New Upper Bounds on A(n,d)
|
|
- Eric Hopkins
- 5 years ago
- Views:
Transcription
1 New Upper Bouds o A(d Beam Mouts Departmet of Mathematcs Techo-Israel Isttute of Techology Hafa 3000 Israel Emal: moutsb@techuxtechoacl Tu Etzo Departmet of Computer Scece Techo-Israel Isttute of Techology Hafa 3000 Israel Emal: etzo@cstechoacl Smo Ltsy Departmet of Electrcal Egeerg Tel A Uersty Ramat-A Israel Emal: ltsy@egtauacl arx:cs/ [csit] 4 Aug 005 Abstract Upper bouds o the maxmum umber of codewords a bary code of a ge legth mmum Hammg dstace are cosdered New bouds are dered by a combato of lear programmg coutg argumets Some of these bouds mproe o the best kow aalytc bouds Seeral ew record bouds are obtaed for codes wth small legths I INTRODUCTION Let A(d deote the maxmum umber of codewords a bary code of legth mmum Hammg dstace d A(d s a basc quatty codg theory Lower bouds o A(d are obtaed by costructos For surey o the kow lower bouds the reader s referred to [9] I ths work we cosder upper bouds o A(d The most basc upper boud o A(d d = e1 s the sphere packg boud also kow as the Hammg boud: A(e1 (1 Johso [8] has mproed the sphere packg boud I hs theorem Johso used the quatty A(dw whch s the maxmum umber of codewords a bary code of legth costat weght w mmum dstace d: A(e1 I [11] a ew boud was obtaed: A(e1 e1 ( e1 e1a(ee1 A(ee1 ( 1 e ( e ea(1ee A(1ee (3 Ths boud s at least as good as the Johso boud for all alues of d for each d there are ftely may alues of for whch the ew boud s better tha the Johso boud Whe someoe s ge specfc relately small alues of d usually the best method to fd upper boud o A( d s the lear programmg (LP boud A summary about ths method some ew upper bouds appeared [11] Howeer the computato of ths boud s ot tractable for large alues of I ths work we wll preset ew upper bouds o A(e1 e 1 Let F = {01} let F deote the set of all bary words of legth For xy F d(xy deote the Hammg dstace betwee x y Ge xy F such that d(xy = k we deote by p k j the umber of words z F such that d(xz = d(zy = j Ths umber s depedet of choce of x y equal to ( k k jk j k f j k s ee p k j = 0 f j k s odd If x = y the p 0 j = δ j = s the umber of words at dstace from x F δ j = 1 f = j zero otherwse We also deote = The p k j s are the tersecto umbers of the Hammg scheme s the alecy of the relato R For the coecto betwee assocato schemes codg theory the reader s referred to [6] [10 Chapter 1] A (Me1 code C s a oempty subset of F of cardalty M mmum Hammg dstace e1 For a wordx F d(xc s the Hammg dstace betweex C e d(xc = m c C d(xc A word h F s called a hole f d(hc > e H = {h F : d(hc > e} s the set of all holes Clearly we hae H = C V(e (4 V(e = j t s the olume of sphere of radus e The dstace dstrbuto of C s defed as the sequece A = {(c 1 c C C : d(c 1 c = } / C for 0 A (c deote the umber of codewords at dstace from c C We also defe the (o-ormalzed holes dstace dstrbuto{d } byd = {(h 1 h H H : d(h 1 h = } D (h deote the umber of holes at dstace from h H Fally we defe NC(hC to be the umber of codewords of C at dstace from a hole h II HOLES DISTANCE DISTRIBUTION I the frst theorem we state that for a ge (Me 1 code C the dstace dstrbuto of the holes s uquely determed by the dstace dstrbuto {A } of the code C Theorem 1: If C s a (Me1 code wth dstace dstrbuto {A } the D = C (R(C V(e
2 for each 0 R(C = δ k p k j k=0 j=1 p k lm p l j l=1 m=1 j=1 A k Corollary 1: Let C be a (Me1 code wth dstace dstrbuto{a } If {q } s a sequece of real umbers the q D = q C q (R(C V(e By usg Corollary 1 for ay ge sequece {q } we obta a lear combato of the D s By fdg a lower boud o ths combato we ca obta a upper boud o the sze of C Example 1: Let q 0 = 1 q = 0 > 0 Clearly 0 = 1 by (5 we hae R(C0 = V(e After substtutg the tral boud D 0 0 to (6 we obta the sphere packg boud (1 The sequece {q } of Corollary 1 wll be called the holes dstace dces (HDI sequece For coeece the rest of the paper we wll wrte {q } stead of {q } I the ext two sectos we wll fd some good HDI sequeces {q } deelop methods to fd lower bouds o q D III HDI SEQUENCES WITH SMALL INDICES I ths secto we cosder HDI sequeces ozero q s correspod to small dces The followg lemma ges a alterate expresso for D Lemma 1: For each 0 D = e k=e1 NC(hCk p k j Ge a sequece {q } by usg Lemma 1 (4 we estmate q D the followg way q D = = q q e k=e1 e q k=e1 NC(hCk NC(hCk p k j p k j (5 (6 ( C V(e q ξ(c{q } (7 ξ(c{q } = max e q k=e1 By combg (6 (7 we obta NC(hCk p k j (8 Theorem : If C s a (Me1 code wth dstace dstrbuto {A } the V(e q(v(e R(C ξ(c{q } proded ξ(c{q } s ot zero ξ(c{q } s ge by (8 R(C s ge by (5 Example : Let q 1 = 1 q = 0 for 1 From (5 (8 we hae R(C1 = V(e 1 p e1 1e e1 p e1 1e pe1 e1e A e1 ξ(c{q } = p e1 1e max {NC(hCe1} Thus usg Theorem we obta Theorem 3: If C s a (Me1 code wth dstace dstrbuto {A } the By substtutg V(e e1 pe1 e1e Ae1 max {NC(hCe1} A e1 A(ee1 max{nc(hce1} A(ee1 (9 we obta the Johso upper boud ( Example 3: Let q 1 = pe e pe1 e 1 pe1 e p e1 1e q = 1 q = 0 for / {1} From (5 (8 we hae (9 q 1 R(C1q R(C = V(e(q 1 1 q p e e ( e1 e (p e1 e1e pe1 ee 1 pe1 ee A e1 p e ee A e ξ(c{q } = p e e max {NC(hCe1NC(hCe} Thus usg Theorem we obta
