Superbosonization meets Free Probability

Size: px
Start display at page:

Download "Superbosonization meets Free Probability"

Transcription

1 Suprbosoato mts Fr Probablty M Zrbaur jot wor wth S Madt Eulr Symposum St Ptrsburg Ju Itroducto From momts to cumulats Larg- charactrstc fucto by fr probablty Suprbosoato Applcato to dsordrd scattrg

2 Itroducto

3 Itroducto: oto of fr probablty 'Fr probablty' troducd by D Voculscu 986 th study of vo uma algbras Gvs calculatoal schm by whch to hadl varat smbls of radom matrcs Larg- lmt of dsty of stats codd Voculscu R-trasform : Fr cumulats c R 0 + ar addtv udr addto of dpdt radom matrcs for c

4 Itroducto: fr probablty II Voculscu' s aalytcal approach : df R-trasform by vrtg a g avrag trac of rsolvt Combatoral dscrpto of fr cumulats trms of o-crossg parttos gv by R Spchr 994 Fr probablty thory has ot yt producd rsults for spctral corrlato fuctos th mcroscopc lmt

5 Itroducto: suprsymmtry mthod Mthod of commutg ad at-commutg varabls Wgr Eftov : rsults for corrlato fuctos g lvl statstcs of small mtallc gras localato thc dsordrd wrs scalg xpots at th Adrso trasto tc Tradtoal varat lmtd to Gaussa ubbard-stratoovch trasformato radom varabls Rct varat calld ' suprbosoato' much wdr class of dstrbutos allows to trat

6 Itroducto: suprsymmtry mthod frst stps Ω + Ω + Tr xp : Dt Dt Dt : Im Dt : sg d w w - Br w w μ μ fucto Charactrstc varabls commutg at ovr tgral Gaussa varabls commutg ovr Gaussa tgral spac vctor o rmta oprator lar

7 From momts to cumulats

8 From momts to cumulats I 0 0!! : Ω Ω d d c c m x d x d x m x grats th cumulats l Th logarthm charactrstc fucto ar gratdby th Momts Commutatv cas ω ω μ μ

9 From momts to cumulats II: combatoral dscrpto Momts ar xprssd trms of cumulats m d d Ω by summg ovr parttos p Π : whr ν p l 0 d d ω 0 p Π s th umbr of blocs of l l c ν l p l lν l p lgth l Exampl ν p 8 : ν p p { 36} { 8} { 45} { 7} ν p 3 ν p 0 4

10 From momts to cumulats III: R-trasform + + Ω Ω lm : Tr Tr R- c R R m g m m m d m d d trasform whr s vrtd by By dfto Lt dffrtato : ar gratd by Momts Charactrstc fucto : matrcs for Probablty masur a a μ μ μ

11 R-trasform xampls Luc Sommrs MZ: J Math Phys /3 / g R g R g R Exampl : smcrcl Wgr GUE Exampl :

12 From momts to cumulats IV: frss Rcall R c + R Spchr 994 : Combatoral dfto of fr cumulats Exampl c by 8 : p m p C { 58} { 34} { 67} whr th sum rus ovr o-crossg parttos p C l c ν l l p Fr cumulats add udr covoluto of masurs or addto of dpdt radom matrcs

13 Larg- charactrstc fucto by fr probablty

14 Cumulats o-commutatv cas ot : udr ω lm l Ω s addtv addto of dpdt radom matrcs Assum for g GL ad cumulats must b of th gral form [whr γ π s costat dμ Tr V o cojugacy classs] ω 0 d Th ω ω g π S γ π Graphcal mthods suggst larg- hypothss : j γ π c j j f π rrducbl cycl ad γ π l δ j l π l 0 ls g

15 urstcs from plaar graphs I Rcall Ω Tr V + Tr Prturbato thory for l Ω coctd graphs lads to topol xpaso : l Ω d χ 0 Ladg cotrbuto coms from summg all plaar graphs Eulr charactrstc χ χ ω χ Exampl : graph cotrbutg to Tr t ooft Wtt Gross 4 9 vrtcs: dgs: 4 facs: 9 4

16 urstcs from plaar graphs II χ χ 0 Tr 4 Tr 3 Tr

17 Larg- charactrstc fucto from fr probablty I Rcall th larg- scaro from plaar graphs : j j l Ω 0 c π [rr] l δ l j π l By Spchr' s combatoral dscrpto th umbrs c ar dtfd as th fr cumulats I fact tag th formula for th momt w hav m l Ω 0 p C l c ν l p l

