Graphs of q-exponentials and q-trigonometric functions

Size: px
Start display at page:

Download "Graphs of q-exponentials and q-trigonometric functions"

Transcription

1 Grahs of -otals ad -trgoomtrc fuctos Amla Carola Saravga To ct ths vrso: Amla Carola Saravga. Grahs of -otals ad -trgoomtrc fuctos. 26. <hal > HAL Id: hal htts://hal.archvs-ouvrts.fr/hal Submttd o 6 Oct 26 HAL s a mult-dsclary o accss archv for th dost ad dssmato of sctfc rsarch documts, whthr thy ar ublshd or ot. Th documts may com from tachg ad rsarch sttutos Frac or abroad, or from ublc or rvat rsarch ctrs. L archv ouvrt lurdsclar HAL, st dsté au déôt t à la dffuso d documts sctfus d vau rchrch, ublés ou o, émaat ds établssmts d sgmt t d rchrch fraças ou étragrs, ds laborators ublcs ou rvés.

2 Grahs of -otals ad -trgoomtrc fuctos Amla Carola Saravga Poltcco d Toro Abstract Hr w wll show th bhavor of som of -fuctos. I artcular w lot th - otal ad th -trgoomtrc fuctos. Sc ths fuctos ar ot grally cludd as softwar routs, a Fortra rogram was cssary to gv thm. Kywords -calculus, -otal, -trgoomtrc fuctos, Tsalls -fuctos. Itroducto Frak Hlto Jackso (87-96) was a Eglsh clrgyma ad mathmatca who systmatcally studd what today s kow as th -calculus. I artcular, h studd som -fuctos ad th -aalogs of drvatv ad tgral []. Actually, Jackso startd hs studs troducg th -dffrc orator, a orator th orgs of whch ca b trackd back to Eduard H ad Lohard Eulr [2]. For ths raso, th -dffrc orator s also kow as th Eulr-H-Jackso orator [3]. Also Carl Frdrch Gauss was volvd th -calculus sc h roosd som rlatos such as th -aalog of bomals [4]. As told [5], th -calculus has varous dalcts, ad for ths raso t s kow as uatum calculus, tm-scal calculus or calculus of arttos [5]. Hr w wll us th otato gv th book by Kac ad Chug [6]. Ths book s dscussg two modfd vrsos of a uatum calculus, dfd as th h-calculus ad th -calculus. Th lttr h dcats th Plack's costat ad th lttr stads for uatum. Lt us ot that ths two vrsos of calculus rduc to Nwto's calculus th lmt whr h or. Hr, th framwork of th -calculus of [6], w wll show th bhavors of som of th - fuctos. I artcular w wll lot th -otal ad of th -trgoomtrc fuctos. W wll s that, th lmt whr, ths fuctos bcom th fuctos commoly usd. Sc ths -fuctos ar ot grally cludd as softwar routs, w rard a Fortra rogram for gvg thm. Bfor showg th rsults, lt us dscuss shortly th -dffrc orator ad th rlatd -drvatv. Th uatum dffrc orator. I th -calculus, th -dffrc s smly gv by: d From ths dffrc, th -drvatv s: D f f f ( ) f ( ) f ( ) f ( ) Th -drvatv rducs to th Nwto s drvatv as. W hav also th h-drvatv: f ( + h) f ( ) D h f h I th lmt as h, ths rducs to th usual drvatv.

3 Takg h ( ), w may s that: f ( + h) f ( ) f ( + ( ) ) f ( ) f ( ) f ( ) Dh f D h From th two formulas for chags wh a uatty h s addd to, whras varabl s multld by a factor. Lt us cosdr th fucto D f ad D h f, w s that D h f cocrs how th fucto f () D f cosdrs how t chags wh th f ( ). If w calculat ts -drvatv, w obta: f () D ( ) Comarg th ordary calculus, gvg, to Euato (), w ca df th tgr [ ] by: ( )' Thrfor Euato () turs out to b: 2 [ ] D [ ] As a cosuc, a -th -drvatv of -drvatv, grats th -factoral: f ( ), whch s obtad by rrtg tms th [ ]! [ ][ ]...[3][2][] Form th -factorals, w ca df -bomal coffcts: [ ]! [ m]![ m]! Ths mas that w ca us th usual Taylor formula, rlacg th drvatvs by th - drvatvs ad th factorals by -factorals. Th -otals As gv [6], th -aalog of th otal fucto s dfd as [ ]! Sc w hav that [6]:

4 ( )( ) L( ) ( ) [ ]! Th -otal has th followg rsso too: ( ) ( )( ) L( ) Aothr -aalog of th otal fucto s [6]: E L A rorty of th -otal fuctos s y + y f y y Du to ths commutato rlato, ad y ar ot symmtrc ad thrfor: y Wh, th -otals bcom th usual otal. W ca s ths th followg Fgur. Th -trgoomtrc fuctos As gv [6], th -aalog of th trgoomtrc fuctos ar dfd as: s 2 ; cos y + 2 S E E 2 ; Cos E + E 2 W hav that: (2) cos Cos + s S. I th Fgur 2, w ca s th grahs of th -trgoomtrc fuctos for two dffrt valus of. Wh, ths fuctos bcom th usual os. To chck th calculato, th Fgur 2, t s show also th valu of cos Cos + s S. It s ual to, as t must b bcaus gv by (2). From th lots, w s that th bhavor of th -trgoomtrc