3 Theorem 4: If C s a (Me 1 code wth dstace dstrbuto {A } the V(e e1 e γ max {NC(hCe1NC(hCe} (10 γ = (p e1 e1e pe1 ee 1 pe1 ee A e1 p e ee A e By substtutg A e1 A e A(1ee max {NC(hCe1NC(hCe} A(1ee (10 we obta the boud of (3 Next we wat to mproe the tral boud o A ge by A A(e We wll fd upper bouds o dstace dstrbuto coeffcetsa s usg lear programmg For a (Me1 code C wth dstace dstrbuto {A } let us deote by LP[e1] the followg system of Delsarte s lear costrats: A P k ( 0 for 0 k 0 A A(e for = e1e A 0 = 1A = 0 for 1 < e1 P k ( = k ( 1j( j k j deote Krawtchouk polyomal of degree k We also deote ñ = 1 let {Ã}ñ be the dstace dstrbuto of the (1Me exteded code C e whch s obtaed from the (Me1 code C wth dstace dstrbuto {A } by addg a ee party bt to each codeword of C It s easy to erfy that for each e1 ñ/ à = A 1 A (11 For the ee weght code C e of legth ñ dstace d = e we deote by LP e [ñe] the followg system of Delsarte s lear costrats: ñ ÃP k ( 0 for 0 k ñ/ 0 à A(ñd for = ee4 ñ/ à 0 = 1à = 0 for 1 < e I some cases we wll add more costrats to obta some specfc bouds as [5] [7] [11] [1] By Theorem 4 we hae that for a (Me1 code C wth dstace dstrbuto {A } the followg holds: Usg (11 we obta 1 e ( e e(a e1a e A(1ee Theorem 5: A(e1 1 e ( e emax{ãe} A(1ee max{ãe} s take subject to LP e [ñe] For the ext result we eed the followg theorem whch s a geeralzato of a theorem ge by Best [3] Theorem 6: Let C be a code of legth mmum Hammg dstace d dstace dstrbuto {A } Let {p } be a sequece of real umbers The there exsts a code C of legth 1 dstace d wth dstace dstrbuto {A } 1 satsfyg 1 ( p A p A (1 It was proed [13] by usg LP that for a ee weght code C of legth ñ 1(mod 4 dstace d = 4 dstace dstrbuto {Ã}ñ à 4 (ñ 1(ñ (ñ 3 (13 4 We substtute p = δ 4 (1 (13 for the upper boud o A 4 to obta Lemma : If C s a ee weght code of legth ñ (mod 4 dstace d = 4 dstace dstrbuto {Ã}ñ the à 4 ñ(ñ (ñ 3 4 We take e = 1 9(mod 1 Sce A(143 = ( 3/6 for 9(mod 1 [10 p 59] t follows by Lemma Theorem 5 that Theorem 7: For 9(mod 1 A( The preous best kow boud A(3 /( 3 for 1(mod 4 was obtaed [4] by LP I partcular we hae A( whch mproes o the preous best kow boud A( [11] IV HDI SEQUENCES WITH LARGE INDICES We demostrate aother approach to estmatg q D ozero elemets of {q } correspod to large dces For each t 0 t e we deote E t = {h H NC(hC t = 1} Note that for ay hole h H we hae NC(hC t {01} 0 t e
4 Lemma 3: For each t 0 t e et E t = C t A p tj (14 Let q 1 = q = 1 q = 0 for / { 1} If h E t for t {01e 1} the If h E e the D 1 (hd (h = 0 (15 D 1 (hd (h e (e1a( eee1 = e (e1 e (16 e1 If for a ge hole h there exsts o codeword at dstace k { e (e 1 1} the D 1 (hd (h 1 (e1a(ee1 = 1 (e1 (17 e1 By combg (14-(17 wth Corollary 1 we obta Theorem 8: A(e1 e(( e (e1 e U( = max{r(c 1R(C ( e 1 et 1 (e1 e1 t=0 ( (e1 1 e e1 e e1 e1 U( (e1 e1 A A p tj p ej } subject to LP[e1] R(C 1 R(C are ge by (5 By Theorem 8 we obta A( A( whch mproe the preous best kow bouds A( [11] A( [14] Let be ee teger let e = 1 By Theorem 8 (11 we obta Theorem 9: If s a ee teger the subject to LP e [ñ4] A(3 3 U( U( = max{6ãñ 3 3Ãñ 1} Usg LP we ca proe the followg lemma Lemma 4: If C s a ee weght code of legth ñ 11(mod 1 dstace d = 4 dstace dstrbuto {Ã}ñ the 6Ãñ 3 3(ñ 1Ãñ 1 (ñ 1(ñ (ñ4 ñ Therefore by Theorem 9 Lemma 4 we hae Theorem 10: For 10(mod 1 A(3 8 3 The preous best kow aalytc boud A(3 (14 8 was obtaed by (3 By smlar argumets f {q } s a sequece wth q = 0 except for q = q 1 = q = 1 we obta the followg boud Theorem 11: If C s a (Me1 code the φ = ( φ U( ( e A(1ee 1 e (1 e ( e A(1 eee ( e U( = max{ A(1ee = ( 1 R(C (1 ( e e et A(1ee A ( e1 (1e( e A(1 eee ( e t=0 p tj (A(1ee t=e 1 et A p tj } subject to LP[ e1] R(C R(C 1 R(C are ge by (5 Applyg Theorem 11 we obta A( whch s better tha the best preously kow boud (see Secto III
5 V GENERALIZATION FOR ARBITRARY METRIC ASSOCIATION SCHEMES We ca geeralze our approach to arbtrary metrc assocato scheme (X R wth dstace fucto d whch cossts of a fte set X together wth a set R of1 relatos defed o X wth certa propertes For the complete defto bref troducto to the assocato schemes the reader s referred to [10 Chapter 1] We exted the deftos from the frst secto as follows X = s the umber of pots of a fte set X s the alecy of the relato R p k j s are the tersecto umbers of the scheme A code C s a oempty subset of X wth mmum dstace e 1 The deftos related to holes dstace dstrbuto are easly geeralzed The results of (4 Lemma 1 Theorems 1 through 4 Corollary 1 are ald for arbtrary metrc assocato schemes As a example we cosder the Johso scheme I ths scheme X s the set of all bary ectors of legth weght w Note that ths scheme the umber of relatos s w 1 has dfferet meag The dstace betwee two ectors s defed to be the half of the Hammg dstace betwee them Oe ca erfy that = ( w = p k j s ge by w k l=0 ( k l k w l w ( ( k w k w j l j l w Deote by T(w 1 1 w d the maxmum umber of bary ectors of legth 1 hag mutual Hammg dstace of at least