On the Borda Method for Multicriterial Decision-Making

Size: px
Start display at page:

Download "On the Borda Method for Multicriterial Decision-Making"

Transcription

1 O the Brda Methd r Multcrteral Decs-Makg Radu A. Pău Iteratal Metary Fud Isttute th Street, N.W. Washgt, D.C rpau@m.rg r radupau@yah.cm Abstract The preset paper dscusses tw ssues wth multcrteral decs-makg methds Brda type (whe scres such as, -,, 2, are gve t the bects t be raked ad the herarchy s btaed based the ttals these scres). The rst ssue s related t the luece the result varus trasrmats the scres. We shw that a lear trasrmat the scres des t chage the al rakg ad that (almst) ay plymal secd degree r mre, wth pstve cecets, ca alter the slut (rakg). The same happes e chages the scres by emplyg the lgarthm, expetal, r square rt ucts. I the secd part the paper we csder a terated vers the Brda methd. We shw that ths methd s t rbust: there are cases whe deret sluts are retured at deret terats.. Itrduct It s well-kw that mst decss (ether persal, scal, r ecmc) are multcrteral: several pssbltes act (bects) are take t accut gve several crtera, r are csdered by several decs-makers. The gal s t select a partcular curse act (a uque bect), whch s te tmes the e at the tp a sythess rakg bects. There are umerus methds t address ths prblem the reader s guded twards Adraşu et al. (986) ad the reereces t prvdes. I the 8 th cetury, the Frech mathematca Jea-Charles de Brda has prpsed a methd whch s stll wdely used. I shrt, the methd requres that, usg a pre-deed scale, pts be allcated t bects that are t be raked, ad that these pts be summed; the sythess herarchy s btaed by srtg decreasg rder these scres. The pts that are allcated ca decrease cstat steps (r stace, the bect the th place a departg herarchy may receve m + pts, where m s the umber bects t be raked), r they ca belg t a specc set (r example, Ocescu (970) prpses that, ½, ¼,,½,,½ m-, ½ m be the pts allcated t bects the st, 2 d,, th,,m th place a departg herarchy). It has bee shw that such rakg methds ctradct e the ma ratalty cdts the Arrw therem : depedece. I shrt, a multcrteral decs-makg methd (a methd whch aggregates the departg herarches) satses the depedece prperty the al rderg ay tw bects des t deped the presece (r absece) a thrd bect. Fr stace, let us emply the example rm Pău (987, p. 30), wh csders ve decs-makers wh eed t rak three bects. The departg herarches are (a, a 2, a 3 ), (a, a 2, a 3 ), (a 3, a, a 2 ), (a 2, a 3, a ), (a 2, a 3, a ). I we allcate three pts t the rst bect a herarchy, tw pts t the secd bect ad e pt t the thrd, we bta the llwg scres: a 0, a 2, a 3 9, Fr a exhaustve vew, the reader s guded twards the semal wrk Arrw (963). 364

2 leadg t sythess herarchy (a 2, a, a 3 ). Hwever, we elmate bect a 2 rm all departg herarches, ad thus have the ve decs-makes dcate (a, a 3 ), (a, a 3 ), (a 3, a ), (a 3, a ), (a 3, a ), the, a smlar maer as abve, a btas 7 pts whle a 3 btas 8 pts, ad thus the sythess herarchy s (a 3, a ). Clearly, the relatshp betwee a ad a 3 s ppste w t the e btaed whe a 2 was preset. Ather weakess may methds r herarchy aggregat s the pssblty t mapulate the al result by adustg sme dvdual herarches (vtes). Ths pt has bee emphaszed the early 70s see Gbbard (973) ad Satterthwate (975). Oe way t cuter ths pssblty s t make the methd mre cmplcated, s that recastg ad luecg the result are less lkely. A classc example s the Cpelad methd, thrughly preseted by Nurm (989). The rst degree Cpelad methd s act a Brda methd: the bect the th place s allcated m pts (ad t m + as abve). The methd ca be mapulated plymal tme. The secd degree Cpelad methd s mre evlved. Ater the rst step, whch bects receve pts as descrbed abve, the secd step the ttal scre r each bect s btaed by summg the rst step pts thse bects dmated by ths partcular bect. Mapulat ths methd s sgcatly mre dcult; ths has bee shw t be a NPcmplete prblem. The dea t terate a Brda-lke methd urther appears aturally. Hwever, ths rases tw mprtat ssues: Frst, ttal scres at each step crease rapdly, whch may lead t dcultes whe mplemetg large, real-le applcats cmputer. A usual slut such cases s data rmalzat, btaed by applyg a specc trasrmat the tal gures. We wll prve that such trasrmats may alter the al result, ad s ths shuld be avded. Precsely, we shw that a lear trasrmat des t alter the result, but sme plymal trasrmats d chage the herarchy. A smlar stuat s ud whe e uses the expetal, lgarthmc, r square rt trasrmats. Secd, a desrable eature terated methds s that herarches at deret terats rema uchaged. A umercal example wll shw that ths gal s smetmes mssed. 2. Ntat I what llws, wll always represet the umber decs-makers (r the umber crtera), whch we dete d, d 2,,d, ad m wll represet the umber bects t be raked, deted, 2,, m. Each decs-maker cveys a herarchy bects (a ttal, lear rder) ad bects these departg herarches are allcated pts accrdg t ther pst the herarchy (mre pts beg allcated t bects raked hgher). Let d, ), where, m, dete the umber pts receved by bect decs-maker s d herarchy. We ca w dee the qualty a bect: ( ) = d, ). Let us csder a uct : R R. I the bects are allcated pts such as (d, )), hece trasrmed by applyg uct, the the crrespdg qualty a bect wll be deted ( ). 3. The sestvty the Brda methd t pt trasrmats Lemma. (The addtvty lemma) Let, 2 : R R be tw ucts. Let us assume we have a multcrteral decs-makg prblem such that, r tw bects, k, we have 365

3 ( ) > ( ), ( ) k 2 > 2 ( k Let uct (x) = a (x) + a 2 2 (x) + a 3, where a ad a 2 are tw pstve umbers. The, ). Pr. ( ) > ( k ). ( ) = ( a ( )) + a2 2( )) + a3) = a ( )) + a2 2( )) + a3 ( ) + a2 ( ) + a3 ( k ) + a2 ( k ) + a3 = a, 2 2 ( k ) = a. Clearly, ( ) > ( k ). The llwg example wll be used several tmes belw. We csder ur decs-makers wh have t decde ve bects, ad let the departg herarches be: d d 2 d 3 d 4 st place d place 4 3 rd place th place th place I we allcate 5 pts t the bect the rst place, 4 t the bect the secd, ad s, pt t the th placed bect, the the ttal scres the ve bects bta are 5, 4, 3,, 7 ad thus the Brda herarchy s (, 2, 3, 4, 5 ). ( ). Lemma 2. Gve the prevus example, r ay uct (x) = x k, wth k 2, we bta ( 2 ) > Pr. It s bvus that ( ) = 3 4 k + 3 k, ( 2 ) = 2 5 k + 3 k +. It suces t shw that 2 5 k > 3 4 k. We ca wrte 5 k = (4 + ) k ad use the Newt bmal seres. The, 2 (4 + ) k = 0 k k k k 0 2 ( C k 4 + Ck Ck 4 + Ck 4 ) = 2 4 k + 2 k 4 k- + α, where α s a strctly pstve umber. Sce k 2, we bta 2 4 k + 2 k 4 k- 3 4 k, whch meas that 2 5 k > 3 4 k. Observat. I the prevus setup, t ca be shw that r k 3 a eve strger result emerges: ( 3 ) > ( ). The equalty s clearly true r k = 3 whle, r k greater tha 3, t ca be 366

