ON RANKING OF ALTERNATIVES IN UNCERTAIN GROUP DECISION MAKING MODEL

Size: px
Start display at page:

Download "ON RANKING OF ALTERNATIVES IN UNCERTAIN GROUP DECISION MAKING MODEL"

Transcription

1 IJRRAS (3) Ju _5.pdf ON RANKING OF ALRNAIVS IN UNCRAIN GROUP DCISION MAKING MODL Chao Wag * & Lag L Gul Uvrty of chology Gul 544 Cha * mal: wagchao244@63.com llag6666@26.com ABSRAC h wghtd gomtrc ma ordrg vctor ad logarthmc lat quar ar propod to aggrgat formato of dco mar to grat wght vctor from ucrtaty comparo matrc ucrta group AHP modl. pcally wh th judgmt matrc ar complt th lattr ca b dvlopd to drv prort udr th codto of mmal dvato. Som cocpt ucrtaty thory ar troducd ad th ucrtaty dtrbuto ar aumd to b lar. wo umrcal xampl ar xamd to how th applcato of th propod mthod. Kyword: Group dco mag AHP Ucrtaty thory Ucrta AHP Ucrta group AHP.. INRODUCION h Aaly Hrarchy Proc (AHP) a multcrtra dco mag (MCDM) tchqu propod by Saaty [] that grat th rlatv wght of crtra. h advatag of th MCDM tool t capablty of hadlg ubjctv crtra ad vagu judgmt th dco mag proc. Dco mag problm th proc of fdg th bt choc from all of th altratv. I almot all uch problm th multplcty of crtra for judgg th attrbut prvav. For may uch problm th xprt wat to olv a multpl crtra dco mag (MCDM) problm. A MCDM problm ca b cocly xprd matrx format a D A x x x A x x x A x x x whr A A2 A ar pobl altratv amog whch dco mar hav to choo 2 th dgr of prfrc of attrbut rato cal wth rpct to crtro C 2 m x j 9 9 h rarch wa upportd by th Guagx Natural Scc Foudato of Cha udr th Grat NO. 2GXNSFA849; Iovato Projct of Guagx Graduat ducato udr th Grat No M A ovr attrbut. A j whch ta from th A w all ow t mportat to gv th judgmt of o attrbut ovr aothr th applcato of AHP. I clacal MCDM mthod w alway codrd that thr wa o dco mar who gav th comparo matrc. Howvr ral-lf tuato vral or may dco mar wll partcpat th proc of dco mag ad occaoally th judgmt matrc gv by dco mar ar complt. W call th two ca group dco mag ad complt judgmt matrc. Morovr du to th complxty ad ucrtaty volvd ral-world dco problm ad hrt ubjctv atur of huma prfrc judgmt t alway uraltc ad fabl to obta xact rato x j 2. h th mthod ug fuzzy trval or ucrta judgmt for part or all of th judgmt a parw comparo matrx ar mployd aturally. A umbr of tchqu hav b dvlopd to u uch a fuzzy trval or ucrta comparo matrx to drv wght. I th followg cto w focu o th rag of altratv ucrta group dco mag modl udr complt ad complt formato vromt. I clacal AHP Wag ad Xu [2] propod vral mthod for group dco mag modl to obta rag vctor of altratv uch a gvalu mthod [3] logarthmc lat quar [4] ad mmum dvato mthod. Wth th mthod thy ca drv th wght vctor ad gav th codto of ra prrvato. Bcau of th complxty ad ucrtaty of ral-world dco aaly problm ad th ubjctvty of huma judgmt Ch [5] mployd xto of th OPSIS (tchqu for ordr prformac by mlarty to dal oluto) u a lgutc dco proc to olv th multpl crtra dco mag problm udr fuzzy vromt. Ucrtaty thory propod by Lu [6] 27 ad rfd by Lu [7] 2 provd a w approach to dal wth o-dtrmtc factor. Ad mor dtal ca b obtad h wor [8]. Wag [9 ] ubqutly

2 IJRRAS (3) Ju 22 Wag & L Altratv Ucrta Group Dco Mag troducd th ucrta varabl to AHP ad provdd th UVM (ucrta varabl mthod) to grat th wght vctor wth th ucrta maur. It appld to o xprt ytm of AHP. I th proc of dco mag ot all th judgmt ca b gv actually om of thm mg bcau of lacg formato. So th ca th matrc gv by xprt ar complt. W call thm complt comparo matrc. Study o th rag of altratv bad o complt comparo matrx th problm of drvg rag ordr udr complt formato vromt. I th papr w furthr xtdd th UVM to dvlop a mthodology for olvg mult-pro mult-crtra dco mag problm ucrta vromt. Codrg th ucrtaty th dco data ad group dco mag proc wh xpctd ucrta varabl ar ud to a th mportac of o attrbut ovr aothr wth rpct to ach crtro w ca covrt th ucrtaty comparo matrx [8] to a clacal judgmt matrx ad th mthod gvalu mthod ad logarthmc lat quar mtod abov ca b ud. Fally wght vctor thr udr complt or complt formato vromt drvd aturally. h papr orgazd a follow. I Scto II ucrtaty thory frt troducd mpl word ad om bac cocpt ad proprt ar gv ad algorthm for clacal AHP ar dcud. Scto III th ma part of th papr ucrta group AHP olvd ug th propod mthod. Scto IV prt two umrcal xampl to how th applcato o ucrta group AHP. Fally th cocluo ar provdd Scto V. 2. PRLIMINARIS 2.. Ucrtaty hory ad Ucrta AHP I th followg w brfly rvw om bac dfto of ucrtaty thory ad ucrta AHP. h bac dfto ad otato blow wll b ud throughout th papr utl othrw tatd. Dfto. (Lu [6]) A ucrta varabl a maurabl fucto from a ucrtaty pac LM to th t of ral umbr.. for ay Borl t of ral umbr th t B B a vt. Dfto 2. (Lu [8]) h ucrtaty dtrbuto of a ucrta varabl dfd by for ay ral umbr x. Lar ucrtaty dtrbuto ab L gv by x M x f x a x x a b a f a x b f x b whr a ad b ar ral umbr wth a b ( Fgur.). Dfto 3. (Lu [6]) A ucrtaty dtrbuto ad to b rgular f t vr fucto xt ad uqu for ach. xpctd valu th avrag valu of ucrta varabl th of ucrta maur ad rprt th z of ucrta varabl. Dfto 4. (Lu [6]) Lt b a ucrta varabl. h th xpctd valu of dfd by M M provdd that at lat o of th two tgral ft. r dr r dr. Dfto 5. (Lu [6]) Lt b a ucrta varabl wth rgular ucrtaly dtrbuto. h th vr fucto calld th vr ucrtaty dtrbuto of. 387

3 IJRRAS (3) Ju 22 Wag & L Altratv Ucrta Group Dco Mag (x) a b x Fgur. A Lar Ucrtaty Dtrbuto. horm. (Lu [8]) Lt b a ucrta varabl wth ucrtaty dtrbuto. If th xpctd valu xt th Lt ab x dx x dx. L b a lar ucrta varabl. h t vr ucrtaty dtrbuto a b ad t xpctd valu [8] a b a bd. () 2 Dfto 6. (Wag [9]) Lt A b a ucrtaty comparo matrx of ordr who tr ar ucrtaty dtrbuto 2 ; j of ucrtaty rato 2 ; j th th ucrtaty comparo dotd by Sc ucrtaty dtrbuto A ; j of vr ucrtaty rato j x j x 2 ; j caot b aly dtrmd th of Dfto 2. So th papr w jut d to obta th ucrtaty dtrbuto of uppr tragular ucrtaty rato th lowr tragular ucrtaty rato ca b obtad by j whr th ucrta maur (cofdc lvl) wth. 2 ; j 388

