĂŢII AL.I. CUZA DIN IAŞI (S.N.) MATEMATICĂ, Tomul LIX, 013, f.1 DOI: 10.478/v10157-01-00-y ALGORITHM FOR THE CALCULATION OF THE TWO VARIABLES CUBIC SPLINE FUNCTION BY ION
More informationGames of Threats. Elon Kohlberg Abraham Neyman. Working Paper
Games of Threats Elon Kohlberg Abraham Neyman Workng Paper 18-023 Games of Threats Elon Kohlberg Harvard Busness School Abraham Neyman The Hebrew Unversty of Jerusalem Workng Paper 18-023 Copyrght 2017
More informationIterative General Dynamic Model for Serial-Link Manipulators
EEL6667: Knematcs, Dynamcs and Control of Robot Manpulators 1. Introducton Iteratve General Dynamc Model for Seral-Lnk Manpulators In ths set of notes, we are gong to develop a method for computng a general
More informationC/CS/Phy191 Problem Set 3 Solutions Out: Oct 1, 2008., where ( 00. ), so the overall state of the system is ) ( ( ( ( 00 ± 11 ), Φ ± = 1
C/CS/Phy9 Problem Set 3 Solutons Out: Oct, 8 Suppose you have two qubts n some arbtrary entangled state ψ You apply the teleportaton protocol to each of the qubts separately What s the resultng state obtaned
More informationMAE140 - Linear Circuits - Fall 13 Midterm, October 31
Instructons ME140 - Lnear Crcuts - Fall 13 Mdterm, October 31 () Ths exam s open book. You may use whatever wrtten materals you choose, ncludng your class notes and textbook. You may use a hand calculator
More informationMaximum Likelihood Estimation of Binary Dependent Variables Models: Probit and Logit. 1. General Formulation of Binary Dependent Variables Models
ECO 452 -- OE 4: Probt and Logt Models ECO 452 -- OE 4 Maxmum Lkelhood Estmaton of Bnary Dependent Varables Models: Probt and Logt hs note demonstrates how to formulate bnary dependent varables models
More informationACTM State Calculus Competition Saturday April 30, 2011
ACTM State Calculus Competton Saturday Aprl 30, 2011 ACTM State Calculus Competton Sprng 2011 Page 1 Instructons: For questons 1 through 25, mark the best answer choce on the answer sheet provde Afterward
More information1. A bound n terms of a global mesh rato In ths secton, I outlne the proof of a slght strengthenng of (0.1) n order to gve an ndcaton of some of the a
n Approxmaton and Functon Spaces. Cesels (ed.) North Holland (Amsterdam), 1981, pp. 163{175 On a max{norm bound for the least{squares splne approxmant Carl de Boor Unversty of Wsconsn-Madson, MRC, Madson,
More informationCS 468 Lecture 16: Isometry Invariance and Spectral Techniques
CS 468 Lecture 16: Isometry Invarance and Spectral Technques Justn Solomon Scrbe: Evan Gawlk Introducton. In geometry processng, t s often desrable to characterze the shape of an object n a manner that
More information9 Derivation of Rate Equations from Single-Cell Conductance (Hodgkin-Huxley-like) Equations
Physcs 171/271 - Chapter 9R -Davd Klenfeld - Fall 2005 9 Dervaton of Rate Equatons from Sngle-Cell Conductance (Hodgkn-Huxley-lke) Equatons We consder a network of many neurons, each of whch obeys a set
More informationLecture 13 APPROXIMATION OF SECOMD ORDER DERIVATIVES
COMPUTATIONAL FLUID DYNAMICS: FDM: Appromaton of Second Order Dervatves Lecture APPROXIMATION OF SECOMD ORDER DERIVATIVES. APPROXIMATION OF SECOND ORDER DERIVATIVES Second order dervatves appear n dffusve
More informationSimulated Power of the Discrete Cramér-von Mises Goodness-of-Fit Tests
Smulated of the Cramér-von Mses Goodness-of-Ft Tests Steele, M., Chaselng, J. and 3 Hurst, C. School of Mathematcal and Physcal Scences, James Cook Unversty, Australan School of Envronmental Studes, Grffth