4 prved by duct ad by relyg ce aga the Newt bmal seres. We ca w demstrate the ma result ths sect. Therem. () A lear trasrmat, wth pstve cecets, the allcated pts des t alter the rakg prduced by the Brda methd. () Let us csder a plymal secd degree r mre, wth pstve cecets, such that the cecet the rst degree term s ether zer r equal t the cecet the secd degree term. Fr ay such plymal (x), there are multcrteral decsmakg prblems r whch the slut btaed by the Brda methd s altered e apples the (x) trasrmat. Pr. The Brda methd btas the sythess herarchy by rderg ( ) = d, ), m. A lear trasrmat (x) = ax + b, wth a 0, leads t ( ) whch, accrdg t Lemma, wll keep the herarchy uchaged 2. Let us csder a plymal (x) = a x k + a 2 x k- + + a k x + a k+, wth a 0, k +, ad ether a k = 0 r a k = a k-. I we dete (x) = a x k-+, k +, we have (x) = (x) + 2 (x) + + k-2 (x) + k+ (x) + ( k- (x) + k (x)). Accrdg t Lemma 2, r ay =, 2,, k 2, we have that ( 2) > ( ). Usg Lemma repeatedly, we get that g ( 2 ) > g ( ), where g(x) = (x) + 2 (x) + + k-2 (x) + k+ (x). Let us aalyze separately the case trasrmat h(x) = x 2 + x. Usg ths uct, 30 pts are allcated t the bect the rst place, 20 t the bect the secd place, 2 t the thrd placed bect, 6 t the bect the urth place ad 2 pts t the last bect a herarchy. Usg the prevus example departg herarches, h ( ) = 72 < h ( 2 ) = 74. Accrdg t Lemma, the same s the case we apply trasrmat g (x) = a k- g(x). We emply Lemma ce aga r ucts h(x) ad g (x), whch, by addt, lead precsely t plymal (x). Observat 2. The cdt we mpsed the cecets the rst ad secd degree terms the plymal s eeded the abve pr, gve the partcular example we were wrkg wth. Ideed, let us csder the plymal (x) = x 2 + 4x. The pts t be allcated t bects are w 45, 32, 2, 2, ad 5. Accrdgly, ( ) = 7 > ( 2 ) = 6, s the rder the tw bects reversed. As expected, ther lear trasrmats als chage the herarchy. Therem 2. Let us csder trasrmats (x) = 2 x, 2 (x) = l x, ad 3 (x) = x ½. There are multcrteral decs-makg prblems r whch the slut btaed by the Brda methd s altered e apples these trasrmats. Pr. I we csder the prevus example ad trasrmat (x) = 2 x, the the pts t be allcated are 32, 6, 8, 4, ad 2, whch leads t ( ) = 56, ( 2) = 74, ( 3) = 70, ( 4) = 28, ( 5) = 6, ad thus t herarchy ( 2, 3,, 4, 5 ). Ths s cmpletely deret rm the herarchy btaed whe 5, 4, 3, 2, pts were allcated. Fr trasrmat 2 (x) = l x we csder the example belw: d d 2 d 3 d 4 pts st place d place rd place th place th place Ths s a partcular case Lemma, whch (x) = x, 2 (x) = x, a = a 2 = a/2, ad a 3 = b. 367

5 Fr ths example, the Brda methd, usg the stadard pts 5, 4, 3, 2,, leads t herarchy (, 2, 3, 5, 4 ). We w allcate l 5, l 4, l 3, l 2, ad l pts (usg the apprxmate values we have lsted the abve table), whch leads t ttal scres 4.37, 4.65, 3.68, 2.99, ad 3.39 ad thus t herarchy ( 2,, 3, 5, 4 ), deret rm the prevus e. Lastly, r trasrmat 3 (x) = x ½ we csder the llwg example: d d 2 d 3 d 4 d5 pts st place d place rd place th place th place The Brda methd usg the stadard pt system leads t herarchy ( 3, 4,, 2, 5 ). By trasrmg the pts t be allcated usg uct 3 (see the apprxmate values the table abve), the ve bects get the scres 8.236, 8.292, 8.936, 8.74, ad 7.732, whch leads t herarchy ( 2,, 3, 5, 4 ), deret rm the prevus e. 4. Iteratg the Brda methd As meted the trduct, the Cpelad methd secd degree s a tw-step prcedure: the rst step pts are allcated t bects a smlar ash t the Brda methd, whle the secd step the ttal r each bect s btaed by summg the rst step pts the bects that partcular bect dmates. We w prceed t terate ths prcedure urther. Frmally, r a multcrteral decs-makg prblem the type we have aalyzed s ar, where bects qualty s evaluated by ( ), wth m, we dee ( ), wth 0, ths maer: ( ) = ( ), + ( ) = ( l ), k= l D ( ) where D k () s the set bects decs-maker s d k herarchy whch are dmated by bect. The, at each step, the decreasg rder ( ), wth m, dcates the herarchy at that partcular terat. Naturally, the quest s whether, gve a partcular prblem, herarches rema uchaged at varus steps. I s, ths wuld mea the prcedure s rbust, ad thus ull cdece shuld be placed that (cstat) herarchy. Urtuately, ths s t always the case, ad we wll demstrate ths by emplyg a umercal example. Fur decs-makers eed t rak ve bects, ad ther departg herarches are prvded the table belw: k, d d 2 d 3 d 4 st place d place rd place th place th place