18 Larg- charactrstc fucto from fr probablty II Summary ω : lm For ot : d d ω Π smbls w hav ω 0 ra- projctor c wth + l Ω 0 GL c + Π s th Tr + th drvatv of ω R -symmtry + for Π R-trasform :

19 o-prturbatv argumt: rducto to D tgral Lt Rcall : Π Ω Π ra-o projctor Π Tr Π TrV d Dagoal g Do tgral ovr Ω Π c whr dgr for ot : p p + x orthogoal polyomal of dag λ g U V x λ x V x g usg CIZ formula : x dx has ros [ ab] p

20 Asymptotcs for larg Saddl pot of x-tgral ls outsd of [ a b] Larg- asymptotcs of p x b a p l xy dν y wh x [ a b] : G x Itgral for scald logarthm of char fucto ω Π c l + lm l has good saddl pot : 0 x V x + G x V x + g x dx Fal rsult Dyso Coulomb gas : ω Π 0 R t dt

21 Asymptotcs for small o good saddl pot for prvous tgral saddl wats to b oscllatory rgo Us ow asymptotcs of to swtch to w rprstato cotour tgral : ω Π + l + lm wth saddl pot quato g p l G d Fal rsult aga : ω Π Guot & Mada R t dt

22 Suprbosoato acbroch Wdmüllr 95 Lhma Sahr Soolov Sommrs 95 Barruto Browr Svttsy 0 Eftov Schwt Taahash 04 Guhr 06; Basl Ama 07 Budr Eftov ravtsov Yvtusho MZ 07 Lttlma Sommrs MZ 08

23 Rmdr: suprsymmtry mthod ; ; xp ; Im ; ; sg Tr + Ω + Ω g g g g f f gg G d w f f f D d b b b b a a a a a j a aj j b q b b p a j a a j μ μ th actg by cojugato by som group varat If ad s alog whr th tgral fuctos : corrlato spctral for fucto Gratg s valuatd o fucto Charactrstc a

24 Suprbosoato spcal cas: commutg varabls oly Lt p q f : 0 ad cosdr GL -varat f f g g holom fct g GL Fact varat thory: thr xsts a holomorphc fucto F : such that F f By push forward of th tgral o has f d + c F r r dr f tgral xsts gralato to p > : s Fyodorov ucl Phys B

25 Suprbosoato spcal cas: Grassma varabls oly p 0 q Lt F : b holomorphc Atcommutg varabls Br tgral F d d : F + + F! 0 th U F th drvatv at d / π th org q > : awamoto ad Smt ucl Phys B

26 Th da of suprbosoato Rcall for g f ; G Lt G GL f g g or G O ; g g or G Sp Suprbosoato xplots to ma a stp of rducto : ths symmtry Th tgral ovr of th G-varat fucto f s covrtd to a tgral ovr a Rmaa symmtrc suprspac Th larg umbr of varabls th bcoms a paramtr of th tgral

27 Suprbosoato: utary symmtry U U / GL SDt MZ Sommrs Lttlma t t DQ - M Q F Q DQ f f p q p p M form Br tgrato varat ad doma wth tgrato th holomorphc ad Schwart alog ad If Thorm ; : ; GL F f Q F f G to Lft Lt

28 Applcato to dsordrd scattrg

29 Th sttg tral stats radom matrx amltoa coupld to M scattrg chals dlbrg approxmato to scattrg matrx : S E Id M W E + WW W To comput corrlatos C ab cd E E Z X Y S Dt E Dt E ab E δ us gratg fucto : ab S cd E δ + WXW Dt E WW + WW Dt E + WYW cd

30 Avragg trc Problm : ca' t us suprbosoato drctly sc prsc of WW bras U -symmtry Us trc of avragg tgrad ovr forc! U -symmtry U to For larg w hav formula lm f ra of both A ad B s pt fxd Etrs of g U U Tr AgBg dg Dt Id A B bcom Gaussa radom varabls