4 IJRRAS (3) Ju 22 Wag & L Altratv Ucrta Group Dco Mag Dfto 7. W call a matrx A a complt comparo matrx f thr o rato a ca ot b dtrmd dotd a. Appartly a. j 2.2. Mthod for Group Dco Mag (Group AHP) hr ar may wor [4 2] o group AHP ad a umbr of tchqu hav b dvlopd to u uch a comparo matrx to drv wght vctor. h mthod of wghtd gomtrc ma ordrg vctor ad logarthmc lat quar ar troducd hr. Lt b th umbr of xprt (dco mar) ad D D2 D b th comparo matrc rpctvly D d. Aftr chcg th cotcy w drv th wght vctor ug th Rght whr 2 gvctor Mthod (VM uch a (2)) Dw w (2) whr D d th potv parw comparo matrx who tr ar cho th t of valu max ud to tt th cotcy of D ad w w w w 2 th wght vctor of th attrbut w d wh t ormalzd. For ach xprt th ordrg vctor 2 w w w w 2 ad th comprhv wghtd gomtrc ma ordrg vctor w w w2 w whr. h w ca calculat th tadard dvato ad th tadard dvato w w 2 w j j 2 (3) w w w 2 w 2 (4) j of w j w w 2 (5) j j j of th w comprhv comparo matrx D d w wj d d 2 (6) from whch th fdbac formato ca b obtad by xprt for modfcato. Lootma propod logarthmc lat quar to hadl th tuato of group judgmt ad complt judgmt D d mxd. If 2 wth a complt comparo matrx th j d 2 w wj m log log. w w w2 w atfd (7) 3. H PROPOSD MHOD A wll ow whatvr th mthod of wghtd gomtrc ma ordrg vctor or logarthmc lat quar thy apply to th matrc whch tr ar xact. Wh w cotruct th ucrtaty comparo matrc th of Dfto 6 th lmt ar ucrtaty dtrbuto dotd L ab. h rato ar ucrta varabl. 389

5 IJRRAS (3) Ju 22 Wag & L Altratv Ucrta Group Dco Mag 3.. Complt Iformato vromt h propod th mthod ca ot b ud o th ucrtaty comparo matrc. Howvr from th Dfto 4 th xpctd valu of ucrta varabl a xact umbr ad ca b aly calculatd ug th horm. Subqutly th w comparo matrx xprd a whr th xpctd rato A A D A j ad I ordr to obta th approxmat valu of gvctor (wght vctor) w ormalz th comparo matrx ug th formula j w 2. j Accordg to q. (3) ad (9) th compot wght vctor (8) D (9) w w w2 w ca b computd aturally. What mor cotcy rato CR.. ad th q. (5) ad (6) wll rflct th formato of comparo matrx Icomplt Iformato vromt I ucrtaty thory vr ucrtaty dtrbuto ca traform a ucrta varabl to a xact umbr wth ucrta maur. W ow that a xact umbr ad.5 wll ta. Lt.5 f thr ar lmt.5 d D th mot pobl valu 2 th complt comparo matrx udr th complt formato vromt. So q. (7) xprd a j whr a b.5 If th x w w w j x x 2 w wj m log.5 log. D th () a b of horm. 2 x y l.5 ad x j. h formula (7) rprtd by From how abov w drv th quato t j f d f d y x x 2 m. j 39

6 IJRRAS (3) Ju 22 Wag & L Altratv Ucrta Group Dco Mag It ca b mplfd a x j j j j x y 2. I x I x y 2 () j j whr I th umbr of lmt whch ca b judgd th th row whch gv ad y th logarthmc um of judgd lmt th th row. I rprt th umbr of d Accordg to q. () w ca obta th oluto of x 2 2 drvd from x. hrfor accordg to th wght vctor w ca dtrm th rag ordr of all altratv ad lct th bt o from amog a t of fabl altratv. I a word a algorthm of th mult-pro multcrtra dco mag wth ucrta varabl approach gv th followg. Stp : Form a group of dco mar ad th dtfy th valuato crtra. Stp 2: Judg th rlatv mportac attrbut A ovr attrbut A ad cotruct th complt or complt ad ubqutly comparo matrc. Stp 3: Comput th xpctd valu of ach ucrta varabl ad cotruct th matrx a (8). j w w w w Stp 4: If t udr complt formato vromt calculat w w w ug (9) (4) ad (3) rpctvly ad th th wght vctor w obtad. Stp 5: Solv th quato t ug () f t udr complt formato vromt. Stp 6: Calculat th wght vctor Stp 7: Comput th tadard dvato w w w2 w th rag ordr of all altratv ca b dtrmd. of th w comprhv comparo matrx D d w wj. If t accptabl th w lct th bt o from amog a t of fabl altratv. Othrw th dco mar hav to modfy th matrc. h proc how Fgur 2. Ucrtaty Comparo Matrc Y Complt Iformato? No Wghtd Gomtrc Ma Ordrg Vctor Logarthmc Lat Squar Y Wght Vctor Accptabl? No Modfcato Fgur 2. Proc for gratg prort from group dco modl. 39

7 IJRRAS (3) Ju 22 Wag & L Altratv Ucrta Group Dco Mag 4. NUMRICAL XAMPLS I th cto w offr two umrcal xampl that ar xamd ug th propod mthod ad how thr applcato. h ucrtaty dtrbuto ar obtad by Dlph mthod [8]. xampl. I a proc of dco mag thr dco mar gav th ucrtaty comparo matrc blow D L 3 L L D3 L D 2 L 2 L L L. 23 L W ow that t udr complt formato vromt bcau thr o lmt. Followg th tp w ca gt th w comparo matrc a (8) ug (). Accordg to q. (9) thr group of wght vctor ar obtad w w w Aum that a dco mar a mportat a aothr 2 3 wght vctor w. h tadard dvato comparo matrx D d w wj max.875. ug (4) ad (5) th fal compot of th w comprhv whr 2 Appartly th bt opto th frt attrbut. Ad w codr t accptabl du to th maxmum dvato mall. It wll play a mportat rol wh th rag ordr gv by vry xprt ar dffrt. xampl 2. hr wr thr xprt lctg th bt opto from fv altratv ad provdg th ucrtaty comparo matrc 3 L L L3 L23 24 D L 34 L3 4 2 D2 L

8 IJRRAS (3) Ju 22 Wag & L Altratv Ucrta Group Dco Mag D 3 34 L L. Obvouly t udr th complt formato vromt bcau thr ar lmt. Ug th formula () th thr matrc ca b xpr a Accordg to () th quato t ad t umrcal oluto Fally bcau of x w D 2 7x x2 2x3 3x4 x5.328 x 3x2 x3 x x x2 5x3 2x x x 2x 7x x x x4 x x w j x x j x x th wght vctor w h rag of altratv ucrta group dco mag udr complt vromt problm olvd. 5. CONCLUSIONS I multpl crtra dco aaly problm huma judgmt ar rqurd ordr to grat rlatv wght of crtra. Du to ucrta ad mprc dcrpto of ral world dco problm ad ubjctv atur of huma judgmt ucrtaty thory ca provd a mor raltc framwor to xpla uch ucrtaty. Aftr bg llutratd by a proal lcto problm [9] th mthod to olv th mult- pro multcrtra dco mag problm udr complt formato vromt ar propod th papr. I group AHP proc vry oft th amt of altratv wth rpct to crtra ad th mportac wght ar utabl to u th ucrta varabl tad of prc umbr. I fact th ucrta varabl ad ucrtaty dtrbuto ca b ud to dcrb judgmt aly. h y that aggrgat ach dco mar formato to drv th bt lcto. Hr w propo wghtd gomtrc ma ordrg vctor whch ffctv ad mpl to grat wght vctor aggrgat formato ad calculat dvato udr complt formato vromt. I addto th logarthmc lat quar xtdd to ucrta group AHP udr th codto of complt formato vromt. I ordr to apply th mthod xpctd valu of ucrta varabl ad vr ucrtaty dtrbuto ar ud to traform a ucrtaty comparo matrx to a xact matrx. Fally th wght vctor obtad aturally. Howvr how to aggrgat th formato of mult-hrarchy tll ubjct to furthr vtgato.. 393