6 Gve the abve, the results the rst te terats (retured by a smple cmputer prgram) are the llwg: Scres at terat : 2,, 5, 9, 3 Herarchy at terat : (, 2, 4, 3, 5 ) Scres at terat 2: 72, 75, 29, 59, 25 Herarchy at terat 2: ( 2,, 4, 3, 5 ) Scres at terat 3: 484, 483, 209, 38, 63 Herarchy at terat 3: (, 2, 4, 3, 5 ) Scres at terat 4: 326, 3227, 353, 2567, 073 Herarchy at terat 4: ( 2,, 4, 3, 5 ) Scres at terat 5: 2292, 24, 903, 6937, 747 Herarchy at terat 5: ( 2,, 4, 3, 5 ) Scres at terat 6: 4336, 4875, 59789, 2475, 4736 Herarchy at terat 6: ( 2,, 4, 3, 5 ) Scres at terat 7: , 94547, , , 3439 Herarchy at terat 7: ( 2,, 4, 3, 5 ) Scres at terat 8: , , , , Herarchy at terat 8: ( 2,, 4, 3, 5 ) Scres at terat 9: , , 74440, , Herarchy at terat 9: ( 2,, 4, 3, 5 ) Scres at terat 0: , , , , Herarchy at terat 0: ( 2,, 4, 3, 5 ) As dcated abve, herarches at the rst ur steps alterate ( ad 2 swtch ther places). Frm the urth step the herarchy remas cstat (hwever, we have t explred past the 0 th terat). 5. Ccludg remarks The tw ma results the preset paper are: () Trasrmg the tal pts allcated t bects a multcrteral decs-makg prblem slved by the Brda methd may alter the al result. Ths s the case r may the usual lear ucts e may emply: plymal secd degree r mre (wth sme restrcts placed cecets), lgarthm, expetal, square rt. The herarchy s t mded e emplys a lear trasrmat the tal pts. (2) Iteratg the Brda methd may lead t deret herarches at deret terats. We eed t emphasze ce aga that Brda-lke methds are the mst cmmly used r multcrteral decs prblems. The, the sgcace the abve results s bvus. Frst, caut shuld be exerted whe mdyg the tal data (by rmalzat r ther perats mpsed, r stace, by lmtats cmputg capacty). Als, e shuld always bear md that the al result may deped the actual methd emplyed, r eve the vers that partcular methd. 369

7 Reereces Adraşu M., Bacu A., Pascu A., Puşcaş E., Tasad Al. (986) Metde de decz multcrterale, Edtura Tehcă, Bucureşt Arrw K. (963) Scal Chce ad Idvdual Values, Jh Wley, New Yrk Gbbard A. (973) Mapulat Vtg Schemes: A Geeral Result, Ecmetrca, 4, Nurm H. (989) Cmputatal Appraches t Bargag ad Chce, Jural Theretcal Pltcs,, Ocescu O. (970) Prcedee de estmare cmparatvă a ur becte purtăatare de ma multe caracterstc, Revsta de Statstcă, 4 Pău Gh. (987) Paradxurle clasametelr, Edtura Ştţcă ş Ecclpedcă, Bucureşt Satterthwate M. (975) Strategy-Press ad Arrw's Cdts, Jural Ecmc Thery, 0,

The Simple Linear Regression Model: Theory

The Simple Linear Regression Model: Theory Chapter 3 The mple Lear Regress Mdel: Ther 3. The mdel 3.. The data bservats respse varable eplaatr varable : : Plttg the data.. Fgure 3.: Dsplag the cable data csdered b Che at al (993). There are 79

More information

PY3101 Optics. Learning objectives. Wave propagation in anisotropic media Poynting walk-off The index ellipsoid Birefringence. The Index Ellipsoid

PY3101 Optics. Learning objectives. Wave propagation in anisotropic media Poynting walk-off The index ellipsoid Birefringence. The Index Ellipsoid The Ide Ellpsd M.P. Vaugha Learg bjectves Wave prpagat astrpc meda Ptg walk-ff The de ellpsd Brefrgece 1 Wave prpagat astrpc meda The wave equat Relatve permttvt I E. Assumg free charges r currets E. Substtutg

More information

Objective of curve fitting is to represent a set of discrete data by a function (curve). Consider a set of discrete data as given in table.

Objective of curve fitting is to represent a set of discrete data by a function (curve). Consider a set of discrete data as given in table. CURVE FITTING Obectve curve ttg s t represet set dscrete dt b uct curve. Csder set dscrete dt s gve tble. 3 3 = T use the dt eectvel, curve epress s tted t the gve dt set, s = + = + + = e b ler uct plml

More information

Lecture 2. Basic Semiconductor Physics

Lecture 2. Basic Semiconductor Physics Lecture Basc Semcductr Physcs I ths lecture yu wll lear: What are semcductrs? Basc crystal structure f semcductrs Electrs ad hles semcductrs Itrsc semcductrs Extrsc semcductrs -ded ad -ded semcductrs Semcductrs

More information

CHAPTER 5 ENTROPY GENERATION Instructor: Prof. Dr. Uğur Atikol

CHAPTER 5 ENTROPY GENERATION Instructor: Prof. Dr. Uğur Atikol CAPER 5 ENROPY GENERAION Istructr: Pr. Dr. Uğur Atkl Chapter 5 Etrpy Geerat (Exergy Destruct Outle st Avalable rk Cycles eat ege cycles Rergerat cycles eat pump cycles Nlw Prcesses teady-flw Prcesses Exergy

More information

Ordinary Differential Equations. Orientation. Lesson Objectives. Ch. 25. ODE s. Runge-Kutta Methods. Motivation Mathematical Background

Ordinary Differential Equations. Orientation. Lesson Objectives. Ch. 25. ODE s. Runge-Kutta Methods. Motivation Mathematical Background Ordar Deretal Equats C. 5 Oretat ODE s Mtvat Matematcal Bacgrud Ruge-Kutta Metds Euler s Metd Hue ad Mdpt metds Less Objectves Be able t class ODE s ad dstgus ODE s rm PDE s. Be able t reduce t rder ODE

More information

Basics of heteroskedasticity

Basics of heteroskedasticity Sect 8 Heterskedastcty ascs f heterskedastcty We have assumed up t w ( ur SR ad MR assumpts) that the varace f the errr term was cstat acrss bservats Ths s urealstc may r mst ecmetrc applcats, especally

More information

Chapter 3.1: Polynomial Functions

Chapter 3.1: Polynomial Functions Ntes 3.1: Ply Fucs Chapter 3.1: Plymial Fuctis I Algebra I ad Algebra II, yu ecutered sme very famus plymial fuctis. I this secti, yu will meet may ther members f the plymial family, what sets them apart

More information

Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques Data Mg: cepts ad Techques 3 rd ed. hapter 10 1 Evaluat f lusterg lusterg evaluat assesses the feasblty f clusterg aalyss a data set ad the qualty f the results geerated by a clusterg methd. Three mar

More information

Goal of the Lecture. Lecture Structure. FWF 410: Analysis of Habitat Data I: Definitions and Descriptive Statistics

Goal of the Lecture. Lecture Structure. FWF 410: Analysis of Habitat Data I: Definitions and Descriptive Statistics FWF : Aalyss f Habtat Data I: Defts ad Descrptve tatstcs Number f Cveys A A B Bur Dsk Bur/Dsk Habtat Treatmet Matthew J. Gray, Ph.D. Cllege f Agrcultural ceces ad Natural Resurces Uversty f Teessee-Kvlle

More information

The fuzzy decision of transformer economic operation

The fuzzy decision of transformer economic operation The fuzzy decs f trasfrmer ecmc perat WENJUN ZHNG, HOZHONG CHENG, HUGNG XIONG, DEXING JI Departmet f Electrcal Egeerg hagha Jatg Uversty 954 Huasha Rad, 3 hagha P. R. CHIN bstract: - Ths paper presets

More information

Third handout: On the Gini Index

Third handout: On the Gini Index Thrd hadout: O the dex Corrado, a tala statstca, proposed (, 9, 96) to measure absolute equalt va the mea dfferece whch s defed as ( / ) where refers to the total umber of dvduals socet. Assume that. The