31 Uvrsalty of corrlato fucto Corrlato fucto by suprbosoato: C ab cd E E lm M DQ STr l Q + ω Q STr Q / E E STr ΛQ F ab cd Q Tag Q + R Q Soluto saddl-pot symmtry ad hc uvrsalup to scal factor Cocluso: gvs saddl-pot quato for Q : E Corrlatos of ar uvrsal dpdt mafold s dtrmd by S-matrx lmts radom matrx smbl th larg- + E of th choc of lmt

32 Coclusos Fr probablty thory provds th propr framwor whch to ta th larg- lmt of th dsty of stats for smbls whch ar varat but o-gaussa Fr cumulats ar th Taylor coffcts of th logarthm of th charactrstc fucto whch s coutrd wh usg suprbosoato Th group of suprsymmtrs dtrms crtcal tgrato mafold saddl pots th Our formalsm stablshs radom matrx uvrsalty of spctral corrlatos as wll as trasport obsrvabls

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

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

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

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

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

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

Phase diagram and frustration of decoherence in Y-shaped Josephson junction networks. D.Giuliano(Cosenza), P. Sodano(Perugia)

Phase diagram and frustration of decoherence in Y-shaped Josephson junction networks. D.Giuliano(Cosenza), P. Sodano(Perugia) Phas dagram ad frustrato of dcohrc Y-shapd Josphso jucto tworks D.GulaoCosza, P. SodaoPruga Frz, Frz, Octobr Octobr 008 008 Ma da Y-Shapd twork of Josphso jucto chas YJJN wth a magtc frustrato Ft-couplg

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

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

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

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

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

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

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

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

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

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

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

Estimation Theory. Chapter 4

Estimation Theory. Chapter 4 Estmato ory aptr 4 LIEAR MOELS W - I matrx form Estmat slop B ad trcpt A,,.. - WG W B A l fttg Rcall W W W B A W ~ calld vctor I gral, ormal or Gaussa ata obsrvato paramtr Ma, ovarac KOW p matrx to b stmatd,

More information

Pion Production via Proton Synchrotron Radiation in Strong Magnetic Fields in Relativistic Quantum Approach

Pion Production via Proton Synchrotron Radiation in Strong Magnetic Fields in Relativistic Quantum Approach Po Producto va Proto Sychrotro Radato Strog Magtc Flds Rlatvstc Quatum Approach Partcl Productos TV Ergy Rgo Collaborators Toshtaka Kajo Myog-K Chou Grad. J. MATHEWS Tomoyuk Maruyama BRS. Nho Uvrsty NaO,

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

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

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

Borel transform a tool for symmetry-breaking phenomena?

Borel transform a tool for symmetry-breaking phenomena? Borl trasform a tool for symmtry-braig phoma? M Zirbaur SFB/TR, Gdas Spt, 9 Itroductio: spotaous symmtry braig, ordr paramtr, collctiv fild mthods Borl trasform for itractig frmios ampl: frromagtic ordr

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

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

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

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

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

Statistical Thermodynamics Essential Concepts. (Boltzmann Population, Partition Functions, Entropy, Enthalpy, Free Energy) - lecture 5 -

Statistical Thermodynamics Essential Concepts. (Boltzmann Population, Partition Functions, Entropy, Enthalpy, Free Energy) - lecture 5 - Statstcal Thrmodyamcs sstal Cocpts (Boltzma Populato, Partto Fuctos, tropy, thalpy, Fr rgy) - lctur 5 - uatum mchacs of atoms ad molculs STATISTICAL MCHANICS ulbrum Proprts: Thrmodyamcs MACROSCOPIC Proprts

More information

Backlund Transformations for Non-Commutative (NC) Integrable Eqs.

Backlund Transformations for Non-Commutative (NC) Integrable Eqs. Backlud Trasormatos or No-Commutatv NC Itral Es. asas HAANAKA Uvrsty o Naoya vst Glaso utl. 3 ad IHES 3 - ar3 9 Basd o Clar R.Glso Glaso H ad oata.c.nmmo Glaso ``Backlud trs or NC at-sl-dual AS Ya-lls

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

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

X ε ) = 0, or equivalently, lim

X ε ) = 0, or equivalently, lim Revew for the prevous lecture Cocepts: order statstcs Theorems: Dstrbutos of order statstcs Examples: How to get the dstrbuto of order statstcs Chapter 5 Propertes of a Radom Sample Secto 55 Covergece