9 IJRRAS (3) Ju 22 Wag & L Altratv Ucrta Group Dco Mag 6. ACKNOWLDGMNS h author wh to tha Lag L th profor of Gul Uvrty of chology Cha for hr tructo ad upport; ad Huad Sh th matr of Uvrty of Scc ad chology Bg for hr couragmt ad upport. h wor ha b upportd by Gul Uvrty of chology. 7. RFRNCS []..L. Saaty A calg mthod for prort hrarchcal tructur Joural of Mathmatcal Pychology 5 pp (977). [2]. L.F. Wag S.B. Xu h Itroducto about th Aalytc Hrarchy Proc Cha Rm Uvrty Pr pp (99). [3]. P.. Harr L.G. Varga hory of rato cal tmato Saaty aalytc hrarchy Proc Dpartmt of Dco Scc (985). [4]. F.A. Lootma Prformac valuato of o-lar optmzato mthod va multcrtra dco aaly ad va lar modl aaly Acadmc Pr Lodo pp (98). [5]. C.. Ch xto of th OPSIS for group dco-mag udr fuzzy vromt Fuzzy St ad Sytm 4 pp. -9 (2). [6]. B. Lu Ucrtaty hory 2d d. Sprgr-Vrlag Brl (27). [7]. B. Lu Ucrtaty hory: A Brach of Mathmatc for Modlg Huma Ucrtaty Sprgr-Vrlag Brl (2). [8]. B. Lu Ucrtaty hory 4d d. Sprgr-Vrlag Brl (2). [9]. C. Wag L. L S. Wu A ucrta dco mag modl: A applcato to AHP Itratoal Joural of Advacd Rarch Computr Scc Vol. 3 No. 2 Aprl (22). []. C. Wag L. L J.J. Lu Ucrtaty wght grato approach bad o ucrtaty comparo matrc Appld Mathmatc Vol. 3 No. 5 May (22). []..L. Saaty h Aalytc Hrarchy Proc (988). [2]. J.M.D.S. Nv Motorg Cotcy Group Dco Mag: A mprcal Study of th Aalytc Hrarchy Proc (gvctor Prortzato Modl) Uvrty of Pylvaa pp. 233 (984). 394

On Ranking of Alternatives in Uncertain Group Decision Making Model

On Ranking of Alternatives in Uncertain Group Decision Making Model H COMPUING SCINC AND CHNOLOGY INRNAIONAL JOURNAL VOL NO 3 Aprl 22 ISSN (Prt) 262-66 ISSN (Ol) 262-687 Publhd ol Aprl 22 (http://wwwrarchpuborg/oural/c) 3 O Rag of Altratv Ucrta Group Dco Mag Modl Chao

More information

3.4 Properties of the Stress Tensor

3.4 Properties of the Stress Tensor cto.4.4 Proprts of th trss sor.4. trss rasformato Lt th compots of th Cauchy strss tsor a coordat systm wth bas vctors b. h compots a scod coordat systm wth bas vctors j,, ar gv by th tsor trasformato

More information

Course 10 Shading. 1. Basic Concepts: Radiance: the light energy. Light Source:

Course 10 Shading. 1. Basic Concepts: Radiance: the light energy. Light Source: Cour 0 Shadg Cour 0 Shadg. Bac Coct: Lght Sourc: adac: th lght rg radatd from a ut ara of lght ourc or urfac a ut old agl. Sold agl: $ # r f lght ourc a ot ourc th ut ara omttd abov dfto. llumato: lght

More information

International Journal in Foundations of Computer Science & Technology (IJFCST) Vol.8, No.3, May Amin Ghodousian *

International Journal in Foundations of Computer Science & Technology (IJFCST) Vol.8, No.3, May Amin Ghodousian * AN ALGORIHM OR SOLVING LINEAR OPIMIZAION PROBLEMS SUBJECE O HE INERSECION O WO UZZY RELAIONAL INEQUALIIES EINE BY RANK AMILY O -NORMS Am Ghodoua aculty of Egrg Scc, Collg of Egrg, Uvrty of hra, POBox 365-4563,

More information

THE BALANCED CREDIBILITY ESTIMATORS WITH MULTITUDE CONTRACTS OBTAINED UNDER LINEX LOSS FUNCTION

THE BALANCED CREDIBILITY ESTIMATORS WITH MULTITUDE CONTRACTS OBTAINED UNDER LINEX LOSS FUNCTION Joural of Stattc: Advac Thory ad Applcato Volum 4 Numbr 5 Pag - Avalabl at http://ctfcadvac.co. DOI: http://dx.do.org/.864/jata_746 THE BALANCED CREDIBILITY ESTIMATORS WITH MULTITUDE CONTRACTS OBTAINED

More information

Chiang Mai J. Sci. 2014; 41(2) 457 ( 2) ( ) ( ) forms a simply periodic Proof. Let q be a positive integer. Since

Chiang Mai J. Sci. 2014; 41(2) 457 ( 2) ( ) ( ) forms a simply periodic Proof. Let q be a positive integer. Since 56 Chag Ma J Sc 0; () Chag Ma J Sc 0; () : 56-6 http://pgscccmuacth/joural/ Cotrbutd Papr Th Padova Sucs Ft Groups Sat Taș* ad Erdal Karaduma Dpartmt of Mathmatcs, Faculty of Scc, Atatürk Uvrsty, 50 Erzurum,

More information

Independent Domination in Line Graphs

Independent Domination in Line Graphs Itratoal Joural of Sctfc & Egrg Rsarch Volum 3 Issu 6 Ju-1 1 ISSN 9-5518 Iddt Domato L Grahs M H Muddbhal ad D Basavarajaa Abstract - For ay grah G th l grah L G H s th trscto grah Thus th vrtcs of LG

More information

Estimating the Variance in a Simulation Study of Balanced Two Stage Predictors of Realized Random Cluster Means Ed Stanek

Estimating the Variance in a Simulation Study of Balanced Two Stage Predictors of Realized Random Cluster Means Ed Stanek Etatg th Varac a Sulato Study of Balacd Two Stag Prdctor of Ralzd Rado Clutr Ma Ed Stak Itroducto W dcrb a pla to tat th varac copot a ulato tudy N ( µ µ W df th varac of th clutr paratr a ug th N ulatd

More information

Network reliability importance measures : combinatorics and Monte Carlo based computations

Network reliability importance measures : combinatorics and Monte Carlo based computations 7th WSEAS Itratoal Cofrc o APPLIED COMPUTER SCIENCE, Vc, Italy, Novmr 2-2, 2007 88 Ntwork rlalty mportac maur : comatorc ad Mot Carlo ad computato ILYA GERTSAKH Dpartmt of Mathmatc Guro Uvrty PO 65 r Shva

More information

Total Prime Graph. Abstract: We introduce a new type of labeling known as Total Prime Labeling. Graphs which admit a Total Prime labeling are

Total Prime Graph. Abstract: We introduce a new type of labeling known as Total Prime Labeling. Graphs which admit a Total Prime labeling are Itratoal Joural Of Computatoal Egrg Rsarch (crol.com) Vol. Issu. 5 Total Prm Graph M.Rav (a) Ramasubramaa 1, R.Kala 1 Dpt.of Mathmatcs, Sr Shakth Isttut of Egrg & Tchology, Combator 641 06. Dpt. of Mathmatcs,

More information

Introduction to logistic regression

Introduction to logistic regression Itroducto to logstc rgrsso Gv: datast D { 2 2... } whr s a k-dmsoal vctor of ral-valud faturs or attrbuts ad s a bar class labl or targt. hus w ca sa that R k ad {0 }. For ampl f k 4 a datast of 3 data

More information

LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES

LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES LECTURE 6 TRANSFORMATION OF RANDOM VARIABLES TRANSFORMATION OF FUNCTION OF A RANDOM VARIABLE UNIVARIATE TRANSFORMATIONS TRANSFORMATION OF RANDOM VARIABLES If s a rv wth cdf F th Y=g s also a rv. If w wrt

More information

Different types of Domination in Intuitionistic Fuzzy Graph

Different types of Domination in Intuitionistic Fuzzy Graph Aals of Pur ad Appld Mathmatcs Vol, No, 07, 87-0 ISSN: 79-087X P, 79-0888ol Publshd o July 07 wwwrsarchmathscorg DOI: http://dxdoorg/057/apama Aals of Dffrt typs of Domato Itutostc Fuzzy Graph MGaruambga,

More information

Comparisons of the Variance of Predictors with PPS sampling (update of c04ed26.doc) Ed Stanek