d each ector has exactly w 1 oes the frst 1 coordates exactly w oes the last coordates By substtutg max{nc(hce1} T(e1we1 w4e (9 we obta the followg boud Theorem 1: A(4ew w w U w ( = max{a e1 } e1 w e1 ( e1 e U w( T(e1we1 w4e subject to Delsarte s lear costrats for Johso scheme (see [10 Theorem 1 p 666] Applyg Theorem 1 for e = 1 we obta the followg mproemets (the alues the paretheses are the best bouds preously kow [1] [14]: A( (50 A( (5064 A( (4080 We would lke to remark that LP ca be appled for upper bouds that obtaed by ceterg a spheres aroud a codewords We ge a example of such boud Theorem 13: A(10w ( ( w w 3 T(3w3 w10 3 w 4 w 4 T(4w4 w10 U w( 5 U w ( = max{ T(4w4 w10 A e 100 T(3w3 w T(4w4 w10 A e1 } subject to Delsarte s lear costrats for Johso scheme By Theorem 13 we hae: A( (81 A( (119 A( (158 A( (99 A( (81 ACKNOWLEDGMENT The work of Beam Mouts was supported part by grat o 63/04 of the Israel Scece Foudato The work of Tu Etzo was supported part by grat o 63/04 of the Israel Scece Foudato The work of Smo Ltsy was supported part by grat o 533/03 of the Israel Scece Foudato REFERENCES [1] E Agrell A Vardy K Zeger Upper bouds for costat-weght codes IEEE Tras o Iform Theory ol 46 pp No 000 [] E Agrell A Vardy K Zeger A table of upper bouds for bary codes IEEE Tras o Iform Theory ol 47 o 7 pp No 001 [3] M R Best Bary codes wth a mmum dstace of four IEEE Tras o Iform Theory ol 6 pp No 1980 [4] M R Best A E Brouwer The Trply Shorteed Bary Hammg Code Is Optmal Dscrete Mathematcs ol 17 pp [5] M R Best A E Brouwer F J MacWllams A M Odlyzko N J A Sloae Bouds for bary codes of legth less tha 5 IEEE Tras o Iform Theory ol 4 pp Ja 1978 [6] Ph Delsarte A algebrac approach to the assocato schemes of codg theory Phlps Research Reports Supplemets No [7] I Hokala Bouds for bary costat weght coerg codes Lcetate thess Departmet of Mathematcs U of Turku Turku Fl Mar 1987 [8] S M Johso A ew upper boud for error-correctg codes IRE Tras o Iform Theory ol 8 pp [9] S Ltsy A updated table of the best bary codes kow Hbook of Codg Theory (V S Pless W C Huffma eds ol 1 pp Amsterdam: Elseer 1998 [10] F J MacWllams N J A Sloae The Theory of Error-Correctg Codes Amsterdam: North-Holl 1977 [11] B Mouts T Etzo S Ltsy Improed Upper Bouds o Szes of Codes IEEE Tras o Iform Theory ol 48 pp Aprl 00 [1] C L N a Pul O bouds o codes Master s thess Dept of Mathematcs Computg Scece Edhoe U of Techology Edhoe the Netherls Aug 198 [13] C Roos C de Vroedt Upper Bouds for A(4 A(6 Dered from Delsarte s Lear Programmg Boud Dscrete Mathematcs ol 40 pp [14] A Schrjer New code upper bouds from the Terwllger algebra preprt Apr 004
18.413: Error Correcting Codes Lab March 2, Lecture 8
18.413: Error Correctg Codes Lab March 2, 2004 Lecturer: Dael A. Spelma Lecture 8 8.1 Vector Spaces A set C {0, 1} s a vector space f for x all C ad y C, x + y C, where we take addto to be compoet wse
More informationLecture 9: Tolerant Testing
Lecture 9: Tolerat Testg Dael Kae Scrbe: Sakeerth Rao Aprl 4, 07 Abstract I ths lecture we prove a quas lear lower boud o the umber of samples eeded to do tolerat testg for L dstace. Tolerat Testg We have
More information1 Onto functions and bijections Applications to Counting
1 Oto fuctos ad bectos Applcatos to Coutg Now we move o to a ew topc. Defto 1.1 (Surecto. A fucto f : A B s sad to be surectve or oto f for each b B there s some a A so that f(a B. What are examples of
More informationNon-uniform Turán-type problems
Joural of Combatoral Theory, Seres A 111 2005 106 110 wwwelsevercomlocatecta No-uform Turá-type problems DhruvMubay 1, Y Zhao 2 Departmet of Mathematcs, Statstcs, ad Computer Scece, Uversty of Illos at
More information9 U-STATISTICS. Eh =(m!) 1 Eh(X (1),..., X (m ) ) i.i.d
9 U-STATISTICS Suppose,,..., are P P..d. wth CDF F. Our goal s to estmate the expectato t (P)=Eh(,,..., m ). Note that ths expectato requres more tha oe cotrast to E, E, or Eh( ). Oe example s E or P((,
More informationOn Eccentricity Sum Eigenvalue and Eccentricity Sum Energy of a Graph
Aals of Pure ad Appled Mathematcs Vol. 3, No., 7, -3 ISSN: 79-87X (P, 79-888(ole Publshed o 3 March 7 www.researchmathsc.org DOI: http://dx.do.org/.7/apam.3a Aals of O Eccetrcty Sum Egealue ad Eccetrcty
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 informationCHAPTER 4 RADICAL EXPRESSIONS
6 CHAPTER RADICAL EXPRESSIONS. The th Root of a Real Number A real umber a s called the th root of a real umber b f Thus, for example: s a square root of sce. s also a square root of sce ( ). s a cube
More informationBounds on the expected entropy and KL-divergence of sampled multinomial distributions. Brandon C. Roy
Bouds o the expected etropy ad KL-dvergece of sampled multomal dstrbutos Brado C. Roy bcroy@meda.mt.edu Orgal: May 18, 2011 Revsed: Jue 6, 2011 Abstract Iformato theoretc quattes calculated from a sampled
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 informationAN UPPER BOUND FOR THE PERMANENT VERSUS DETERMINANT PROBLEM BRUNO GRENET