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

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

Section 5.1/5.2: Areas and Distances the Definite Integral

Section 5.1/5.2: Areas and Distances the Definite Integral Scto./.: Ars d Dstcs th Dt Itgrl Sgm Notto Prctc HW rom Stwrt Ttook ot to hd p. #,, 9 p. 6 #,, 9- odd, - odd Th sum o trms,,, s wrtt s, whr th d o summto Empl : Fd th sum. Soluto: Th Dt Itgrl Suppos w

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

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

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

Large N phase transitions in Supersymmetric gauge theories with massive matter

Large N phase transitions in Supersymmetric gauge theories with massive matter Lar phas trastos Suprsytrc au thors wth assv attr Mul Trz trz@uc.s Uvrsdad Copluts d Madrd Basd o: J. Russo ad K. arbo arv:309.004 3.4 30.6968 A. Barraco ad J. Russo arv:40.367 J. Russo G. Slva ad M.T.

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

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

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

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

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

Control Systems (Lecture note #6)

Control Systems (Lecture note #6) 6.5 Corol Sysms (Lcur o #6 Las Tm: Lar algbra rw Lar algbrac quaos soluos Paramrzao of all soluos Smlary rasformao: compao form Egalus ad gcors dagoal form bg pcur: o brach of h cours Vcor spacs marcs

More information

Chapter 14 Logistic Regression Models

Chapter 14 Logistic Regression Models Chapter 4 Logstc Regresso Models I the lear regresso model X β + ε, there are two types of varables explaatory varables X, X,, X k ad study varable y These varables ca be measured o a cotuous scale as

More information

ME 501A Seminar in Engineering Analysis Page 1

ME 501A Seminar in Engineering Analysis Page 1 St Ssts o Ordar Drtal Equatos Novbr 7 St Ssts o Ordar Drtal Equatos Larr Cartto Mcacal Er 5A Sar Er Aalss Novbr 7 Outl Mr Rsults Rvw last class Stablt o urcal solutos Stp sz varato or rror cotrol Multstp

More information

Discrete Fourier Transform (DFT)

Discrete Fourier Transform (DFT) Discrt Fourir Trasorm DFT Major: All Egirig Majors Authors: Duc guy http://umricalmthods.g.us.du umrical Mthods or STEM udrgraduats 8/3/29 http://umricalmthods.g.us.du Discrt Fourir Trasorm Rcalld th xpotial

More information

Chapter 5 Properties of a Random Sample

Chapter 5 Properties of a Random Sample Lecture 6 o BST 63: Statstcal Theory I Ku Zhag, /0/008 Revew for the prevous lecture Cocepts: t-dstrbuto, F-dstrbuto Theorems: Dstrbutos of sample mea ad sample varace, relatoshp betwee sample mea ad sample

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

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

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

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

EE 570: Location and Navigation: Theory & Practice

EE 570: Location and Navigation: Theory & Practice EE 570: ocato ad Navgato: Thory & Practc Navgato Mathmatcs Thursay 7 F 2013 NMT EE 570: ocato ad Navgato: Thory & Practc Sld 1 of 15 Navgato Mathmatcs : Coordat Fram Trasformatos Dtrm th dtald kmatc rlatoshps

More information

On Approximation Lower Bounds for TSP with Bounded Metrics

On Approximation Lower Bounds for TSP with Bounded Metrics O Approxmato Lowr Bouds for TSP wth Boudd Mtrcs Mark Karpsk Rchard Schmd Abstract W dvlop a w mthod for provg xplct approxmato lowr bouds for TSP problms wth boudd mtrcs mprovg o th bst up to ow kow bouds.

More information

Radial Distribution Function. Long-Range Corrections (1) Temperature. 3. Calculation of Equilibrium Properties. Thermodynamics Properties

Radial Distribution Function. Long-Range Corrections (1) Temperature. 3. Calculation of Equilibrium Properties. Thermodynamics Properties . Calculato o qulbrum Prorts. hrmodamc Prorts mratur, Itral rg ad Prssur Fr rg ad tro. Calculato o Damc Prorts Duso Coct hrmal Coductvt Shar scost Irard Absorto Coct k k k mratur m v Rmmbr hrmodamcs or

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

Integral Equation Methods. Jacob White. Thanks to Deepak Ramaswamy, Michal Rewienski, Xin Wang and Karen Veroy