More information

MEASURES OF DISPERSION

MEASURES OF DISPERSION MEASURES OF DISPERSION Measure of Cetral Tedecy: Measures of Cetral Tedecy ad Dsperso ) Mathematcal Average: a) Arthmetc mea (A.M.) b) Geometrc mea (G.M.) c) Harmoc mea (H.M.) ) Averages of Posto: a) Meda

More information

. The set of these sums. be a partition of [ ab, ]. Consider the sum f( x) f( x 1)

. The set of these sums. be a partition of [ ab, ]. Consider the sum f( x) f( x 1) Chapter 7 Fuctos o Bouded Varato. Subject: Real Aalyss Level: M.Sc. Source: Syed Gul Shah (Charma, Departmet o Mathematcs, US Sargodha Collected & Composed by: Atq ur Rehma (atq@mathcty.org, http://www.mathcty.org

More information

Exergy Analysis of Large ME-TVC Desalination System

Exergy Analysis of Large ME-TVC Desalination System Exergy Aalyss f arge ME-V esalat System Awar O. Bamer Water & Eergy Prgram\Research rectrate Kuwat udat fr the Advacemet f Sceces (KAS) he 0 th Gulf Water ferece, -4 Aprl 0, ha- Qatar Outles Itrduct Prcess

More information

Multi-objective Programming Approach for. Fuzzy Linear Programming Problems

Multi-objective Programming Approach for. Fuzzy Linear Programming Problems Applied Mathematical Scieces Vl. 7 03. 37 8-87 HIKARI Ltd www.m-hikari.cm Multi-bective Prgrammig Apprach fr Fuzzy Liear Prgrammig Prblems P. Padia Departmet f Mathematics Schl f Advaced Scieces VIT Uiversity

More information

Part 4b Asymptotic Results for MRR2 using PRESS. Recall that the PRESS statistic is a special type of cross validation procedure (see Allen (1971))

Part 4b Asymptotic Results for MRR2 using PRESS. Recall that the PRESS statistic is a special type of cross validation procedure (see Allen (1971)) art 4b Asymptotc Results for MRR usg RESS Recall that the RESS statstc s a specal type of cross valdato procedure (see Alle (97)) partcular to the regresso problem ad volves fdg Y $,, the estmate at the

More information

ON THE LAGRANGIAN RHEONOMIC MECHANICAL SYSTEMS

ON THE LAGRANGIAN RHEONOMIC MECHANICAL SYSTEMS THE PUBISHING HOUSE PROCEEDINGS OF THE ROMANIAN ACADEMY, Seres A, OF THE ROMANIAN ACADEMY Vlume 1, Number 1/9, pp. ON THE AGRANGIAN RHEONOMIC MECHANICA SYSTEMS Radu MIRON*, Tmak KAWAGUCHI**, Hrak KAWAGUCHI***

More information

arxiv:math/ v1 [math.gm] 8 Dec 2005

arxiv:math/ v1 [math.gm] 8 Dec 2005 arxv:math/05272v [math.gm] 8 Dec 2005 A GENERALIZATION OF AN INEQUALITY FROM IMO 2005 NIKOLAI NIKOLOV The preset paper was spred by the thrd problem from the IMO 2005. A specal award was gve to Yure Boreko

More information

b. There appears to be a positive relationship between X and Y; that is, as X increases, so does Y.

b. There appears to be a positive relationship between X and Y; that is, as X increases, so does Y. .46. a. The frst varable (X) s the frst umber the par ad s plotted o the horzotal axs, whle the secod varable (Y) s the secod umber the par ad s plotted o the vertcal axs. The scatterplot s show the fgure

More information

Solutions. Definitions pertaining to solutions

Solutions. Definitions pertaining to solutions Slutis Defiitis pertaiig t slutis Slute is the substace that is disslved. It is usually preset i the smaller amut. Slvet is the substace that des the disslvig. It is usually preset i the larger amut. Slubility

More information

Intermediate Division Solutions

Intermediate Division Solutions Itermediate Divisi Slutis 1. Cmpute the largest 4-digit umber f the frm ABBA which is exactly divisible by 7. Sluti ABBA 1000A + 100B +10B+A 1001A + 110B 1001 is divisible by 7 (1001 7 143), s 1001A is

More information

5.1 Two-Step Conditional Density Estimator

5.1 Two-Step Conditional Density Estimator 5.1 Tw-Step Cditial Desity Estimatr We ca write y = g(x) + e where g(x) is the cditial mea fucti ad e is the regressi errr. Let f e (e j x) be the cditial desity f e give X = x: The the cditial desity

More information

Summary of the lecture in Biostatistics

Summary of the lecture in Biostatistics Summary of the lecture Bostatstcs Probablty Desty Fucto For a cotuos radom varable, a probablty desty fucto s a fucto such that: 0 dx a b) b a dx A probablty desty fucto provdes a smple descrpto of the

More information

Chapter 9 Jordan Block Matrices

Chapter 9 Jordan Block Matrices Chapter 9 Jorda Block atrces I ths chapter we wll solve the followg problem. Gve a lear operator T fd a bass R of F such that the matrx R (T) s as smple as possble. f course smple s a matter of taste.

More information

2.28 The Wall Street Journal is probably referring to the average number of cubes used per glass measured for some population that they have chosen.

2.28 The Wall Street Journal is probably referring to the average number of cubes used per glass measured for some population that they have chosen. .5 x 54.5 a. x 7. 786 7 b. The raked observatos are: 7.4, 7.5, 7.7, 7.8, 7.9, 8.0, 8.. Sce the sample sze 7 s odd, the meda s the (+)/ 4 th raked observato, or meda 7.8 c. The cosumer would more lkely

More information

X X X E[ ] E X E X. is the ()m n where the ( i,)th. j element is the mean of the ( i,)th., then

X X X E[ ] E X E X. is the ()m n where the ( i,)th. j element is the mean of the ( i,)th., then Secto 5 Vectors of Radom Varables Whe workg wth several radom varables,,..., to arrage them vector form x, t s ofte coveet We ca the make use of matrx algebra to help us orgaze ad mapulate large umbers

More information

Can we take the Mysticism Out of the Pearson Coefficient of Linear Correlation?