AN UPPER BOUND FOR THE PERMANENT VERSUS DETERMINANT PROBLEM BRUNO GRENET Abstract. The Permaet versus Determat problem s the followg: Gve a matrx X of determates over a feld of characterstc dfferet from
More informationCIS 800/002 The Algorithmic Foundations of Data Privacy October 13, Lecture 9. Database Update Algorithms: Multiplicative Weights
CIS 800/002 The Algorthmc Foudatos of Data Prvacy October 13, 2011 Lecturer: Aaro Roth Lecture 9 Scrbe: Aaro Roth Database Update Algorthms: Multplcatve Weghts We ll recall aga) some deftos from last tme:
More informationTHE PROBABILISTIC STABILITY FOR THE GAMMA FUNCTIONAL EQUATION
Joural of Scece ad Arts Year 12, No. 3(2), pp. 297-32, 212 ORIGINAL AER THE ROBABILISTIC STABILITY FOR THE GAMMA FUNCTIONAL EQUATION DOREL MIHET 1, CLAUDIA ZAHARIA 1 Mauscrpt receved: 3.6.212; Accepted
More informationGeneralization of the Dissimilarity Measure of Fuzzy Sets
Iteratoal Mathematcal Forum 2 2007 o. 68 3395-3400 Geeralzato of the Dssmlarty Measure of Fuzzy Sets Faramarz Faghh Boformatcs Laboratory Naobotechology Research Ceter vesa Research Isttute CECR Tehra
More information{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution:
Chapter 4 Exercses Samplg Theory Exercse (Smple radom samplg: Let there be two correlated radom varables X ad A sample of sze s draw from a populato by smple radom samplg wthout replacemet The observed
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 informationPTAS for Bin-Packing
CS 663: Patter Matchg Algorthms Scrbe: Che Jag /9/00. Itroducto PTAS for B-Packg The B-Packg problem s NP-hard. If we use approxmato algorthms, the B-Packg problem could be solved polyomal tme. For example,
More 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 informationC-1: Aerodynamics of Airfoils 1 C-2: Aerodynamics of Airfoils 2 C-3: Panel Methods C-4: Thin Airfoil Theory
ROAD MAP... AE301 Aerodyamcs I UNIT C: 2-D Arfols C-1: Aerodyamcs of Arfols 1 C-2: Aerodyamcs of Arfols 2 C-3: Pael Methods C-4: Th Arfol Theory AE301 Aerodyamcs I Ut C-3: Lst of Subects Problem Solutos?
More informationChapter 9 Jordan Block Matrices
Chapter 9 Jorda Block atrces I ths chapter we wll solve the followg problem. Gve a lear operator T fd a bass R of F such that the matrx R (T) s as smple as possble. f course smple s a matter of taste.
More informationStrong Convergence of Weighted Averaged Approximants of Asymptotically Nonexpansive Mappings in Banach Spaces without Uniform Convexity
BULLETIN of the MALAYSIAN MATHEMATICAL SCIENCES SOCIETY Bull. Malays. Math. Sc. Soc. () 7 (004), 5 35 Strog Covergece of Weghted Averaged Appromats of Asymptotcally Noepasve Mappgs Baach Spaces wthout
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 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 informationDecomposition of Hadamard Matrices
Chapter 7 Decomposto of Hadamard Matrces We hae see Chapter that Hadamard s orgal costructo of Hadamard matrces states that the Kroecer product of Hadamard matrces of orders m ad s a Hadamard matrx of
More informationInvestigating Cellular Automata
Researcher: Taylor Dupuy Advsor: Aaro Wootto Semester: Fall 4 Ivestgatg Cellular Automata A Overvew of Cellular Automata: Cellular Automata are smple computer programs that geerate rows of black ad whte
More informationCubic Nonpolynomial Spline Approach to the Solution of a Second Order Two-Point Boundary Value Problem
Joural of Amerca Scece ;6( Cubc Nopolyomal Sple Approach to the Soluto of a Secod Order Two-Pot Boudary Value Problem W.K. Zahra, F.A. Abd El-Salam, A.A. El-Sabbagh ad Z.A. ZAk * Departmet of Egeerg athematcs
More informationInvestigation of Partially Conditional RP Model with Response Error. Ed Stanek
Partally Codtoal Radom Permutato Model 7- vestgato of Partally Codtoal RP Model wth Respose Error TRODUCTO Ed Staek We explore the predctor that wll result a smple radom sample wth respose error whe a
More informationBounds for the Connective Eccentric Index
It. J. Cotemp. Math. Sceces, Vol. 7, 0, o. 44, 6-66 Bouds for the Coectve Eccetrc Idex Nlaja De Departmet of Basc Scece, Humates ad Socal Scece (Mathematcs Calcutta Isttute of Egeerg ad Maagemet Kolkata,
More informationEconometric Methods. Review of Estimation
Ecoometrc Methods Revew of Estmato Estmatg the populato mea Radom samplg Pot ad terval estmators Lear estmators Ubased estmators Lear Ubased Estmators (LUEs) Effcecy (mmum varace) ad Best Lear Ubased Estmators
More informationSTK4011 and STK9011 Autumn 2016
STK4 ad STK9 Autum 6 Pot estmato Covers (most of the followg materal from chapter 7: Secto 7.: pages 3-3 Secto 7..: pages 3-33 Secto 7..: pages 35-3 Secto 7..3: pages 34-35 Secto 7.3.: pages 33-33 Secto
More informationLINEAR REGRESSION ANALYSIS
LINEAR REGRESSION ANALYSIS MODULE V Lecture - Correctg Model Iadequaces Through Trasformato ad Weghtg Dr. Shalabh Departmet of Mathematcs ad Statstcs Ida Isttute of Techology Kapur Aalytcal methods for
More information2.28 The Wall Street Journal is probably referring to the average number of cubes used per glass measured for some population that they have chosen.