Integral Equation Methods. Jacob White. Thanks to Deepak Ramaswamy, Michal Rewienski, Xin Wang and Karen Veroy Itroducto to Smulato - Lecture 22 Itegral Equato ethods Jacob Whte Thaks to Deepak Ramaswamy, chal Rewesk, X Wag ad Kare Veroy Outle Itegral Equato ethods Exteror versus teror problems Start wth usg pot

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

1985 AP Calculus BC: Section I

1985 AP Calculus BC: Section I 985 AP Calculus BC: Sctio I 9 Miuts No Calculator Nots: () I this amiatio, l dots th atural logarithm of (that is, logarithm to th bas ). () Ulss othrwis spcifid, th domai of a fuctio f is assumd to b

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

THE ROYAL STATISTICAL SOCIETY GRADUATE DIPLOMA

THE ROYAL STATISTICAL SOCIETY GRADUATE DIPLOMA THE ROYAL STATISTICAL SOCIETY 3 EXAMINATIONS SOLUTIONS GRADUATE DIPLOMA PAPER I STATISTICAL THEORY & METHODS The Socety provdes these solutos to assst caddates preparg for the examatos future years ad

More information

' 1.00, has the form of a rhomb with

' 1.00, has the form of a rhomb with Problm I Rflcto ad rfracto of lght A A trstg prsm Th ma scto of a glass prsm stuatd ar ' has th form of a rhomb wth A th yllow bam of moochromatc lght propagatg towards th prsm paralll wth th dagoal AC

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

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

Transforms that are commonly used are separable

Transforms that are commonly used are separable Trasforms s Trasforms that are commoly used are separable Eamples: Two-dmesoal DFT DCT DST adamard We ca the use -D trasforms computg the D separable trasforms: Take -D trasform of the rows > rows ( )

More information

Triple Play: From De Morgan to Stirling To Euler to Maclaurin to Stirling

Triple Play: From De Morgan to Stirling To Euler to Maclaurin to Stirling Tripl Play: From D Morga to Stirlig To Eulr to Maclauri to Stirlig Augustus D Morga (186-1871) was a sigificat Victoria Mathmaticia who mad cotributios to Mathmatics History, Mathmatical Rcratios, Mathmatical

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

Statistics 3858 : Likelihood Ratio for Exponential Distribution

Statistics 3858 : Likelihood Ratio for Exponential Distribution Statistics 3858 : Liklihood Ratio for Expotial Distributio I ths two xampl th rjctio rjctio rgio is of th form {x : 2 log (Λ(x)) > c} for a appropriat costat c. For a siz α tst, usig Thorm 9.5A w obtai

More information

Special Instructions / Useful Data

Special Instructions / Useful Data JAM 6 Set of all real umbers P A..d. B, p Posso Specal Istructos / Useful Data x,, :,,, x x Probablty of a evet A Idepedetly ad detcally dstrbuted Bomal dstrbuto wth parameters ad p Posso dstrbuto wth

More information

Chapter 6 Student Lecture Notes 6-1

Chapter 6 Student Lecture Notes 6-1 Chaptr 6 Studnt Lctur Nots 6-1 Chaptr Goals QM353: Busnss Statstcs Chaptr 6 Goodnss-of-Ft Tsts and Contngncy Analyss Aftr compltng ths chaptr, you should b abl to: Us th ch-squar goodnss-of-ft tst to dtrmn

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

ρ < 1 be five real numbers. The

ρ < 1 be five real numbers. The Lecture o BST 63: Statstcal Theory I Ku Zhag, /0/006 Revew for the prevous lecture Deftos: covarace, correlato Examples: How to calculate covarace ad correlato Theorems: propertes of correlato ad covarace

More information

22 Nonparametric Methods.

22 Nonparametric Methods. 22 oparametrc Methods. I parametrc models oe assumes apror that the dstrbutos have a specfc form wth oe or more ukow parameters ad oe tres to fd the best or atleast reasoably effcet procedures that aswer

More information

Eulerian numbers revisited : Slices of hypercube

Eulerian numbers revisited : Slices of hypercube Eulera umbers revsted : Slces of hypercube Kgo Kobayash, Hajme Sato, Mamoru Hosh, ad Hroyosh Morta Abstract I ths talk, we provde a smple proof o a terestg equalty coectg the umber of permutatos of,...,

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

On the Hubbard-Stratonovich Transformation for Interacting Bosons

On the Hubbard-Stratonovich Transformation for Interacting Bosons O h ubbrd-sroovh Trsformo for Irg osos Mr R Zrbur ff Fbrury 8 8 ubbrd-sroovh for frmos: rmdr osos r dffr! Rdom mrs: hyrbol S rsformo md rgorous osus for rg bosos /8 Wyl grou symmry L : G GL V b rrso of

More information

CBSE , ˆj. cos CBSE_2015_SET-1. SECTION A 1. Given that a 2iˆ ˆj. We need to find. 3. Consider the vector equation of the plane.