More informationStanford University CS359G: Graph Partitioning and Expanders Handout 4 Luca Trevisan January 13, 2011
Stanford Unversty CS359G: Graph Parttonng and Expanders Handout 4 Luca Trevsan January 3, 0 Lecture 4 In whch we prove the dffcult drecton of Cheeger s nequalty. As n the past lectures, consder an undrected
More informationTHE CHINESE REMAINDER THEOREM. We should thank the Chinese for their wonderful remainder theorem. Glenn Stevens
THE CHINESE REMAINDER THEOREM KEITH CONRAD We should thank the Chnese for ther wonderful remander theorem. Glenn Stevens 1. Introducton The Chnese remander theorem says we can unquely solve any par of
More informationSOLVING NON-LINEAR SYSTEMS BY NEWTON s METHOD USING SPREADSHEET EXCEL Tay Kim Gaik Universiti Tun Hussein Onn Malaysia
SOLVING NON-LINEAR SYSTEMS BY NEWTON s METHOD USING SPREADSHEET EXCEL Tay Km Gak Unverst Tun Hussen Onn Malaysa Kek Se Long Unverst Tun Hussen Onn Malaysa Rosmla Abdul-Kahar
More informationAdditional Codes using Finite Difference Method. 1 HJB Equation for Consumption-Saving Problem Without Uncertainty
Addtonal Codes usng Fnte Dfference Method Benamn Moll 1 HJB Equaton for Consumpton-Savng Problem Wthout Uncertanty Before consderng the case wth stochastc ncome n http://www.prnceton.edu/~moll/ HACTproect/HACT_Numercal_Appendx.pdf,
More informationNumerical Solution of Ordinary Differential Equations
Numercal Methods (CENG 00) CHAPTER-VI Numercal Soluton of Ordnar Dfferental Equatons 6 Introducton Dfferental equatons are equatons composed of an unknown functon and ts dervatves The followng are examples
More information1 Matrix representations of canonical matrices
1 Matrx representatons of canoncal matrces 2-d rotaton around the orgn: ( ) cos θ sn θ R 0 = sn θ cos θ 3-d rotaton around the x-axs: R x = 1 0 0 0 cos θ sn θ 0 sn θ cos θ 3-d rotaton around the y-axs:
More informationSolution Thermodynamics
Soluton hermodynamcs usng Wagner Notaton by Stanley. Howard Department of aterals and etallurgcal Engneerng South Dakota School of nes and echnology Rapd Cty, SD 57701 January 7, 001 Soluton hermodynamcs
More informationA Hybrid Variational Iteration Method for Blasius Equation
Avalable at http://pvamu.edu/aam Appl. Appl. Math. ISSN: 1932-9466 Vol. 10, Issue 1 (June 2015), pp. 223-229 Applcatons and Appled Mathematcs: An Internatonal Journal (AAM) A Hybrd Varatonal Iteraton Method
More informationPolynomial Reproduction by Symmetric Subdivision Schemes
Polynomal Reproducton by Symmetrc Subdvson Schemes Nra Dyn School of Mathematcal Scences Tel Avv Unversty Malcolm A. Sabn Computer Laboratory Unversty of Cambrdge Ka Hormann Department of Informatcs Clausthal
More informationElectrical double layer: revisit based on boundary conditions
Electrcal double layer: revst based on boundary condtons Jong U. Km Department of Electrcal and Computer Engneerng, Texas A&M Unversty College Staton, TX 77843-318, USA Abstract The electrcal double layer
More informationChapter 2 - The Simple Linear Regression Model S =0. e i is a random error. S β2 β. This is a minimization problem. Solution is a calculus exercise.
Chapter - The Smple Lnear Regresson Model The lnear regresson equaton s: where y + = β + β e for =,..., y and are observable varables e s a random error How can an estmaton rule be constructed for the
More information