Can we take the Mysticism Out of the Pearson Coefficient of Linear Correlation? Ca we tae the Mstcsm Out of the Pearso Coeffcet of Lear Correlato? Itroducto As the ttle of ths tutoral dcates, our purpose s to egeder a clear uderstadg of the Pearso coeffcet of lear correlato studets

More information

CHAPTER 4 RADICAL EXPRESSIONS

CHAPTER 4 RADICAL EXPRESSIONS 6 CHAPTER RADICAL EXPRESSIONS. The th Root of a Real Number A real umber a s called the th root of a real umber b f Thus, for example: s a square root of sce. s also a square root of sce ( ). s a cube

More information

element k Using FEM to Solve Truss Problems

element k Using FEM to Solve Truss Problems sng EM t Slve Truss Prblems A truss s an engneerng structure cmpsed straght members, a certan materal, that are tpcall pn-ned at ther ends. Such members are als called tw-rce members snce the can nl transmt

More information

Discrete Mathematics and Probability Theory Fall 2016 Seshia and Walrand DIS 10b

Discrete Mathematics and Probability Theory Fall 2016 Seshia and Walrand DIS 10b CS 70 Dscrete Mathematcs ad Probablty Theory Fall 206 Sesha ad Walrad DIS 0b. Wll I Get My Package? Seaky delvery guy of some compay s out delverg packages to customers. Not oly does he had a radom package

More information

1 Onto functions and bijections Applications to Counting

1 Onto functions and bijections Applications to Counting 1 Oto fuctos ad bectos Applcatos to Coutg Now we move o to a ew topc. Defto 1.1 (Surecto. A fucto f : A B s sad to be surectve or oto f for each b B there s some a A so that f(a B. What are examples of

More information

Chapter 11 The Analysis of Variance

Chapter 11 The Analysis of Variance Chapter The Aalyss of Varace. Oe Factor Aalyss of Varace. Radomzed Bloc Desgs (ot for ths course) NIPRL . Oe Factor Aalyss of Varace.. Oe Factor Layouts (/4) Suppose that a expermeter s terested populatos

More information

Lecture 9: Tolerant Testing

Lecture 9: Tolerant Testing Lecture 9: Tolerat Testg Dael Kae Scrbe: Sakeerth Rao Aprl 4, 07 Abstract I ths lecture we prove a quas lear lower boud o the umber of samples eeded to do tolerat testg for L dstace. Tolerat Testg We have

More information

STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ " 1

STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ  1 STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS Recall Assumpto E(Y x) η 0 + η x (lear codtoal mea fucto) Data (x, y ), (x 2, y 2 ),, (x, y ) Least squares estmator ˆ E (Y x) ˆ " 0 + ˆ " x, where ˆ

More information

THE ROYAL STATISTICAL SOCIETY HIGHER CERTIFICATE

THE ROYAL STATISTICAL SOCIETY HIGHER CERTIFICATE THE ROYAL STATISTICAL SOCIETY 00 EXAMINATIONS SOLUTIONS HIGHER CERTIFICATE PAPER I STATISTICAL THEORY The Socety provdes these solutos to assst caddates preparg for the examatos future years ad for the

More information

Assignment 7/MATH 247/Winter, 2010 Due: Friday, March 19. Powers of a square matrix

Assignment 7/MATH 247/Winter, 2010 Due: Friday, March 19. Powers of a square matrix Assgmet 7/MATH 47/Wter, 00 Due: Frday, March 9 Powers o a square matrx Gve a square matrx A, ts powers A or large, or eve arbtrary, teger expoets ca be calculated by dagoalzg A -- that s possble (!) Namely,

More information

A Primer on Summation Notation George H Olson, Ph. D. Doctoral Program in Educational Leadership Appalachian State University Spring 2010

A Primer on Summation Notation George H Olson, Ph. D. Doctoral Program in Educational Leadership Appalachian State University Spring 2010 Summato Operator A Prmer o Summato otato George H Olso Ph D Doctoral Program Educatoal Leadershp Appalacha State Uversty Sprg 00 The summato operator ( ) {Greek letter captal sgma} s a structo to sum over

More information

MA 524 Homework 6 Solutions

MA 524 Homework 6 Solutions MA 524 Homework 6 Solutos. Sce S(, s the umber of ways to partto [] to k oempty blocks, ad c(, s the umber of ways to partto to k oempty blocks ad also the arrage each block to a cycle, we must have S(,

More information

Lecture Notes Types of economic variables

Lecture Notes Types of economic variables Lecture Notes 3 1. Types of ecoomc varables () Cotuous varable takes o a cotuum the sample space, such as all pots o a le or all real umbers Example: GDP, Polluto cocetrato, etc. () Dscrete varables fte

More information

Quantum Mechanics for Scientists and Engineers. David Miller

Quantum Mechanics for Scientists and Engineers. David Miller Quatum Mechaics fr Scietists ad Egieers David Miller Time-depedet perturbati thery Time-depedet perturbati thery Time-depedet perturbati basics Time-depedet perturbati thery Fr time-depedet prblems csider

More information

Derivation of 3-Point Block Method Formula for Solving First Order Stiff Ordinary Differential Equations

Derivation of 3-Point Block Method Formula for Solving First Order Stiff Ordinary Differential Equations Dervato of -Pot Block Method Formula for Solvg Frst Order Stff Ordary Dfferetal Equatos Kharul Hamd Kharul Auar, Kharl Iskadar Othma, Zara Bb Ibrahm Abstract Dervato of pot block method formula wth costat

More information

Solving Constrained Flow-Shop Scheduling. Problems with Three Machines

Solving Constrained Flow-Shop Scheduling. Problems with Three Machines It J Cotemp Math Sceces, Vol 5, 2010, o 19, 921-929 Solvg Costraed Flow-Shop Schedulg Problems wth Three Maches P Pada ad P Rajedra Departmet of Mathematcs, School of Advaced Sceces, VIT Uversty, Vellore-632

More information

Exam-style practice: A Level

Exam-style practice: A Level Exa-tye practce: A Leve a Let X dete the dtrbut ae ad X dete the dtrbut eae The dee the rad varabe Y X X j j The expected vaue Y : E( Y) EX X j j EX EX j j EX E X 7 The varace : Var( Y) VarX VarX j j Var(

More information

PTAS for Bin-Packing

PTAS for Bin-Packing CS 663: Patter Matchg Algorthms Scrbe: Che Jag /9/00. Itroducto PTAS for B-Packg The B-Packg problem s NP-hard. If we use approxmato algorthms, the B-Packg problem could be solved polyomal tme. For example,

More information

Fibonacci Identities as Binomial Sums

Fibonacci Identities as Binomial Sums It. J. Cotemp. Math. Sceces, Vol. 7, 1, o. 38, 1871-1876 Fboacc Idettes as Bomal Sums Mohammad K. Azara Departmet of Mathematcs, Uversty of Evasvlle 18 Lcol Aveue, Evasvlle, IN 477, USA E-mal: azara@evasvlle.edu

More information

Investigating Cellular Automata

Investigating Cellular Automata Researcher: Taylor Dupuy Advsor: Aaro Wootto Semester: Fall 4 Ivestgatg Cellular Automata A Overvew of Cellular Automata: Cellular Automata are smple computer programs that geerate rows of black ad whte

More information

Prof. YoginderVerma. Prof. Pankaj Madan Dean- FMS Gurukul Kangri Vishwavidyalaya, Haridwar

Prof. YoginderVerma. Prof. Pankaj Madan Dean- FMS Gurukul Kangri Vishwavidyalaya, Haridwar Paper:5, Quattatve Techques or aagemet Decsos odule:5 easures o Cetral Tedecy: athematcal Averages (A, G, H) Prcpal Ivestgator Co-Prcpal Ivestgator Paper Coordator Cotet Wrter Pro. S P Basal Vce Chacellor

