Superbosonization meets Free Probability
|
|
- Sherman Gaines
- 5 years ago
- Views:
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
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 information3.4 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 informationCorrelation 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 informationLECTURE 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 informationMATHEMATICAL 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 informationIntroduction 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 informationPhase 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 informationNumerical 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 informationUnbalanced 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 informationCounting 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 informationMODEL 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 informationMath 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 informationAotomorphic 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 informationLecture 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 informationReliability 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 informationCOMPLEX 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 informationBinary 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 informationThe 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 informationEstimation 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 informationPion 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 informationAlmost 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 informationChapter 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 informationRepeated 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 informationBorel 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 informationDifferent 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 informationIn 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 informationOn 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 informationWashington 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 informationIndependent 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 informationStatistical 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 informationBacklund 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 informationIntroduction 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 informationChapter 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 informationX 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 informationChiang 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 informationCourse 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 informationSection 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 informationASYMPTOTIC 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 informationTotal 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 informationERDOS-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 informationLarge 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 informationSuzan 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 informationEstimating 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 informationChannel 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 informationThree-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 informationNotation 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 informationControl 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 informationChapter 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 informationME 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 informationDiscrete 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 informationChapter 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 informationMachine 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 informationOrdinary 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 informationTolerance 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 informationSecond 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 informationEE 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 informationOn 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 informationRadial 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 informationComplex 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 informationIntegral 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 informationChapter 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 information1985 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 informationBayesian 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 informationTHE 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
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 informationThe 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 informationNote: 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 informationTransforms 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 informationTriple 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 informationIranian 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 informationStatistics 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 informationSpecial 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 informationChapter 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 informationA 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 informationρ < 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 information22 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 informationEulerian 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 informationA 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 informationOn 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 informationCBSE , 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 informationDr. 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 informationLecture #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 informationChapter 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 informationLecture 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 informationBeam 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 informationMaximum 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 informationLoop-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 informationAdvances 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 informationProbability & 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 informationReview 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 informationPerfect 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 informationβ-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 informationTime : 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 informationSECTION 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 informationAdagba 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 informationChapter 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 informationMA 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 informationON 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 informationComparisons 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 informationLecture 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