.5 x 54.5 a. x 7. 786 7 b. The raked observatos are: 7.4, 7.5, 7.7, 7.8, 7.9, 8.0, 8.. Sce the sample sze 7 s odd, the meda s the (+)/ 4 th raked observato, or meda 7.8 c. The cosumer would more lkely
More informationA tighter lower bound on the circuit size of the hardest Boolean functions
Electroc Colloquum o Computatoal Complexty, Report No. 86 2011) A tghter lower boud o the crcut sze of the hardest Boolea fuctos Masak Yamamoto Abstract I [IPL2005], Fradse ad Mlterse mproved bouds o the
More 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 informationCHAPTER VI Statistical Analysis of Experimental Data
Chapter VI Statstcal Aalyss of Expermetal Data CHAPTER VI Statstcal Aalyss of Expermetal Data Measuremets do ot lead to a uque value. Ths s a result of the multtude of errors (maly radom errors) that ca
More informationX X X E[ ] E X E X. is the ()m n where the ( i,)th. j element is the mean of the ( i,)th., then
Secto 5 Vectors of Radom Varables Whe workg wth several radom varables,,..., to arrage them vector form x, t s ofte coveet We ca the make use of matrx algebra to help us orgaze ad mapulate large umbers
More informationComplete Convergence and Some Maximal Inequalities for Weighted Sums of Random Variables
Joural of Sceces, Islamc Republc of Ira 8(4): -6 (007) Uversty of Tehra, ISSN 06-04 http://sceces.ut.ac.r Complete Covergece ad Some Maxmal Iequaltes for Weghted Sums of Radom Varables M. Am,,* H.R. Nl
More informationD KL (P Q) := p i ln p i q i
Cheroff-Bouds 1 The Geeral Boud Let P 1,, m ) ad Q q 1,, q m ) be two dstrbutos o m elemets, e,, q 0, for 1,, m, ad m 1 m 1 q 1 The Kullback-Lebler dvergece or relatve etroy of P ad Q s defed as m D KL
More informationLINEAR RECURRENT SEQUENCES AND POWERS OF A SQUARE MATRIX
INTEGERS: ELECTRONIC JOURNAL OF COMBINATORIAL NUMBER THEORY 6 2006, #A12 LINEAR RECURRENT SEQUENCES AND POWERS OF A SQUARE MATRIX Hacèe Belbachr 1 USTHB, Departmet of Mathematcs, POBox 32 El Ala, 16111,
More informationComparing Different Estimators of three Parameters for Transmuted Weibull Distribution
Global Joural of Pure ad Appled Mathematcs. ISSN 0973-768 Volume 3, Number 9 (207), pp. 55-528 Research Ida Publcatos http://www.rpublcato.com Comparg Dfferet Estmators of three Parameters for Trasmuted
More informationExercises for Square-Congruence Modulo n ver 11
Exercses for Square-Cogruece Modulo ver Let ad ab,.. Mark True or False. a. 3S 30 b. 3S 90 c. 3S 3 d. 3S 4 e. 4S f. 5S g. 0S 55 h. 8S 57. 9S 58 j. S 76 k. 6S 304 l. 47S 5347. Fd the equvalece classes duced
More informationLattices. Mathematical background
Lattces Mathematcal backgroud Lattces : -dmesoal Eucldea space. That s, { T x } x x = (,, ) :,. T T If x= ( x,, x), y = ( y,, y), the xy, = xy (er product of xad y) x = /2 xx, (Eucldea legth or orm of
More informationFibonacci Identities as Binomial Sums
It. J. Cotemp. Math. Sceces, Vol. 7, 1, o. 38, 1871-1876 Fboacc Idettes as Bomal Sums Mohammad K. Azara Departmet of Mathematcs, Uversty of Evasvlle 18 Lcol Aveue, Evasvlle, IN 477, USA E-mal: azara@evasvlle.edu
More informationBayes (Naïve or not) Classifiers: Generative Approach
Logstc regresso Bayes (Naïve or ot) Classfers: Geeratve Approach What do we mea by Geeratve approach: Lear p(y), p(x y) ad the apply bayes rule to compute p(y x) for makg predctos Ths s essetally makg
More information#A27 INTEGERS 13 (2013) SOME WEIGHTED SUMS OF PRODUCTS OF LUCAS SEQUENCES
#A27 INTEGERS 3 (203) SOME WEIGHTED SUMS OF PRODUCTS OF LUCAS SEQUENCES Emrah Kılıç Mathematcs Departmet, TOBB Uversty of Ecoomcs ad Techology, Akara, Turkey eklc@etu.edu.tr Neşe Ömür Mathematcs Departmet,
More informationEntropy ISSN by MDPI
Etropy 2003, 5, 233-238 Etropy ISSN 1099-4300 2003 by MDPI www.mdp.org/etropy O the Measure Etropy of Addtve Cellular Automata Hasa Aı Arts ad Sceces Faculty, Departmet of Mathematcs, Harra Uversty; 63100,
More informationDimensionality Reduction and Learning
CMSC 35900 (Sprg 009) Large Scale Learg Lecture: 3 Dmesoalty Reducto ad Learg Istructors: Sham Kakade ad Greg Shakharovch L Supervsed Methods ad Dmesoalty Reducto The theme of these two lectures s that
More informationResearch Article A New Iterative Method for Common Fixed Points of a Finite Family of Nonexpansive Mappings
Hdaw Publshg Corporato Iteratoal Joural of Mathematcs ad Mathematcal Sceces Volume 009, Artcle ID 391839, 9 pages do:10.1155/009/391839 Research Artcle A New Iteratve Method for Commo Fxed Pots of a Fte
More informationThe Mathematical Appendix
The Mathematcal Appedx Defto A: If ( Λ, Ω, where ( λ λ λ whch the probablty dstrbutos,,..., Defto A. uppose that ( Λ,,..., s a expermet type, the σ-algebra o λ λ λ are defed s deoted by ( (,,...,, σ Ω.