More information

Arithmetic Mean and Geometric Mean

Arithmetic Mean and Geometric Mean Acta Mathematca Ntresa Vol, No, p 43 48 ISSN 453-6083 Arthmetc Mea ad Geometrc Mea Mare Varga a * Peter Mchalča b a Departmet of Mathematcs, Faculty of Natural Sceces, Costate the Phlosopher Uversty Ntra,

More information

Load Frequency Control in Interconnected Power System Using Modified Dynamic Neural Networks

Load Frequency Control in Interconnected Power System Using Modified Dynamic Neural Networks Prceedgs f the 5th Medterraea Cferece Ctrl & Autmat, July 7-9, 007, Athes - Greece 6-0 Lad Frequecy Ctrl tercected Pwer System Usg Mdfed Dyamc Neural Netwrks K.Sabah, M.A.Neku, M.eshehlab, M.Alyar ad M.Masur

More information

(b) By independence, the probability that the string 1011 is received correctly is

(b) By independence, the probability that the string 1011 is received correctly is Soluto to Problem 1.31. (a) Let A be the evet that a 0 s trasmtted. Usg the total probablty theorem, the desred probablty s P(A)(1 ɛ ( 0)+ 1 P(A) ) (1 ɛ 1)=p(1 ɛ 0)+(1 p)(1 ɛ 1). (b) By depedece, the probablty

More information

2. Independence and Bernoulli Trials

2. Independence and Bernoulli Trials . Ideedece ad Beroull Trals Ideedece: Evets ad B are deedet f B B. - It s easy to show that, B deedet mles, B;, B are all deedet ars. For examle, ad so that B or B B B B B φ,.e., ad B are deedet evets.,

More information

Econometric Methods. Review of Estimation

Econometric Methods. Review of Estimation Ecoometrc Methods Revew of Estmato Estmatg the populato mea Radom samplg Pot ad terval estmators Lear estmators Ubased estmators Lear Ubased Estmators (LUEs) Effcecy (mmum varace) ad Best Lear Ubased Estmators

More information

For combinatorial problems we might need to generate all permutations, combinations, or subsets of a set.

For combinatorial problems we might need to generate all permutations, combinations, or subsets of a set. Addtoal Decrease ad Coquer Algorthms For combatoral problems we mght eed to geerate all permutatos, combatos, or subsets of a set. Geeratg Permutatos If we have a set f elemets: { a 1, a 2, a 3, a } the

More information

ENGI 4421 Propagation of Error Page 8-01

ENGI 4421 Propagation of Error Page 8-01 ENGI 441 Propagato of Error Page 8-01 Propagato of Error [Navd Chapter 3; ot Devore] Ay realstc measuremet procedure cotas error. Ay calculatos based o that measuremet wll therefore also cota a error.

More information

Ch. 1 Introduction to Estimation 1/15

Ch. 1 Introduction to Estimation 1/15 Ch. Itrducti t stimati /5 ample stimati Prblem: DSB R S f M f s f f f ; f, φ m tcsπf t + φ t f lectrics dds ise wt usually white BPF & mp t s t + w t st. lg. f & φ X udi mp cs π f + φ t Oscillatr w/ f

More information

CHAPTER 2 Algebraic Expressions and Fundamental Operations

CHAPTER 2 Algebraic Expressions and Fundamental Operations CHAPTER Algebraic Expressins and Fundamental Operatins OBJECTIVES: 1. Algebraic Expressins. Terms. Degree. Gruping 5. Additin 6. Subtractin 7. Multiplicatin 8. Divisin Algebraic Expressin An algebraic

More information

MATH 247/Winter Notes on the adjoint and on normal operators.

MATH 247/Winter Notes on the adjoint and on normal operators. MATH 47/Wter 00 Notes o the adjot ad o ormal operators I these otes, V s a fte dmesoal er product space over, wth gve er * product uv, T, S, T, are lear operators o V U, W are subspaces of V Whe we say

More information

9 U-STATISTICS. Eh =(m!) 1 Eh(X (1),..., X (m ) ) i.i.d

9 U-STATISTICS. Eh =(m!) 1 Eh(X (1),..., X (m ) ) i.i.d 9 U-STATISTICS Suppose,,..., are P P..d. wth CDF F. Our goal s to estmate the expectato t (P)=Eh(,,..., m ). Note that ths expectato requres more tha oe cotrast to E, E, or Eh( ). Oe example s E or P((,

More information

Non-Cooperative Games

Non-Cooperative Games N-Cperatve Games a ucerta evrmet Rger J-B Wets rbwets@ucavs.eu Uverst Calra, Davs ONR-MURI, Jul 2002 p.1/?? I. Determstc Vers ONR-MURI, Jul 2002 p.2/?? Fg a Nash-equlbrum prblem rmulat the Nash-uct asscate

More information

Multiple Regression. More than 2 variables! Grade on Final. Multiple Regression 11/21/2012. Exam 2 Grades. Exam 2 Re-grades

Multiple Regression. More than 2 variables! Grade on Final. Multiple Regression 11/21/2012. Exam 2 Grades. Exam 2 Re-grades STAT 101 Dr. Kar Lock Morga 11/20/12 Exam 2 Grades Multple Regresso SECTIONS 9.2, 10.1, 10.2 Multple explaatory varables (10.1) Parttog varablty R 2, ANOVA (9.2) Codtos resdual plot (10.2) Trasformatos

More information

Evaluating Polynomials

Evaluating Polynomials Uverst of Nebraska - Lcol DgtalCommos@Uverst of Nebraska - Lcol MAT Exam Expostor Papers Math the Mddle Isttute Partershp 7-7 Evaluatg Polomals Thomas J. Harrgto Uverst of Nebraska-Lcol Follow ths ad addtoal

More information

UNIT 7 RANK CORRELATION

UNIT 7 RANK CORRELATION UNIT 7 RANK CORRELATION Rak Correlato Structure 7. Itroucto Objectves 7. Cocept of Rak Correlato 7.3 Dervato of Rak Correlato Coeffcet Formula 7.4 Te or Repeate Raks 7.5 Cocurret Devato 7.6 Summar 7.7

More information

Introduction to Electronic circuits.

Introduction to Electronic circuits. Intrductn t Electrnc crcuts. Passve and Actve crcut elements. Capactrs, esstrs and Inductrs n AC crcuts. Vltage and current dvders. Vltage and current surces. Amplfers, and ther transfer characterstc.

More information

n -dimensional vectors follow naturally from the one

n -dimensional vectors follow naturally from the one B. Vectors ad sets B. Vectors Ecoomsts study ecoomc pheomea by buldg hghly stylzed models. Uderstadg ad makg use of almost all such models requres a hgh comfort level wth some key mathematcal sklls. I

More information

Ordinary Least Squares Regression. Simple Regression. Algebra and Assumptions.