CBSE , ˆj. cos CBSE_2015_SET-1. SECTION A 1. Given that a 2iˆ ˆj. We need to find. 3. Consider the vector equation of the plane. CBSE CBSE SET- SECTION. Gv tht d W d to fd 7 7 Hc, 7 7 7. Lt,. W ow tht.. Thus,. Cosd th vcto quto of th pl.. z. - + z = - + z = Thus th Cts quto of th pl s - + z = Lt d th dstc tw th pot,, - to th pl.

More information

Dr. Shalabh. Indian Institute of Technology Kanpur

Dr. Shalabh. Indian Institute of Technology Kanpur Aalyss of Varace ad Desg of Expermets-I MODULE -I LECTURE - SOME RESULTS ON LINEAR ALGEBRA, MATRIX THEORY AND DISTRIBUTIONS Dr. Shalabh Departmet t of Mathematcs t ad Statstcs t t Ida Isttute of Techology

More information

Lecture #11. A Note of Caution

Lecture #11. A Note of Caution ctur #11 OUTE uctos rvrs brakdow dal dod aalyss» currt flow (qualtatv)» morty carrr dstrbutos Radg: Chatr 6 Srg 003 EE130 ctur 11, Sld 1 ot of Cauto Tycally, juctos C dvcs ar formd by coutr-dog. Th quatos

More information

Chapter 4 Multiple Random Variables

Chapter 4 Multiple Random Variables Revew for the prevous lecture: Theorems ad Examples: How to obta the pmf (pdf) of U = g (, Y) ad V = g (, Y) Chapter 4 Multple Radom Varables Chapter 44 Herarchcal Models ad Mxture Dstrbutos Examples:

More information

Lecture 14. P-N Junction Diodes: Part 3 Quantitative Analysis (Math, math and more math) Reading: Pierret 6.1

Lecture 14. P-N Junction Diodes: Part 3 Quantitative Analysis (Math, math and more math) Reading: Pierret 6.1 Lctur 4 - ucto ods art 3 Quattatv alyss Math, math ad mor math Radg rrt 6. Gorga Tch ECE 3040 - r. la oolttl Quattatv - od Soluto ssumtos stady stat codtos o- dgrat dog 3 o- dmsoal aalyss 4 low- lvl jcto

More information

Beam Warming Second-Order Upwind Method

Beam Warming Second-Order Upwind Method Beam Warmg Secod-Order Upwd Method Petr Valeta Jauary 6, 015 Ths documet s a part of the assessmet work for the subject 1DRP Dfferetal Equatos o Computer lectured o FNSPE CTU Prague. Abstract Ths documet

More information

Maximum Likelihood Estimation

Maximum Likelihood Estimation Marquette Uverst Maxmum Lkelhood Estmato Dael B. Rowe, Ph.D. Professor Departmet of Mathematcs, Statstcs, ad Computer Scece Coprght 08 b Marquette Uverst Maxmum Lkelhood Estmato We have bee sag that ~

More information

Loop-independent dependence: dependence exists within an iteration; i.e., if the loop is removed, the dependence still exists.

Loop-independent dependence: dependence exists within an iteration; i.e., if the loop is removed, the dependence still exists. Loop-depedet vs. loop-carred depedeces [ 3.] Loop-carred depedece: depedece exsts across teratos;.e., f the loop s removed, the depedece o loger exsts. Loop-depedet depedece: depedece exsts wth a terato;.e.,

More information

Advances of Clar's Aromatic Sextet Theory and Randic 's Conjugated Circuit Model

Advances of Clar's Aromatic Sextet Theory and Randic 's Conjugated Circuit Model Th Op Orgac hmstry Joural 0 5 (Suppl -M6) 87-87 Op Accss Advacs of lar's Aromatc Sxtt Thory ad Radc 's ougatd rcut Modl Fu Zhag a Xaofg Guo a ad Hpg Zhag b a School of Mathmatcal Sccs Xam Uvrsty Xam Fua