Comparisons of the Variance of Predictors with PPS sampling (update of c04ed26.doc) Ed Stanek Coparo o th Varac o Prdctor wth PPS aplg (updat o c04d6doc Ed Sta troducto W copar prdctor o a PSU a or total bad o PPS aplg Th tratgy to ollow that o Sta ad Sgr (JASA, 004 whr w xpr th prdctor a a lar

More information

Bayesian Shrinkage Estimator for the Scale Parameter of Exponential Distribution under Improper Prior Distribution

Bayesian Shrinkage Estimator for the Scale Parameter of Exponential Distribution under Improper Prior Distribution Itratoal Joural of Statstcs ad Applcatos, (3): 35-3 DOI:.593/j.statstcs.3. Baysa Shrkag Estmator for th Scal Paramtr of Expotal Dstrbuto udr Impropr Pror Dstrbuto Abbas Najm Salma *, Rada Al Sharf Dpartmt

More information

Reliability of time dependent stress-strength system for various distributions

Reliability of time dependent stress-strength system for various distributions IOS Joural of Mathmatcs (IOS-JM ISSN: 78-578. Volum 3, Issu 6 (Sp-Oct., PP -7 www.osrjourals.org lablty of tm dpdt strss-strgth systm for varous dstrbutos N.Swath, T.S.Uma Mahswar,, Dpartmt of Mathmatcs,

More information

Unbalanced Panel Data Models

Unbalanced Panel Data Models Ubalacd Pal Data odls Chaptr 9 from Baltag: Ecoomtrc Aalyss of Pal Data 5 by Adrás alascs 4448 troducto balacd or complt pals: a pal data st whr data/obsrvatos ar avalabl for all crosssctoal uts th tr

More information

Aotomorphic Functions And Fermat s Last Theorem(4)

Aotomorphic Functions And Fermat s Last Theorem(4) otomorphc Fuctos d Frmat s Last Thorm(4) Chu-Xua Jag P. O. Box 94 Bg 00854 P. R. Cha agchuxua@sohu.com bsract 67 Frmat wrot: It s mpossbl to sparat a cub to two cubs or a bquadrat to two bquadrats or gral

More information

A Multi-granular Linguistic Promethee Model

A Multi-granular Linguistic Promethee Model A Mult-graular Lgustc Promth Modl Nsr Haloua, Lus Martíz, Habb Chabchoub, Ja-Marc Martl, Ju Lu 4 Uvrsty of Ecoomc Sccs ad Maagmt, Sfax, Tusa, Uvrsty of Jaé, Spa, Uvrsty of Laval, Caada, 4 Uvrsty of Ulstr,

More information

Almost all Cayley Graphs Are Hamiltonian

Almost all Cayley Graphs Are Hamiltonian Acta Mathmatca Sca, Nw Srs 199, Vol1, No, pp 151 155 Almost all Cayly Graphs Ar Hamltoa Mg Jxag & Huag Qogxag Abstract It has b cocturd that thr s a hamltoa cycl vry ft coctd Cayly graph I spt of th dffculty

More information

Department of Mathematics and Statistics Indian Institute of Technology Kanpur MSO202A/MSO202 Assignment 3 Solutions Introduction To Complex Analysis

Department of Mathematics and Statistics Indian Institute of Technology Kanpur MSO202A/MSO202 Assignment 3 Solutions Introduction To Complex Analysis Dpartmt of Mathmatcs ad Statstcs Ida Isttut of Tchology Kapur MSOA/MSO Assgmt 3 Solutos Itroducto To omplx Aalyss Th problms markd (T) d a xplct dscusso th tutoral class. Othr problms ar for hacd practc..

More information

In 1991 Fermat s Last Theorem Has Been Proved

In 1991 Fermat s Last Theorem Has Been Proved I 99 Frmat s Last Thorm Has B Provd Chu-Xua Jag P.O.Box 94Bg 00854Cha Jcxua00@s.com;cxxxx@6.com bstract I 67 Frmat wrot: It s mpossbl to sparat a cub to two cubs or a bquadrat to two bquadrats or gral

More information

Saw-Dmss Model For Intuitionistic Fuzzy Multi Attribute Decision Making Problems

Saw-Dmss Model For Intuitionistic Fuzzy Multi Attribute Decision Making Problems Itratoal Joural o Rct ad Iovato Trds Computg ad Commucato ISSN: 232-869 Saw-Dmss Modl For Itutostc Fuzzy Mult ttrbut Dcso Mag Problms V. Thagarasu ssocat Profssor of Computr Scc Gob rts & Scc Collg Gobchttpalayam,

More information

Suzan Mahmoud Mohammed Faculty of science, Helwan University

Suzan Mahmoud Mohammed Faculty of science, Helwan University Europa Joural of Statstcs ad Probablty Vol.3, No., pp.4-37, Ju 015 Publshd by Europa Ctr for Rsarch Trag ad Dvlopmt UK (www.ajourals.org ESTIMATION OF PARAMETERS OF THE MARSHALL-OLKIN WEIBULL DISTRIBUTION

More information

Three-Dimensional Theory of Nonlinear-Elastic. Bodies Stability under Finite Deformations

Three-Dimensional Theory of Nonlinear-Elastic. Bodies Stability under Finite Deformations Appld Mathmatcal Sccs ol. 9 5 o. 43 75-73 HKAR Ltd www.m-hkar.com http://dx.do.org/.988/ams.5.567 Thr-Dmsoal Thory of Nolar-Elastc Bods Stablty udr Ft Dformatos Yu.. Dmtrko Computatoal Mathmatcs ad Mathmatcal

More information

COMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES

COMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES COMPLEX NUMBERS AND ELEMENTARY FUNCTIONS OF COMPLEX VARIABLES DEFINITION OF A COMPLEX NUMBER: A umbr of th form, whr = (, ad & ar ral umbrs s calld a compl umbr Th ral umbr, s calld ral part of whl s calld

More information

Ordinary Least Squares at advanced level

Ordinary Least Squares at advanced level Ordary Last Squars at advacd lvl. Rvw of th two-varat cas wth algbra OLS s th fudamtal tchqu for lar rgrssos. You should by ow b awar of th two-varat cas ad th usual drvatos. I ths txt w ar gog to rvw

More information

Notation for Mixed Models for Finite Populations

Notation for Mixed Models for Finite Populations 30- otato for d odl for Ft Populato Smpl Populato Ut ad Rpo,..., Ut Labl for,..., Epctd Rpo (ovr rplcatd maurmt for,..., Rgro varabl (Luz r for,...,,,..., p Aular varabl for ut (Wu z μ for,...,,,..., p

More information

Introduction to logistic regression

Introduction to logistic regression Itroducto to logstc rgrsso Gv: datast D {... } whr s a k-dmsoal vctor of ral-valud faturs or attrbuts ad s a bar class labl or targt. hus w ca sa that R k ad {0 }. For ampl f k 4 a datast of 3 data pots

More information

Repeated Trials: We perform both experiments. Our space now is: Hence: We now can define a Cartesian Product Space.

Repeated Trials: We perform both experiments. Our space now is: Hence: We now can define a Cartesian Product Space. Rpatd Trals: As w hav lood at t, th thory of probablty dals wth outcoms of sgl xprmts. I th applcatos o s usually trstd two or mor xprmts or rpatd prformac or th sam xprmt. I ordr to aalyz such problms

More information

ESTIMATION OF RELIABILITY IN MULTICOMPONENT STRESS-STRENGTH BASED ON EXPONENTIATED HALF LOGISTIC DISTRIBUTION

ESTIMATION OF RELIABILITY IN MULTICOMPONENT STRESS-STRENGTH BASED ON EXPONENTIATED HALF LOGISTIC DISTRIBUTION Joural of Stattc: Advac Thor ad Applcato Volu 9 Nubr 03 Pag 9-35 ESTIMATION OF RELIABILITY IN MULTICOMPONENT STRESS-STRENGTH BASED ON EXPONENTIATED HALF LOGISTIC DISTRIBUTION G. SRINIVASA RAO ad CH. RAMESH

More information

Tolerance Interval for Exponentiated Exponential Distribution Based on Grouped Data

Tolerance Interval for Exponentiated Exponential Distribution Based on Grouped Data Itratoal Rfrd Joural of Egrg ad Scc (IRJES) ISSN (Ol) 319-183X, (Prt) 319-181 Volum, Issu 10 (Octobr 013), PP. 6-30 Tolrac Itrval for Expotatd Expotal Dstrbuto Basd o Groupd Data C. S. Kaad 1, D. T. Shr

More information

COMPLEX NUMBER PAIRWISE COMPARISON AND COMPLEX NUMBER AHP

COMPLEX NUMBER PAIRWISE COMPARISON AND COMPLEX NUMBER AHP ISAHP 00, Bal, Indonsa, August -9, 00 COMPLEX NUMBER PAIRWISE COMPARISON AND COMPLEX NUMBER AHP Chkako MIYAKE, Kkch OHSAWA, Masahro KITO, and Masaak SHINOHARA Dpartmnt of Mathmatcal Informaton Engnrng

More information

Lecture 1: Empirical economic relations

Lecture 1: Empirical economic relations Ecoomcs 53 Lctur : Emprcal coomc rlatos What s coomtrcs? Ecoomtrcs s masurmt of coomc rlatos. W d to kow What s a coomc rlato? How do w masur such a rlato? Dfto: A coomc rlato s a rlato btw coomc varabls.