Ordinary Least Squares Regression. Simple Regression. Algebra and Assumptions. Ordary Least Squares egresso. Smple egresso. Algebra ad Assumptos. I ths part of the course we are gog to study a techque for aalysg the lear relatoshp betwee two varables Y ad X. We have pars of observatos

More information

UNIT 2 SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS

UNIT 2 SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS Numercal Computg -I UNIT SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS Structure Page Nos..0 Itroducto 6. Objectves 7. Ital Approxmato to a Root 7. Bsecto Method 8.. Error Aalyss 9.4 Regula Fals Method

More information

The Selection Problem - Variable Size Decrease/Conquer (Practice with algorithm analysis)

The Selection Problem - Variable Size Decrease/Conquer (Practice with algorithm analysis) We have covered: Selecto, Iserto, Mergesort, Bubblesort, Heapsort Next: Selecto the Qucksort The Selecto Problem - Varable Sze Decrease/Coquer (Practce wth algorthm aalyss) Cosder the problem of fdg the

More information

Ch5 Appendix Q-factor and Smith Chart Matching

Ch5 Appendix Q-factor and Smith Chart Matching h5 Appedx -factr ad mth hart Matchg 5B-1 We-ha a udwg, F rcut Desg Thery ad Applcat, hapter 8 Frequecy espse f -type Matchg Netwrks 5B- Fg.8-8 Tw desg realzats f a -type matchg etwrk.65pf, 80 f 1 GHz Fg.8-9

More information

Simple Linear Regression

Simple Linear Regression Statstcal Methods I (EST 75) Page 139 Smple Lear Regresso Smple regresso applcatos are used to ft a model descrbg a lear relatoshp betwee two varables. The aspects of least squares regresso ad correlato

More information

ANALYSIS ON THE NATURE OF THE BASIC EQUATIONS IN SYNERGETIC INTER-REPRESENTATION NETWORK

ANALYSIS ON THE NATURE OF THE BASIC EQUATIONS IN SYNERGETIC INTER-REPRESENTATION NETWORK Far East Joural of Appled Mathematcs Volume, Number, 2008, Pages Ths paper s avalable ole at http://www.pphm.com 2008 Pushpa Publshg House ANALYSIS ON THE NATURE OF THE ASI EQUATIONS IN SYNERGETI INTER-REPRESENTATION

More information

The equation is sometimes presented in form Y = a + b x. This is reasonable, but it s not the notation we use.

The equation is sometimes presented in form Y = a + b x. This is reasonable, but it s not the notation we use. INTRODUCTORY NOTE ON LINEAR REGREION We have data of the form (x y ) (x y ) (x y ) These wll most ofte be preseted to us as two colum of a spreadsheet As the topc develops we wll see both upper case ad

More information

Lecture 02: Bounding tail distributions of a random variable

Lecture 02: Bounding tail distributions of a random variable CSCI-B609: A Theorst s Toolkt, Fall 206 Aug 25 Lecture 02: Boudg tal dstrbutos of a radom varable Lecturer: Yua Zhou Scrbe: Yua Xe & Yua Zhou Let us cosder the ubased co flps aga. I.e. let the outcome

More information

Laboratory I.10 It All Adds Up

Laboratory I.10 It All Adds Up Laboratory I. It All Adds Up Goals The studet wll work wth Rema sums ad evaluate them usg Derve. The studet wll see applcatos of tegrals as accumulatos of chages. The studet wll revew curve fttg sklls.

More information

Block Cipher Cryptanalysis - Part 1

Block Cipher Cryptanalysis - Part 1 Blck Cpher Cryptaalyss - Part 1 Alessar Baregh Departmet f Electrcs, Ifrmat a Begeerg (DEIB) Pltecc Mla alessar.baregh - at - plm.t G. Pels, A. Baregh (DEIB) Blck Cpher Cryptaalyss - Part 1 1 / 37 Overvew

More information

Section l h l Stem=Tens. 8l Leaf=Ones. 8h l 03. 9h 58

Section l h l Stem=Tens. 8l Leaf=Ones. 8h l 03. 9h 58 Secto.. 6l 34 6h 667899 7l 44 7h Stem=Tes 8l 344 Leaf=Oes 8h 5557899 9l 3 9h 58 Ths dsplay brgs out the gap the data: There are o scores the hgh 7's. 6. a. beams cylders 9 5 8 88533 6 6 98877643 7 488

More information

Lecture 3 Probability review (cont d)

Lecture 3 Probability review (cont d) STATS 00: Itroducto to Statstcal Iferece Autum 06 Lecture 3 Probablty revew (cot d) 3. Jot dstrbutos If radom varables X,..., X k are depedet, the ther dstrbuto may be specfed by specfyg the dvdual dstrbuto

More information

CHAPTER 3 INEQUALITIES. Copyright -The Institute of Chartered Accountants of India

CHAPTER 3 INEQUALITIES. Copyright -The Institute of Chartered Accountants of India CHAPTER 3 INEQUALITIES Cpyright -The Institute f Chartered Accuntants f India INEQUALITIES LEARNING OBJECTIVES One f the widely used decisin making prblems, nwadays, is t decide n the ptimal mix f scarce

More information

å 1 13 Practice Final Examination Solutions - = CS109 Dec 5, 2018

å 1 13 Practice Final Examination Solutions - = CS109 Dec 5, 2018 Chrs Pech Fal Practce CS09 Dec 5, 08 Practce Fal Examato Solutos. Aswer: 4/5 8/7. There are multle ways to obta ths aswer; here are two: The frst commo method s to sum over all ossbltes for the rak of

More information

ALE 26. Equilibria for Cell Reactions. What happens to the cell potential as the reaction proceeds over time?

ALE 26. Equilibria for Cell Reactions. What happens to the cell potential as the reaction proceeds over time? Name Chem 163 Secti: Team Number: AL 26. quilibria fr Cell Reactis (Referece: 21.4 Silberberg 5 th editi) What happes t the ptetial as the reacti prceeds ver time? The Mdel: Basis fr the Nerst quati Previusly,

More information

Feedback Principle :-

Feedback Principle :- Feedback Prncple : Feedback amplfer s that n whch a part f the utput f the basc amplfer s returned back t the nput termnal and mxed up wth the nternal nput sgnal. The sub netwrks f feedback amplfer are:

More information

Chapter 8. Inferences about More Than Two Population Central Values

Chapter 8. Inferences about More Than Two Population Central Values Chapter 8. Ifereces about More Tha Two Populato Cetral Values Case tudy: Effect of Tmg of the Treatmet of Port-We tas wth Lasers ) To vestgate whether treatmet at a youg age would yeld better results tha

More information

ECONOMETRIC THEORY. MODULE VIII Lecture - 26 Heteroskedasticity

ECONOMETRIC THEORY. MODULE VIII Lecture - 26 Heteroskedasticity ECONOMETRIC THEORY MODULE VIII Lecture - 6 Heteroskedastcty Dr. Shalabh Departmet of Mathematcs ad Statstcs Ida Isttute of Techology Kapur . Breusch Paga test Ths test ca be appled whe the replcated data

More information

A tighter lower bound on the circuit size of the hardest Boolean functions

A tighter lower bound on the circuit size of the hardest Boolean functions Electroc Colloquum o Computatoal Complexty, Report No. 86 2011) A tghter lower boud o the crcut sze of the hardest Boolea fuctos Masak Yamamoto Abstract I [IPL2005], Fradse ad Mlterse mproved bouds o the