More informationSome Notes on the Probability Space of Statistical Surveys
Metodološk zvezk, Vol. 7, No., 200, 7-2 ome Notes o the Probablty pace of tatstcal urveys George Petrakos Abstract Ths paper troduces a formal presetato of samplg process usg prcples ad cocepts from Probablty
More informationA unified matrix representation for degree reduction of Bézier curves
Computer Aded Geometrc Desg 21 2004 151 164 wwwelsevercom/locate/cagd A ufed matrx represetato for degree reducto of Bézer curves Hask Suwoo a,,1, Namyog Lee b a Departmet of Mathematcs, Kokuk Uversty,
More informationEstimation of Stress- Strength Reliability model using finite mixture of exponential distributions
Iteratoal Joural of Computatoal Egeerg Research Vol, 0 Issue, Estmato of Stress- Stregth Relablty model usg fte mxture of expoetal dstrbutos K.Sadhya, T.S.Umamaheswar Departmet of Mathematcs, Lal Bhadur
More informationPROJECTION PROBLEM FOR REGULAR POLYGONS
Joural of Mathematcal Sceces: Advaces ad Applcatos Volume, Number, 008, Pages 95-50 PROJECTION PROBLEM FOR REGULAR POLYGONS College of Scece Bejg Forestry Uversty Bejg 0008 P. R. Cha e-mal: sl@bjfu.edu.c
More informationDerivation of 3-Point Block Method Formula for Solving First Order Stiff Ordinary Differential Equations
Dervato of -Pot Block Method Formula for Solvg Frst Order Stff Ordary Dfferetal Equatos Kharul Hamd Kharul Auar, Kharl Iskadar Othma, Zara Bb Ibrahm Abstract Dervato of pot block method formula wth costat
More informationSolving Constrained Flow-Shop Scheduling. Problems with Three Machines
It J Cotemp Math Sceces, Vol 5, 2010, o 19, 921-929 Solvg Costraed Flow-Shop Schedulg Problems wth Three Maches P Pada ad P Rajedra Departmet of Mathematcs, School of Advaced Sceces, VIT Uversty, Vellore-632
More informationCS286.2 Lecture 4: Dinur s Proof of the PCP Theorem
CS86. Lecture 4: Dur s Proof of the PCP Theorem Scrbe: Thom Bohdaowcz Prevously, we have prove a weak verso of the PCP theorem: NP PCP 1,1/ (r = poly, q = O(1)). Wth ths result we have the desred costat
More informationPacking of graphs with small product of sizes
Joural of Combatoral Theory, Seres B 98 (008) 4 45 www.elsever.com/locate/jctb Note Packg of graphs wth small product of szes Alexadr V. Kostochka a,b,,gexyu c, a Departmet of Mathematcs, Uversty of Illos,
More informationQ-analogue of a Linear Transformation Preserving Log-concavity
Iteratoal Joural of Algebra, Vol. 1, 2007, o. 2, 87-94 Q-aalogue of a Lear Trasformato Preservg Log-cocavty Daozhog Luo Departmet of Mathematcs, Huaqao Uversty Quazhou, Fua 362021, P. R. Cha ldzblue@163.com
More informationMarcinkiewicz strong laws for linear statistics of ρ -mixing sequences of random variables
Aas da Academa Braslera de Cêcas 2006 784: 65-62 Aals of the Brazla Academy of Sceces ISSN 000-3765 www.scelo.br/aabc Marckewcz strog laws for lear statstcs of ρ -mxg sequeces of radom varables GUANG-HUI
More informationF. Inequalities. HKAL Pure Mathematics. 進佳數學團隊 Dr. Herbert Lam 林康榮博士. [Solution] Example Basic properties
進佳數學團隊 Dr. Herbert Lam 林康榮博士 HKAL Pure Mathematcs F. Ieualtes. Basc propertes Theorem Let a, b, c be real umbers. () If a b ad b c, the a c. () If a b ad c 0, the ac bc, but f a b ad c 0, the ac bc. Theorem
More informationOn generalized fuzzy mean code word lengths. Department of Mathematics, Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, India
merca Joural of ppled Mathematcs 04; (4): 7-34 Publshed ole ugust 30, 04 (http://www.scecepublshggroup.com//aam) do: 0.648/.aam.04004.3 ISSN: 330-0043 (Prt); ISSN: 330-006X (Ole) O geeralzed fuzzy mea
More informationTESTS BASED ON MAXIMUM LIKELIHOOD
ESE 5 Toy E. Smth. The Basc Example. TESTS BASED ON MAXIMUM LIKELIHOOD To llustrate the propertes of maxmum lkelhood estmates ad tests, we cosder the smplest possble case of estmatg the mea of the ormal
More informationAssignment 5/MATH 247/Winter Due: Friday, February 19 in class (!) (answers will be posted right after class)
Assgmet 5/MATH 7/Wter 00 Due: Frday, February 9 class (!) (aswers wll be posted rght after class) As usual, there are peces of text, before the questos [], [], themselves. Recall: For the quadratc form
More informationChapter 4 (Part 1): Non-Parametric Classification (Sections ) Pattern Classification 4.3) Announcements
Aoucemets No-Parametrc Desty Estmato Techques HW assged Most of ths lecture was o the blacboard. These sldes cover the same materal as preseted DHS Bometrcs CSE 90-a Lecture 7 CSE90a Fall 06 CSE90a Fall
More informationAitken delta-squared generalized Juncgk-type iterative procedure