More information

A COMPARISON OF SEVERAL TESTS FOR EQUALITY OF COEFFICIENTS IN QUADRATIC REGRESSION MODELS UNDER HETEROSCEDASTICITY

A COMPARISON OF SEVERAL TESTS FOR EQUALITY OF COEFFICIENTS IN QUADRATIC REGRESSION MODELS UNDER HETEROSCEDASTICITY Colloquum Bomtrcum 44 04 09 7 COMPISON OF SEVEL ESS FO EQULIY OF COEFFICIENS IN QUDIC EGESSION MODELS UNDE HEEOSCEDSICIY Małgorzata Szczpa Dorota Domagała Dpartmt of ppld Mathmatcs ad Computr Scc Uvrsty

More information

Numerical Method: Finite difference scheme

Numerical Method: Finite difference scheme Numrcal Mthod: Ft dffrc schm Taylor s srs f(x 3 f(x f '(x f ''(x f '''(x...(1! 3! f(x 3 f(x f '(x f ''(x f '''(x...(! 3! whr > 0 from (1, f(x f(x f '(x R Droppg R, f(x f(x f '(x Forward dffrcg O ( x from

More information

Estimation of the Present Values of Life Annuities for the Different Actuarial Models

Estimation of the Present Values of Life Annuities for the Different Actuarial Models h Scod Itratoal Symposum o Stochastc Modls Rlablty Egrg, Lf Scc ad Opratos Maagmt Estmato of th Prst Valus of Lf Auts for th Dffrt Actuaral Modls Gady M Koshk, Oaa V Guba omsk Stat Uvrsty Dpartmt of Appld

More information

Lecture 5. Estimation of Variance Components

Lecture 5. Estimation of Variance Components Lctur 5 Etmato of Varac Compot Gulhrm J. M. Roa Uvrt of Wco-Mado Mxd Modl Quattatv Gtc SISG Sattl 8 0 Sptmbr 08 Etmato of Varac Compot ANOVA Etmato Codr th data t blow rlatd to obrvato of half-b faml of

More information

Correlation in tree The (ferromagnetic) Ising model

Correlation in tree The (ferromagnetic) Ising model 5/3/00 :\ jh\slf\nots.oc\7 Chaptr 7 Blf propagato corrlato tr Corrlato tr Th (frromagtc) Isg mol Th Isg mol s a graphcal mol or par ws raom Markov fl cosstg of a urct graph wth varabls assocat wth th vrtcs.

More information

A Measure of Inaccuracy between Two Fuzzy Sets

A Measure of Inaccuracy between Two Fuzzy Sets LGRN DEMY OF SENES YERNETS ND NFORMTON TEHNOLOGES Volum No 2 Sofa 20 Masur of accuracy btw Two Fuzzy Sts Rajkumar Vrma hu Dv Sharma Dpartmt of Mathmatcs Jayp sttut of formato Tchoy (Dmd vrsty) Noda (.P.)

More information

Chemistry 222 DO NOT OPEN THE EXAM UNTIL YOU ARE READY TO TAKE IT! You may allocate a maximum of 80 continuous minutes for this exam.

Chemistry 222 DO NOT OPEN THE EXAM UNTIL YOU ARE READY TO TAKE IT! You may allocate a maximum of 80 continuous minutes for this exam. Chmtry Sprg 09 Eam : Chaptr -5 Nam 80 Pot Complt fv (5) of th followg problm. CLEARLY mark th problm you o ot wat gra. You mut how your work to rcv crt for problm rqurg math. Rport your awr wth th approprat

More information

The probability of Riemann's hypothesis being true is. equal to 1. Yuyang Zhu 1

The probability of Riemann's hypothesis being true is. equal to 1. Yuyang Zhu 1 Th robablty of Ra's hyothss bg tru s ual to Yuyag Zhu Abstract Lt P b th st of all r ubrs P b th -th ( ) lt of P ascdg ordr of sz b ostv tgrs ad s a rutato of wth Th followg rsults ar gv ths ar: () Th

More information

Binary Choice. Multiple Choice. LPM logit logistic regresion probit. Multinomial Logit

Binary Choice. Multiple Choice. LPM logit logistic regresion probit. Multinomial Logit (c Pogsa Porchawssul, Faculty of Ecoomcs, Chulalogor Uvrsty (c Pogsa Porchawssul, Faculty of Ecoomcs, Chulalogor Uvrsty 3 Bary Choc LPM logt logstc rgrso probt Multpl Choc Multomal Logt (c Pogsa Porchawssul,

More information

Improved Exponential Estimator for Population Variance Using Two Auxiliary Variables

Improved Exponential Estimator for Population Variance Using Two Auxiliary Variables Improvd Epoal Emaor for Populao Varac Ug Two Aular Varabl Rajh gh Dparm of ac,baara Hdu Uvr(U.P., Ida (rgha@ahoo.com Pakaj Chauha ad rmala awa chool of ac, DAVV, Idor (M.P., Ida Flor maradach Dparm of

More information

Estimators for Finite Population Variance Using Mean and Variance of Auxiliary Variable

Estimators for Finite Population Variance Using Mean and Variance of Auxiliary Variable Itratoal Jal o Probablt a tattc 5 : - DOI:.59/j.jp.5. tmat Ft Poplato Varac U Ma a Varac o Alar Varabl Ph Mra * R. Kara h Dpartmt o tattc Lcow Urt Lcow Ia Abtract F tmat t poplato arac mato o l alar arabl

More information

Counting the compositions of a positive integer n using Generating Functions Start with, 1. x = 3 ), the number of compositions of 4.

Counting the compositions of a positive integer n using Generating Functions Start with, 1. x = 3 ), the number of compositions of 4. Coutg th compostos of a postv tgr usg Gratg Fuctos Start wth,... - Whr, for ampl, th co-ff of s, for o summad composto of aml,. To obta umbr of compostos of, w d th co-ff of (...) ( ) ( ) Hr for stac w

More information

A Stochastic Approximation Iterative Least Squares Estimation Procedure

A Stochastic Approximation Iterative Least Squares Estimation Procedure Joural of Al Azhar Uvrst-Gaza Natural Sccs, 00, : 35-54 A Stochastc Appromato Itratv Last Squars Estmato Procdur Shahaz Ezald Abu- Qamar Dpartmt of Appld Statstcs Facult of Ecoomcs ad Admstrato Sccs Al-Azhar

More information

Improved Exponential Estimator for Population Variance Using Two Auxiliary Variables

Improved Exponential Estimator for Population Variance Using Two Auxiliary Variables Rajh gh Dparm of ac,baara Hdu Uvr(U.P.), Ida Pakaj Chauha, rmala awa chool of ac, DAVV, Idor (M.P.), Ida Flor maradach Dparm of Mahmac, Uvr of w Mco, Gallup, UA Improvd Epoal Emaor for Populao Varac Ug

More information

Reliability Evaluation of Slopes Using Particle Swarm Optimization

Reliability Evaluation of Slopes Using Particle Swarm Optimization atoal Uvrsty of Malaysa From th lctdworks of Mohammad Khajhzadh 20 Rlablty Evaluato of lops Usg Partcl warm Optmzato Mohammad Khajhzadh Mohd Raha Taha hmd El-shaf valabl at: https://works.bprss.com/mohammad_khajhzadh/24/