More information

Floating Point Method for Solving Transportation. Problems with Additional Constraints

Floating Point Method for Solving Transportation. Problems with Additional Constraints Internatinal Mathematical Frum, Vl. 6, 20, n. 40, 983-992 Flating Pint Methd fr Slving Transprtatin Prblems with Additinal Cnstraints P. Pandian and D. Anuradha Department f Mathematics, Schl f Advanced

More information

Application of Matrix Iteration for Determining the Fundamental Frequency of Vibration of a Continuous Beam

Application of Matrix Iteration for Determining the Fundamental Frequency of Vibration of a Continuous Beam Iteratal Jural f Egeerg Research ad Develpet e-issn: 78-67, p-issn : 78-8, www.jerd.c Vlue 4, Issue (Nveber ), PP. -6 Applcat f Matrx Iterat fr Deterg the Fudaetal Frequecy f Vbrat f a Ctuus Bea S. Sule,

More information

Lecture 16: Backpropogation Algorithm Neural Networks with smooth activation functions

Lecture 16: Backpropogation Algorithm Neural Networks with smooth activation functions CO-511: Learg Theory prg 2017 Lecturer: Ro Lv Lecture 16: Bacpropogato Algorthm Dsclamer: These otes have ot bee subected to the usual scruty reserved for formal publcatos. They may be dstrbuted outsde

More information

CIS 800/002 The Algorithmic Foundations of Data Privacy October 13, Lecture 9. Database Update Algorithms: Multiplicative Weights

CIS 800/002 The Algorithmic Foundations of Data Privacy October 13, Lecture 9. Database Update Algorithms: Multiplicative Weights CIS 800/002 The Algorthmc Foudatos of Data Prvacy October 13, 2011 Lecturer: Aaro Roth Lecture 9 Scrbe: Aaro Roth Database Update Algorthms: Multplcatve Weghts We ll recall aga) some deftos from last tme:

More information

Assignment 5/MATH 247/Winter Due: Friday, February 19 in class (!) (answers will be posted right after class)

Assignment 5/MATH 247/Winter Due: Friday, February 19 in class (!) (answers will be posted right after class) Assgmet 5/MATH 7/Wter 00 Due: Frday, February 9 class (!) (aswers wll be posted rght after class) As usual, there are peces of text, before the questos [], [], themselves. Recall: For the quadratc form

More information

MECH6661 lectures 10/1 Dr. M. Medraj Mech. Eng. Dept. - Concordia University

MECH6661 lectures 10/1 Dr. M. Medraj Mech. Eng. Dept. - Concordia University Outle Revew ublattce Mdel Example ublattce Mdel Thermdyamc Mdelg Itrduct Example lly Desg Terary Phase Dagrams Gbbs Phase Rule Thermdyamcs f Multcmpet ystems Mech. Eg. Dept. - crda Uversty Revew: ublattce

More information

5 Short Proofs of Simplified Stirling s Approximation

5 Short Proofs of Simplified Stirling s Approximation 5 Short Proofs of Smplfed Strlg s Approxmato Ofr Gorodetsky, drtymaths.wordpress.com Jue, 20 0 Itroducto Strlg s approxmato s the followg (somewhat surprsg) approxmato of the factoral,, usg elemetary fuctos:

More information

Physics 114 Exam 2 Fall Name:

Physics 114 Exam 2 Fall Name: Physcs 114 Exam Fall 015 Name: For gradg purposes (do ot wrte here): Questo 1. 1... 3. 3. Problem Aswer each of the followg questos. Pots for each questo are dcated red. Uless otherwse dcated, the amout

More information

K [f(t)] 2 [ (st) /2 K A GENERALIZED MEIJER TRANSFORMATION. Ku(z) ()x) t -)-I e. K(z) r( + ) () (t 2 I) -1/2 e -zt dt, G. L. N. RAO L.

K [f(t)] 2 [ (st) /2 K A GENERALIZED MEIJER TRANSFORMATION. Ku(z) ()x) t -)-I e. K(z) r( + ) () (t 2 I) -1/2 e -zt dt, G. L. N. RAO L. Iterat. J. Math. & Math. Scl. Vl. 8 N. 2 (1985) 359-365 359 A GENERALIZED MEIJER TRANSFORMATION G. L. N. RAO Departmet f Mathematics Jamshedpur C-perative Cllege f the Rachi Uiversity Jamshedpur, Idia

More information

SIMULATION OF THREE PHASE THREE LEG TRANSFORMER BEHAVIOR UNDER DIFFERENT VOLTAGE SAG TYPES

SIMULATION OF THREE PHASE THREE LEG TRANSFORMER BEHAVIOR UNDER DIFFERENT VOLTAGE SAG TYPES SIMULATION OF THREE PHASE THREE LEG TRANSFORMER BEHAVIOR UNDER DIFFERENT VOLTAGE SAG TYPES Mhammadreza Dlatan Alreza Jallan Department f Electrcal Engneerng, Iran Unversty f scence & Technlgy (IUST) e-mal:

More information

ENGI 4430 Numerical Integration Page 5-01

ENGI 4430 Numerical Integration Page 5-01 ENGI 443 Numercal Itegrato Page 5-5. Numercal Itegrato I some o our prevous work, (most otaly the evaluato o arc legth), t has ee dcult or mpossle to d the dete tegral. Varous symolc algera ad calculus

More information

What regression does. so β. β β β

What regression does. so β. β β β Sect Smple Regress What regress des Relatshp Ofte ecmcs we beleve that there s a (perhaps causal) relatshp betwee tw varables Usually mre tha tw, but that s deferred t ather day Frm Is the relatshp lear?

More information

GENERALIZATIONS OF CEVA S THEOREM AND APPLICATIONS

GENERALIZATIONS OF CEVA S THEOREM AND APPLICATIONS GENERLIZTIONS OF CEV S THEOREM ND PPLICTIONS Floret Smaradache Uversty of New Mexco 200 College Road Gallup, NM 87301, US E-mal: smarad@um.edu I these paragraphs oe presets three geeralzatos of the famous

More information

Kernels. Nuno Vasconcelos ECE Department, UCSD

Kernels. Nuno Vasconcelos ECE Department, UCSD Kerels Nu Vasels ECE Departmet UCSD Prpal mpet aalyss Dmesalty reut: Last tme we saw that whe the ata lves a subspae t s best t esg ur learg algrthms ths subspae D subspae 3D y φ φ λ λ y ths a be e by

More information

best estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best

best estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best Error Aalyss Preamble Wheever a measuremet s made, the result followg from that measuremet s always subject to ucertaty The ucertaty ca be reduced by makg several measuremets of the same quatty or by mprovg

More information

Equilibrium corporate nance

Equilibrium corporate nance Equlbrum crprate ace Albert s NYU Per Gttard EUI Gud Ruta NYU ad EUI Octber 2, 2009 Abstract We study a geeral equlbrum mdel wth prduct where acal markets are cmplete. I ths evrmet rms crprate acg decss

More information