Atke delta-squared geeralzed Jucgk-type teratve procedure M. De la Se Isttute of Research ad Developmet of Processes. Uversty of Basque Coutry Campus of Leoa (Bzkaa) PO Box. 644- Blbao, 488- Blbao. SPAIN
More informationLebesgue Measure of Generalized Cantor Set
Aals of Pure ad Appled Mathematcs Vol., No.,, -8 ISSN: -8X P), -888ole) Publshed o 8 May www.researchmathsc.org Aals of Lebesgue Measure of Geeralzed ator Set Md. Jahurul Islam ad Md. Shahdul Islam Departmet
More informationFunctions of Random Variables
Fuctos of Radom Varables Chapter Fve Fuctos of Radom Varables 5. Itroducto A geeral egeerg aalyss model s show Fg. 5.. The model output (respose) cotas the performaces of a system or product, such as weght,
More informationBinary Subblock Energy-Constrained Codes: Bounds on Code Size and Asymptotic Rate
217 IEEE Iteratoal ymposum o Iformato Theory (IIT Bary ubblock Eergy-Costraed Codes: Bouds o Code ze ad Asymptotc Rate Ashoo Tado Natoal Uversty of gapore ashootado@gmalcom Ha Mao Kah Nayag Techologcal
More informationMu Sequences/Series Solutions National Convention 2014
Mu Sequeces/Seres Solutos Natoal Coveto 04 C 6 E A 6C A 6 B B 7 A D 7 D C 7 A B 8 A B 8 A C 8 E 4 B 9 B 4 E 9 B 4 C 9 E C 0 A A 0 D B 0 C C Usg basc propertes of arthmetc sequeces, we fd a ad bm m We eed
More informationThird handout: On the Gini Index
Thrd hadout: O the dex Corrado, a tala statstca, proposed (, 9, 96) to measure absolute equalt va the mea dfferece whch s defed as ( / ) where refers to the total umber of dvduals socet. Assume that. The
More informationarxiv:math/ v1 [math.gm] 8 Dec 2005
arxv:math/05272v [math.gm] 8 Dec 2005 A GENERALIZATION OF AN INEQUALITY FROM IMO 2005 NIKOLAI NIKOLOV The preset paper was spred by the thrd problem from the IMO 2005. A specal award was gve to Yure Boreko
More informationA Remark on the Uniform Convergence of Some Sequences of Functions
Advaces Pure Mathematcs 05 5 57-533 Publshed Ole July 05 ScRes. http://www.scrp.org/joural/apm http://dx.do.org/0.436/apm.05.59048 A Remark o the Uform Covergece of Some Sequeces of Fuctos Guy Degla Isttut
More informationCOMPROMISE HYPERSPHERE FOR STOCHASTIC DOMINANCE MODEL
Sebasta Starz COMPROMISE HYPERSPHERE FOR STOCHASTIC DOMINANCE MODEL Abstract The am of the work s to preset a method of rakg a fte set of dscrete radom varables. The proposed method s based o two approaches:
More informationThe Primitive Idempotents in
Iteratoal Joural of Algebra, Vol, 00, o 5, 3 - The Prmtve Idempotets FC - I Kulvr gh Departmet of Mathematcs, H College r Jwa Nagar (rsa)-5075, Ida kulvrsheora@yahoocom K Arora Departmet of Mathematcs,
More informationApplication of Generating Functions to the Theory of Success Runs
Aled Mathematcal Sceces, Vol. 10, 2016, o. 50, 2491-2495 HIKARI Ltd, www.m-hkar.com htt://dx.do.org/10.12988/ams.2016.66197 Alcato of Geeratg Fuctos to the Theory of Success Rus B.M. Bekker, O.A. Ivaov
More informationUNIT 2 SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS
Numercal Computg -I UNIT SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS Structure Page Nos..0 Itroducto 6. Objectves 7. Ital Approxmato to a Root 7. Bsecto Method 8.. Error Aalyss 9.4 Regula Fals Method
More information4 Inner Product Spaces
11.MH1 LINEAR ALGEBRA Summary Notes 4 Ier Product Spaces Ier product s the abstracto to geeral vector spaces of the famlar dea of the scalar product of two vectors or 3. I what follows, keep these key
More informationR t 1. (1 p i ) h(p t 1 ), R t
Multple-Wrte WOM-odes Scott Kayser, Eta Yaaob, Paul H Segel, Alexader Vardy, ad Jac K Wolf Uversty of alfora, Sa Dego La Jolla, A 909 0401, USA Emals: {sayser, eyaaob, psegel, avardy, jwolf}@ucsdedu Abstract
More informationA Study on Generalized Generalized Quasi hyperbolic Kac Moody algebra QHGGH of rank 10
Global Joural of Mathematcal Sceces: Theory ad Practcal. ISSN 974-3 Volume 9, Number 3 (7), pp. 43-4 Iteratoal Research Publcato House http://www.rphouse.com A Study o Geeralzed Geeralzed Quas (9) hyperbolc
More informationMAX-MIN AND MIN-MAX VALUES OF VARIOUS MEASURES OF FUZZY DIVERGENCE
merca Jr of Mathematcs ad Sceces Vol, No,(Jauary 0) Copyrght Md Reader Publcatos wwwjouralshubcom MX-MIN ND MIN-MX VLUES OF VRIOUS MESURES OF FUZZY DIVERGENCE RKTul Departmet of Mathematcs SSM College
More information8.1 Hashing Algorithms
CS787: Advaced Algorthms Scrbe: Mayak Maheshwar, Chrs Hrchs Lecturer: Shuch Chawla Topc: Hashg ad NP-Completeess Date: September 21 2007 Prevously we looked at applcatos of radomzed algorthms, ad bega
More informationmeans the first term, a2 means the term, etc. Infinite Sequences: follow the same pattern forever.