More information

ERDOS-SMARANDACHE NUMBERS. Sabin Tabirca* Tatiana Tabirca**

ERDOS-SMARANDACHE NUMBERS. Sabin Tabirca* Tatiana Tabirca** ERDO-MARANDACHE NUMBER b Tbrc* Tt Tbrc** *Trslv Uvrsty of Brsov, Computr cc Dprtmt **Uvrsty of Mchstr, Computr cc Dprtmt Th strtg pot of ths rtcl s rprstd by rct work of Fch []. Bsd o two symptotc rsults

More information

Second Handout: The Measurement of Income Inequality: Basic Concepts

Second Handout: The Measurement of Income Inequality: Basic Concepts Scod Hadout: Th Masurmt of Icom Iqualty: Basc Cocpts O th ormatv approach to qualty masurmt ad th cocpt of "qually dstrbutd quvalt lvl of com" Suppos that that thr ar oly two dvduals socty, Rachl ad Mart

More information

Chapter 5 Special Discrete Distributions. Wen-Guey Tzeng Computer Science Department National Chiao University

Chapter 5 Special Discrete Distributions. Wen-Guey Tzeng Computer Science Department National Chiao University Chatr 5 Scal Dscrt Dstrbutos W-Guy Tzg Comutr Scc Dartmt Natoal Chao Uvrsty Why study scal radom varabls Thy aar frqutly thory, alcatos, statstcs, scc, grg, fac, tc. For aml, Th umbr of customrs a rod

More information

APPENDIX: STATISTICAL TOOLS

APPENDIX: STATISTICAL TOOLS I. Nots o radom samplig Why do you d to sampl radomly? APPENDI: STATISTICAL TOOLS I ordr to masur som valu o a populatio of orgaisms, you usually caot masur all orgaisms, so you sampl a subst of th populatio.

More information

On the Beta Mekaham Distribution and Its Applications. Chukwu A. U., Ogunde A. A. *

On the Beta Mekaham Distribution and Its Applications. Chukwu A. U., Ogunde A. A. * Amrca Joural of Mathmatcs ad Statstcs 25, 5(3: 37-43 DOI:.5923/j.ajms.2553.5 O th Bta Mkaham Dstruto ad Its Applcatos Chukwu A. U., Ogud A. A. * Dpartmt of Statstcs, Uvrsty Of Iada, Dpartmt of Mathmatcs

More information

Group Consensus of Second-Order Multi-agent Networks with Multiple Time Delays

Group Consensus of Second-Order Multi-agent Networks with Multiple Time Delays Itratoal Cofrc o Appld Mathmatcs, Smulato ad Modllg (AMSM 6) Group Cossus of Scod-Ordr Mult-agt Ntworks wth Multpl Tm Dlays Laghao J* ad Xyu Zhao Chogqg Ky Laboratory of Computatoal Itllgc, Chogqg Uvrsty

More information

ANOVA- Analyisis of Variance

ANOVA- Analyisis of Variance ANOVA- Aalii of Variac CS 700 Comparig altrativ Comparig two altrativ u cofidc itrval Comparig mor tha two altrativ ANOVA Aali of Variac Comparig Mor Tha Two Altrativ Naïv approach Compar cofidc itrval

More information

Weights Interpreting W and lnw What is β? Some Endnotes = n!ω if we neglect the zero point energy then ( )

Weights Interpreting W and lnw What is β? Some Endnotes = n!ω if we neglect the zero point energy then ( ) Sprg Ch 35: Statstcal chacs ad Chcal Ktcs Wghts... 9 Itrprtg W ad lw... 3 What s?... 33 Lt s loo at... 34 So Edots... 35 Chaptr 3: Fudatal Prcpls of Stat ch fro a spl odl (drvato of oltza dstrbuto, also

More information

Odd Generalized Exponential Flexible Weibull Extension Distribution

Odd Generalized Exponential Flexible Weibull Extension Distribution Odd Gralzd Epotal Flbl Wbull Etso Dstrbuto Abdlfattah Mustafa Mathmatcs Dpartmt Faculty of Scc Masoura Uvrsty Masoura Egypt abdlfatah mustafa@yahoo.com Bh S. El-Dsouy Mathmatcs Dpartmt Faculty of Scc Masoura

More information

Robust adaptive neuro-fuzzy controller for hybrid position/force control of robot manipulators in contact with unknown environment

Robust adaptive neuro-fuzzy controller for hybrid position/force control of robot manipulators in contact with unknown environment Robut adaptv uro-fuzzy cotrollr for hybrd poto/forc cotrol of robot mapulator cotact wth ukow vromt Arah Faa ad Mohammad Farrokh * Ira Uvrty of Scc ad chology, Dpartmt of Elctrcal Egrg, Narmak, hra 6844,

More information

Complex Numbers. Prepared by: Prof. Sunil Department of Mathematics NIT Hamirpur (HP)

Complex Numbers. Prepared by: Prof. Sunil Department of Mathematics NIT Hamirpur (HP) th Topc Compl Nmbrs Hyprbolc fctos ad Ivrs hyprbolc fctos, Rlato btw hyprbolc ad crclar fctos, Formla of hyprbolc fctos, Ivrs hyprbolc fctos Prpard by: Prof Sl Dpartmt of Mathmatcs NIT Hamrpr (HP) Hyprbolc

More information

Line Matching Algorithm for Localization of Mobile Robot Using Distance Data from Structured-light Image 1

Line Matching Algorithm for Localization of Mobile Robot Using Distance Data from Structured-light Image 1 Advacd Scc ad Tchoogy Lttrs Vo.86 (Ubqutous Scc ad Egrg 015), pp.37-4 http://dx.do.org/10.1457/ast.015.86.08 L Matchg Agorthm for Locazato of Mob Robot Usg Dstac Data from Structurd-ght Imag 1 Soocho Km

More information

On the Possible Coding Principles of DNA & I Ching

On the Possible Coding Principles of DNA & I Ching Sctfc GOD Joural May 015 Volum 6 Issu 4 pp. 161-166 Hu, H. & Wu, M., O th Possbl Codg Prcpls of DNA & I Chg 161 O th Possbl Codg Prcpls of DNA & I Chg Hupg Hu * & Maox Wu Rvw Artcl ABSTRACT I ths rvw artcl,

More information

Chapter 6. pn-junction diode: I-V characteristics

Chapter 6. pn-junction diode: I-V characteristics Chatr 6. -jucto dod: -V charactrstcs Tocs: stady stat rsos of th jucto dod udr ald d.c. voltag. ucto udr bas qualtatv dscusso dal dod quato Dvatos from th dal dod Charg-cotrol aroach Prof. Yo-S M Elctroc

More information

Math Tricks. Basic Probability. x k. (Combination - number of ways to group r of n objects, order not important) (a is constant, 0 < r < 1)

Math Tricks. Basic Probability. x k. (Combination - number of ways to group r of n objects, order not important) (a is constant, 0 < r < 1) Math Trcks r! Combato - umbr o was to group r o objcts, ordr ot mportat r! r! ar 0 a r a s costat, 0 < r < k k! k 0 EX E[XX-] + EX Basc Probablt 0 or d Pr[X > ] - Pr[X ] Pr[ X ] Pr[X ] - Pr[X ] Proprts

More information

[ ] 1+ lim G( s) 1+ s + s G s s G s Kacc SYSTEM PERFORMANCE. Since. Lecture 10: Steady-state Errors. Steady-state Errors. Then

[ ] 1+ lim G( s) 1+ s + s G s s G s Kacc SYSTEM PERFORMANCE. Since. Lecture 10: Steady-state Errors. Steady-state Errors. Then SYSTEM PERFORMANCE Lctur 0: Stady-tat Error Stady-tat Error Lctur 0: Stady-tat Error Dr.alyana Vluvolu Stady-tat rror can b found by applying th final valu thorm and i givn by lim ( t) lim E ( ) t 0 providd

More information

The real E-k diagram of Si is more complicated (indirect semiconductor). The bottom of E C and top of E V appear for different values of k.