More information

Probability & Statistics,

Probability & Statistics, Probability & Statistics, BITS Pilai K K Birla Goa Campus Dr. Jajati Kshari Sahoo Dpartmt of Mathmatics BITS Pilai, K K Birla Goa Campus Poisso Distributio Poisso Distributio: A radom variabl X is said

More information

Review Exercises. 1. Evaluate using the definition of the definite integral as a Riemann Sum. Does the answer represent an area? 2

Review Exercises. 1. Evaluate using the definition of the definite integral as a Riemann Sum. Does the answer represent an area? 2 MATHEMATIS --RE Itgral alculus Marti Huard Witr 9 Rviw Erciss. Evaluat usig th dfiitio of th dfiit itgral as a Rima Sum. Dos th aswr rprst a ara? a ( d b ( d c ( ( d d ( d. Fid f ( usig th Fudamtal Thorm

More information

Perfect Constant-Weight Codes

Perfect Constant-Weight Codes 56 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 50, NO. 9, SEPTEMBER 004 Prfct Costat-Wght Cods Tuv Etzo, Fllo, IEEE, ad Mosh Schartz, Mmbr, IEEE Abstract I hs porg or from 973, Dlsart cocturd that thr

More information

β-spline Estimation in a Semiparametric Regression Model with Nonlinear Time Series Errors

β-spline Estimation in a Semiparametric Regression Model with Nonlinear Time Series Errors Amrca Joural of Appld Sccs, (9): 343-349, 005 ISSN 546-939 005 Scc Publcatos β-spl Estmato a Smparamtrc Rgrsso Modl wth Nolar Tm Srs Errors Jhog You, ma Ch ad 3 Xa Zhou Dpartmt of ostatstcs, Uvrsty of

More information

Time : 1 hr. Test Paper 08 Date 04/01/15 Batch - R Marks : 120

Time : 1 hr. Test Paper 08 Date 04/01/15 Batch - R Marks : 120 Tim : hr. Tst Papr 8 D 4//5 Bch - R Marks : SINGLE CORRECT CHOICE TYPE [4, ]. If th compl umbr z sisfis th coditio z 3, th th last valu of z is qual to : z (A) 5/3 (B) 8/3 (C) /3 (D) o of ths 5 4. Th itgral,

More information

SECTION where P (cos θ, sin θ) and Q(cos θ, sin θ) are polynomials in cos θ and sin θ, provided Q is never equal to zero.

SECTION where P (cos θ, sin θ) and Q(cos θ, sin θ) are polynomials in cos θ and sin θ, provided Q is never equal to zero. SETION 6. 57 6. Evaluation of Dfinit Intgrals Exampl 6.6 W hav usd dfinit intgrals to valuat contour intgrals. It may com as a surpris to larn that contour intgrals and rsidus can b usd to valuat crtain

More information

Adagba O Henry /International Journal Of Computational Engineering Research / ISSN: OPERATION ON IDEALS

Adagba O Henry /International Journal Of Computational Engineering Research / ISSN: OPERATION ON IDEALS Adagba O Hry /Itratoal Joural Of Computatoal Egrg Rsarh / ISSN 50 3005 OPERATION ON IDEALS Adagba O Hry, Dpt of Idustral Mathmats & Appld Statsts, Eboy Stat Uvrsty, Abakalk Abstrat W provd bas opratos

More information

Chapter 2 Infinite Series Page 1 of 11. Chapter 2 : Infinite Series

Chapter 2 Infinite Series Page 1 of 11. Chapter 2 : Infinite Series Chatr Ifiit Sris Pag of Sctio F Itgral Tst Chatr : Ifiit Sris By th d of this sctio you will b abl to valuat imror itgrals tst a sris for covrgc by alyig th itgral tst aly th itgral tst to rov th -sris

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

ON THE LOGARITHMIC INTEGRAL

ON THE LOGARITHMIC INTEGRAL Hacettepe Joural of Mathematcs ad Statstcs Volume 39(3) (21), 393 41 ON THE LOGARITHMIC INTEGRAL Bra Fsher ad Bljaa Jolevska-Tueska Receved 29:9 :29 : Accepted 2 :3 :21 Abstract The logarthmc tegral l(x)

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

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