9.4 Sequeces ad Seres Pre Calculus 9.4 SEQUENCES AND SERIES Learg Targets:. Wrte the terms of a explctly defed sequece.. Wrte the terms of a recursvely defed sequece. 3. Determe whether a sequece s arthmetc,
More informationL5 Polynomial / Spline Curves
L5 Polyomal / Sple Curves Cotets Coc sectos Polyomal Curves Hermte Curves Bezer Curves B-Sples No-Uform Ratoal B-Sples (NURBS) Mapulato ad Represetato of Curves Types of Curve Equatos Implct: Descrbe a
More informationPinaki Mitra Dept. of CSE IIT Guwahati
Pak Mtra Dept. of CSE IIT Guwahat Hero s Problem HIGHWAY FACILITY LOCATION Faclty Hgh Way Farm A Farm B Illustrato of the Proof of Hero s Theorem p q s r r l d(p,r) + d(q,r) = d(p,q) p d(p,r ) + d(q,r
More informationPr[X (p + t)n] e D KL(p+t p)n.
Cheroff Bouds Wolfgag Mulzer 1 The Geeral Boud Let P 1,..., m ) ad Q q 1,..., q m ) be two dstrbutos o m elemets,.e.,, q 0, for 1,..., m, ad m 1 m 1 q 1. The Kullback-Lebler dvergece or relatve etroy of
More informationCS5620 Intro to Computer Graphics
CS56 Itro to Computer Graphcs Geometrc Modelg art II Geometrc Modelg II hyscal Sples Curve desg pre-computers Cubc Sples Stadard sple put set of pots { } =, No dervatves specfed as put Iterpolate by cubc
More informationOrdinary Least Squares Regression. Simple Regression. Algebra and Assumptions.
Ordary Least Squares egresso. Smple egresso. Algebra ad Assumptos. I ths part of the course we are gog to study a techque for aalysg the lear relatoshp betwee two varables Y ad X. We have pars of observatos
More 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 informationPGE 310: Formulation and Solution in Geosystems Engineering. Dr. Balhoff. Interpolation
PGE 30: Formulato ad Soluto Geosystems Egeerg Dr. Balhoff Iterpolato Numercal Methods wth MATLAB, Recktewald, Chapter 0 ad Numercal Methods for Egeers, Chapra ad Caale, 5 th Ed., Part Fve, Chapter 8 ad
More informationAnalysis of Lagrange Interpolation Formula
P IJISET - Iteratoal Joural of Iovatve Scece, Egeerg & Techology, Vol. Issue, December 4. www.jset.com ISS 348 7968 Aalyss of Lagrage Iterpolato Formula Vjay Dahya PDepartmet of MathematcsMaharaja Surajmal
More informationThe internal structure of natural numbers, one method for the definition of large prime numbers, and a factorization test
Fal verso The teral structure of atural umbers oe method for the defto of large prme umbers ad a factorzato test Emmaul Maousos APM Isttute for the Advacemet of Physcs ad Mathematcs 3 Poulou str. 53 Athes
More informationLecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model
Lecture 7. Cofdece Itervals ad Hypothess Tests the Smple CLR Model I lecture 6 we troduced the Classcal Lear Regresso (CLR) model that s the radom expermet of whch the data Y,,, K, are the outcomes. The
More informationIdeal multigrades with trigonometric coefficients
Ideal multgrades wth trgoometrc coeffcets Zarathustra Brady December 13, 010 1 The problem A (, k) multgrade s defed as a par of dstct sets of tegers such that (a 1,..., a ; b 1,..., b ) a j = =1 for all
More informationSTATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS. x, where. = y - ˆ " 1
STATISTICAL PROPERTIES OF LEAST SQUARES ESTIMATORS Recall Assumpto E(Y x) η 0 + η x (lear codtoal mea fucto) Data (x, y ), (x 2, y 2 ),, (x, y ) Least squares estmator ˆ E (Y x) ˆ " 0 + ˆ " x, where ˆ
More informationRademacher Complexity. Examples
Algorthmc Foudatos of Learg Lecture 3 Rademacher Complexty. Examples Lecturer: Patrck Rebesch Verso: October 16th 018 3.1 Itroducto I the last lecture we troduced the oto of Rademacher complexty ad showed
More informationQT codes. Some good (optimal or suboptimal) linear codes over F. are obtained from these types of one generator (1 u)-
Mathematca Computato March 03, Voume, Issue, PP-5 Oe Geerator ( u) -Quas-Twsted Codes over F uf Ja Gao #, Qog Kog Cher Isttute of Mathematcs, Naka Uversty, Ta, 30007, Cha Schoo of Scece, Shadog Uversty
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 informationDeduction of Fuzzy Autocatalytic Set to Omega Algebra and Transformation Semigroup
World Academy of Scece Egeerg Techology Iteratoal Joural of Mathematcal Computatoal Physcal Electrcal Computer Egeerg Vol:4 No: 00 Deducto of Fuzzy Autocatalytc Set to Omega Algebra Trasformato Semgroup
More informationResearch Article Some Strong Limit Theorems for Weighted Product Sums of ρ-mixing Sequences of Random Variables
Hdaw Publshg Corporato Joural of Iequaltes ad Applcatos Volume 2009, Artcle ID 174768, 10 pages do:10.1155/2009/174768 Research Artcle Some Strog Lmt Theorems for Weghted Product Sums of ρ-mxg Sequeces
More informationRandom Variables and Probability Distributions
Radom Varables ad Probablty Dstrbutos * If X : S R s a dscrete radom varable wth rage {x, x, x 3,. } the r = P (X = xr ) = * Let X : S R be a dscrete radom varable wth rage {x, x, x 3,.}.If x r P(X = x
More information