The real E-k diagram of Si is more complicated (indirect semiconductor). The bottom of E C and top of E V appear for different values of k. Modr Smcoductor Dvcs for Itgratd rcuts haptr. lctros ad Hols Smcoductors or a bad ctrd at k=0, th -k rlatoshp ar th mmum s usually parabolc: m = k * m* d / dk d / dk gatv gatv ffctv mass Wdr small d /

More information

Power System Dynamic Security Region and Its Approximations

Power System Dynamic Security Region and Its Approximations h artcl ha b accpt for publcato a futur u of th joural, but ha ot b fully t. Cott may chag pror to fal publcato. > REPLACE HIS LINE WIH YOUR PAPER IDENIFICAION NUMBER (DOUBLE-CLICK HERE O EDI) < Powr Sytm

More information

Lecture 7 Diffusion. Our fluid equations that we developed before are: v t v mn t

Lecture 7 Diffusion. Our fluid equations that we developed before are: v t v mn t Cla ot fo EE6318/Phy 6383 Spg 001 Th doumt fo tutoal u oly ad may ot b opd o dtbutd outd of EE6318/Phy 6383 tu 7 Dffuo Ou flud quato that w dvlopd bfo a: f ( )+ v v m + v v M m v f P+ q E+ v B 13 1 4 34

More information

Channel Capacity Course - Information Theory - Tetsuo Asano and Tad matsumoto {t-asano,

Channel Capacity Course - Information Theory - Tetsuo Asano and Tad matsumoto   {t-asano, School of Iformato Scc Chal Capacty 009 - Cours - Iformato Thory - Ttsuo Asao ad Tad matsumoto Emal: {t-asao matumoto}@jast.ac.jp Japa Advacd Isttut of Scc ad Tchology Asahda - Nom Ishkawa 93-9 Japa http://www.jast.ac.jp

More information

Learning from Data with Information Theoretic Criteria II

Learning from Data with Information Theoretic Criteria II Larg from Data th Iformato Thortc Crtra II Jos C. Prcp, Ph.D. Dstgushd Profssor of Elctrcal ad Bomdcal Egrg ad BllSouth Profssor Computatoal uroegrg Laborator Uvrst of Florda http://.cl.ufl.du prcp@cl.ufl.du

More information

Note: Torque is prop. to current Stationary voltage is prop. to speed

Note: Torque is prop. to current Stationary voltage is prop. to speed DC Mach Cotrol Mathmatcal modl. Armatr ad orq f m m a m m r a a a a a dt d ψ ψ ψ ω Not: orq prop. to crrt Statoary voltag prop. to pd Mathmatcal modl. Fld magtato f f f f d f dt a f ψ m m f f m fλ h torq

More information

Bayesian Test for Lifetime Performance Index of Ailamujia Distribution Under Squared Error Loss Function

Bayesian Test for Lifetime Performance Index of Ailamujia Distribution Under Squared Error Loss Function Pur ad Appld Mathmatcs Joural 6; 5(6): 8-85 http://www.sccpublshggroup.com/j/pamj do:.648/j.pamj.656. ISSN: 36-979 (Prt); ISSN: 36-98 (Ol) Baysa Tst for ftm Prformac Idx of Alamuja Dstrbuto Udr Squard

More information

Chemistry 350. The take-home least-squares problem will account for 15 possible points on this exam.

Chemistry 350. The take-home least-squares problem will account for 15 possible points on this exam. Chmtry 30 Sprg 08 Eam : Chaptr - Nam 00 Pot You mut how your work to rcv crt for problm rqurg math. Rport your awr wth th approprat umbr of gfcat fgur. Th tak-hom lat-quar problm wll accout for pobl pot

More information

(1) Then we could wave our hands over this and it would become:

(1) Then we could wave our hands over this and it would become: MAT* K285 Spring 28 Anthony Bnoit 4/17/28 Wk 12: Laplac Tranform Rading: Kohlr & Johnon, Chaptr 5 to p. 35 HW: 5.1: 3, 7, 1*, 19 5.2: 1, 5*, 13*, 19, 45* 5.3: 1, 11*, 19 * Pla writ-up th problm natly and

More information

On Estimation of Unknown Parameters of Exponential- Logarithmic Distribution by Censored Data

On Estimation of Unknown Parameters of Exponential- Logarithmic Distribution by Censored Data saqartvlos mcrbata rovul akadms moamb, t 9, #2, 2015 BULLETIN OF THE GEORGIAN NATIONAL ACADEMY OF SCIENCES, vol 9, o 2, 2015 Mathmatcs O Estmato of Ukow Paramtrs of Epotal- Logarthmc Dstrbuto by Csord

More information

MATHEMATICAL IDEAS AND NOTIONS OF QUANTUM FIELD THEORY. e S(A)/ da, h N

MATHEMATICAL IDEAS AND NOTIONS OF QUANTUM FIELD THEORY. e S(A)/ da, h N MATHEMATICAL IDEAS AND NOTIONS OF QUANTUM FIELD THEORY 9 4. Matrx tgrals Lt h N b th spac of Hrmta matrcs of sz N. Th r product o h N s gv by (A, B) = Tr(AB). I ths scto w wll cosdr tgrals of th form Z

More information

We need to first account for each of the dilutions to determine the concentration of mercury in the original solution:

We need to first account for each of the dilutions to determine the concentration of mercury in the original solution: Complt fv (5) of th followg problm. CLEARLY mark th problm you o ot wat gra. You mut how your work to rcv crt for problm rqurg math. Rport your awr wth th approprat umbr of gfcat fgur. Do fv of problm

More information

Control Systems. Lecture 8 Root Locus. Root Locus. Plant. Controller. Sensor

Control Systems. Lecture 8 Root Locus. Root Locus. Plant. Controller. Sensor Cotol Syt ctu 8 Root ocu Clacal Cotol Pof. Eugo Schut hgh Uvty Root ocu Cotoll Plat R E C U Y - H C D So Y C C R C H Wtg th loo ga a w a ttd tackg th clod-loo ol a ga va Clacal Cotol Pof. Eugo Schut hgh

More information

Positive unstable electrical circuits

Positive unstable electrical circuits Taz KZOEK alyto Uvrty of Tchology Faclty of Elctrcal Egrg Potv tabl lctrcal crct btract: Th tablty for th potv lar lctrcal crct compo of rtor col coator a voltag crrt orc ar ar Thr ffrt cla of th potv

More information

Estimation of Population Variance Using a Generalized Double Sampling Estimator

Estimation of Population Variance Using a Generalized Double Sampling Estimator r Laka Joural o Appl tatstcs Vol 5-3 stmato o Populato Varac Us a Gralz Doubl ampl stmator Push Msra * a R. Kara h Dpartmt o tatstcs D.A.V.P.G. Coll Dhrau- 8 Uttarakha Ia. Dpartmt o tatstcs Luckow Uvrst

More information

Machine Learning. Principle Component Analysis. Prof. Dr. Volker Sperschneider

Machine Learning. Principle Component Analysis. Prof. Dr. Volker Sperschneider Mach Larg Prcpl Compot Aalyss Prof. Dr. Volkr Sprschdr AG Maschlls Lr ud Natürlchsprachlch Systm Isttut für Iformatk chsch Fakultät Albrt-Ludgs-Uvrstät Frburg sprschdr@formatk.u-frburg.d I. Archtctur II.

More information

Iranian Journal of Mathematical Chemistry, Vol. 2, No. 2, December 2011, pp (Received September 10, 2011) ABSTRACT

Iranian Journal of Mathematical Chemistry, Vol. 2, No. 2, December 2011, pp (Received September 10, 2011) ABSTRACT Iraa Joral of Mathatcal Chstry Vol No Dcbr 0 09 7 IJMC Two Tys of Gotrc Arthtc dx of V hylc Naotb S MORADI S BABARAHIM AND M GHORBANI Dartt of Mathatcs Faclty of Scc Arak Ursty Arak 856-8-89 I R Ira Dartt

More information

Chapter 4 NUMERICAL METHODS FOR SOLVING BOUNDARY-VALUE PROBLEMS

Chapter 4 NUMERICAL METHODS FOR SOLVING BOUNDARY-VALUE PROBLEMS Chaptr 4 NUMERICL METHODS FOR SOLVING BOUNDRY-VLUE PROBLEMS 00 4. Varatoal formulato two-msoal magtostatcs Lt th followg magtostatc bouar-valu problm b cosr ( ) J (4..) 0 alog ΓD (4..) 0 alog ΓN (4..)

More information

Power Spectrum Estimation of Stochastic Stationary Signals

Power Spectrum Estimation of Stochastic Stationary Signals ag of 6 or Spctru stato of Stochastc Statoary Sgas Lt s cosr a obsrvato of a stochastc procss (). Ay obsrvato s a ft rcor of th ra procss. Thrfor, ca say:

More information

Worksheet: Taylor Series, Lagrange Error Bound ilearnmath.net

Worksheet: Taylor Series, Lagrange Error Bound ilearnmath.net Taylor s Thorm & Lagrag Error Bouds Actual Error This is th ral amout o rror, ot th rror boud (worst cas scario). It is th dirc btw th actual () ad th polyomial. Stps:. Plug -valu ito () to gt a valu.

More information

10/7/14. Mixture Models. Comp 135 Introduction to Machine Learning and Data Mining. Maximum likelihood estimation. Mixture of Normals in 1D

10/7/14. Mixture Models. Comp 135 Introduction to Machine Learning and Data Mining. Maximum likelihood estimation. Mixture of Normals in 1D Comp 35 Introducton to Machn Larnng and Data Mnng Fall 204 rofssor: Ron Khardon Mxtur Modls Motvatd by soft k-mans w dvlopd a gnratv modl for clustrng. Assum thr ar k clustrs Clustrs ar not rqurd to hav

More information

ASYMPTOTIC AND TOLERANCE 2D-MODELLING IN ELASTODYNAMICS OF CERTAIN THIN-WALLED STRUCTURES

ASYMPTOTIC AND TOLERANCE 2D-MODELLING IN ELASTODYNAMICS OF CERTAIN THIN-WALLED STRUCTURES AYMPTOTIC AD TOLERACE D-MODELLIG I ELATODYAMIC OF CERTAI THI-WALLED TRUCTURE B. MICHALAK Cz. WOŹIAK Dpartmt of tructural Mchacs Lodz Uvrsty of Tchology Al. Poltrchk 6 90-94 Łódź Polad Th objct of aalyss

More information

MODEL QUESTION. Statistics (Theory) (New Syllabus) dx OR, If M is the mode of a discrete probability distribution with mass function f

MODEL QUESTION. Statistics (Theory) (New Syllabus) dx OR, If M is the mode of a discrete probability distribution with mass function f MODEL QUESTION Statstcs (Thory) (Nw Syllabus) GROUP A d θ. ) Wrt dow th rsult of ( ) ) d OR, If M s th mod of a dscrt robablty dstrbuto wth mass fucto f th f().. at M. d d ( θ ) θ θ OR, f() mamum valu

More information

Introduction to Laplace Transforms October 25, 2017

Introduction to Laplace Transforms October 25, 2017 Iroduco o Lplc Trform Ocobr 5, 7 Iroduco o Lplc Trform Lrr ro Mchcl Egrg 5 Smr Egrg l Ocobr 5, 7 Oul Rvw l cl Wh Lplc rform fo of Lplc rform Gg rform b gro Fdg rform d vr rform from bl d horm pplco o dffrl

More information

The equilibrium distribution of firms in a monopolistically competitive model with the removal of zero-profit conditions

The equilibrium distribution of firms in a monopolistically competitive model with the removal of zero-profit conditions Tzukayama RIEB Dcuo apr Sr No. 3 T qulbrum dtrbuto o rm a moopoltcally compttv modl wt t rmoval o zro-prot codto Wataru Jodo aculty o Ecoomc, Tzukayama Uvrty Octobr 204 Tzukayama Uvrty Rarc Ittut or Ecoomc

More information

GRAPHS IN SCIENCE. drawn correctly, the. other is not. Which. Best Fit Line # one is which?

GRAPHS IN SCIENCE. drawn correctly, the. other is not. Which. Best Fit Line # one is which? 5 9 Bt Ft L # 8 7 6 5 GRAPH IN CIENCE O of th thg ot oft a rto of a xrt a grah of o k. A grah a vual rrtato of ural ata ollt fro a xrt. o of th ty of grah you ll f ar bar a grah. Th o u ot oft a l grah,

More information

Trignometric Inequations and Fuzzy Information Theory

Trignometric Inequations and Fuzzy Information Theory Iteratoal Joural of Scetfc ad Iovatve Mathematcal Reearch (IJSIMR) Volume, Iue, Jauary - 0, PP 00-07 ISSN 7-07X (Prt) & ISSN 7- (Ole) www.arcjoural.org Trgometrc Iequato ad Fuzzy Iformato Theory P.K. Sharma,

More information

A New Method for Decision Making Based on Soft Matrix Theory

A New Method for Decision Making Based on Soft Matrix Theory Joural of Scetfc esearch & eports 3(5): 0-7, 04; rtcle o. JS.04.5.00 SCIENCEDOMIN teratoal www.scecedoma.org New Method for Decso Mag Based o Soft Matrx Theory Zhmg Zhag * College of Mathematcs ad Computer

More information

Washington State University

Washington State University he 3 Ktics ad Ractor Dsig Sprg, 00 Washgto Stat Uivrsity Dpartmt of hmical Egrg Richard L. Zollars Exam # You will hav o hour (60 muts) to complt this xam which cosists of four (4) problms. You may us

More information

Soft k-means Clustering. Comp 135 Machine Learning Computer Science Tufts University. Mixture Models. Mixture of Normals in 1D

Soft k-means Clustering. Comp 135 Machine Learning Computer Science Tufts University. Mixture Models. Mixture of Normals in 1D Comp 35 Machn Larnng Computr Scnc Tufts Unvrsty Fall 207 Ron Khardon Th EM Algorthm Mxtur Modls Sm-Suprvsd Larnng Soft k-mans Clustrng ck k clustr cntrs : Assocat xampls wth cntrs p,j ~~ smlarty b/w cntr

More information

Integral points on hyperbolas over Z: A special case

Integral points on hyperbolas over Z: A special case Itgral pots o hprbolas ovr Z: A spcal cas `Pag of 7 Kostat Zlator Dpartmt of Mathmatcs ad Computr Scc Rhod Islad Collg 600 Mout Plasat Avu Provdc, R.I. 0908-99, U.S.A. -mal addrss: ) Kzlator@rc.du ) Kostat_zlator@ahoo.com

More information

signal amplification; design of digital logic; memory circuits

signal amplification; design of digital logic; memory circuits hatr Th lctroc dvc that s caabl of currt ad voltag amlfcato, or ga, cojucto wth othr crcut lmts, s th trasstor, whch s a thr-trmal dvc. Th dvlomt of th slco trasstor by Bard, Bratta, ad chockly at Bll

More information

Note on the Computation of Sample Size for Ratio Sampling

Note on the Computation of Sample Size for Ratio Sampling Not o th Computato of Sampl Sz for ato Samplg alr LMa, Ph.D., PF Forst sourcs Maagmt Uvrst of B.C. acouvr, BC, CANADA Sptmbr, 999 Backgroud ato samplg s commol usd to rduc cofdc trvals for a varabl of

More information

Consistency of the Maximum Likelihood Estimator in Logistic Regression Model: A Different Approach

Consistency of the Maximum Likelihood Estimator in Logistic Regression Model: A Different Approach ISSN 168-8 Joural of Statstcs Volum 16, 9,. 1-11 Cosstcy of th Mamum Lklhood Estmator Logstc Rgrsso Modl: A Dffrt Aroach Abstract Mamuur Rashd 1 ad Nama Shfa hs artcl vstgats th cosstcy of mamum lklhood

More information

Entropy Equation for a Control Volume

Entropy Equation for a Control Volume Fudamtals of Thrmodyamcs Chaptr 7 Etropy Equato for a Cotrol Volum Prof. Syoug Jog Thrmodyamcs I MEE2022-02 Thrmal Egrg Lab. 2 Q ds Srr T Q S2 S1 1 Q S S2 S1 Srr T t t T t S S s m 1 2 t S S s m tt S S

More information