5 fuctos bcom mor dffrt from that of th trgoomtrc fuctos, wh th valu of s larg. Lt us also ot that wh s clos to, th -fuctos ar clos to cos ad s. Othr -fuctos It s cssary to ot that othr -fuctos st, whch ar lkd to th formulato of th troy gv by Costato Tsalls. I 948 [7], Claud Shao dfd th troy S of a dscrt radom varabl Ξ as th ctd valu of th formato cott: S I log [8]. I ths rsso, I s th formato cott of Ξ, th b robablty of -vt s ad b s th bas of th usd logarthm. Commo valus of th bas ar 2, Eulr s umbr, ad. Tsalls gralzd th Shao troy th followg mar [9]: S Lt us ot that, f w cosdr th -drvatv of th uatty, w hav: D Thrfor, wh : lm D lm S Th, th Tsalls troy s lkd to th -drvatv. But, th framwork of th Tsalls aroach to statstcs, w hav a dformato of th otal fucto, th Tsalls - otal fucto, gv by []: (3) ( ) /( ) [ + ( ) ] ( ) f f ad + ( ) > Ths s dffrt from th -otal rvously dscussd. If th fucto (3) s add Taylor srs, w hav []: (4) ( ) + Q! I (4), w hav ( ) ( 2 )( 3 2) [ ( ) ] Q L. From th rsso (4) of th Tsalls -otal, gv for coml, w ca obta th Tsalls trgoomtrc fuctos: cos + j ( ) j Q (2 j)! 2 j 2 j ; s j ( ) j Q 2 j (2 j+ )! 2 j+

6 Rfrcs. H. Jackso (98). O -fuctos ad a crta dffrc orators, Tras. Roy. Soc. Ed., T. Erst (22). A Comrhsv Tratmt of -Calculus, Srgr Scc & Busss Mda. 3. M. H. Aaby, Z. S. Masour (22). -Fractoal Calculus ad Euatos, Srgr. 4. Raja Roy (2). Sourcs th Dvlomt of Mathmatcs: Srs ad Products from th Fftth to th Twty-frst Ctury, Cambrdg Uvrsty Prss. 5. T. Erst (28). Th dffrt togus of -calculus. Procdgs of th Estoa Acadmy of Sccs, 28, 57, 2, 8 99 DOI:.376/roc V. Kac, Pokma Chug (22). Quatum Calculus, Srgr, Brl. 7. C. E. Shao (948). A Mathmatcal Thory of Commucato. Bll Systm Tchcal Joural 2 (3): DOI:.2/j tb M. Borda (2). Fudamtals Iformato Thory ad Codg. Srgr. ISBN C. Tsalls (988). Possbl Gralzato of Boltzma-Gbbs Statstcs, Joural of Statstcal Physcs, 52: DOI:.7/BF6429. S. Umarov, C. Tsalls, S. Stbrg (28). O a -Ctral Lmt Thorm Cosstt wth Notsv Statstcal Mchacs. Mla J. Math. Brkhausr Vrlag. 76: do:.7/s y. E. P. Borgs (998). O a -gralzato of crcular ad hyrbolc fuctos. J. Phys. A: Math. G

7 Fgur : Bhavor of,, E for dffrt valus of.

8 Fgur 2: -trgoomtrc fuctos for two dffrt valus of.

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

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

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

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

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

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

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

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

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

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

More information

signal amplification; design of digital logic; memory circuits

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

More information

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

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

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

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

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

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

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

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

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

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

A Measure of Inaccuracy between Two Fuzzy Sets

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

More information

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

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

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

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

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

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

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

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

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

Chapter Discrete Fourier Transform

Chapter Discrete Fourier Transform haptr.4 Dscrt Fourr Trasform Itroducto Rcad th xpota form of Fourr srs s Equatos 8 ad from haptr., wt f t 8, h.. T w t f t dt T Wh th abov tgra ca b usd to comput, h.., t s mor prfrab to hav a dscrtzd

More information

On the approximation of the constant of Napier

On the approximation of the constant of Napier Stud. Uiv. Babş-Bolyai Math. 560, No., 609 64 O th approximatio of th costat of Napir Adri Vrscu Abstract. Startig from som oldr idas of [] ad [6], w show w facts cocrig th approximatio of th costat of

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

On the Possible Coding Principles of DNA & I Ching

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

More information

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

(Reference: sections in Silberberg 5 th ed.)

(Reference: sections in Silberberg 5 th ed.) ALE. Atomic Structur Nam HEM K. Marr Tam No. Sctio What is a atom? What is th structur of a atom? Th Modl th structur of a atom (Rfrc: sctios.4 -. i Silbrbrg 5 th d.) Th subatomic articls that chmists

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

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

Extension of Two-Dimensional Discrete Random Variables Conditional Distribution

Extension of Two-Dimensional Discrete Random Variables Conditional Distribution Itratoal Busss Rsarch wwwccstorg/br Extso of Two-Dsoal Dscrt Rado Varabls Codtoal Dstrbuto Fxu Huag Dpartt of Ecoocs, Dala Uvrsty of Tchology Dala 604, Cha E-al: softwar666@63co Chg L Dpartt of Ecoocs,

More information

MB DISTRIBUTION AND ITS APPLICATION USING MAXIMUM ENTROPY APPROACH

MB DISTRIBUTION AND ITS APPLICATION USING MAXIMUM ENTROPY APPROACH Yugoslav Joural of Opratos Rsarch 6 (06), Numbr, 89-98 DOI: 0.98/YJOR405906B MB DISTRIBUTION AND ITS APPLICATION USING MAXIMUM ENTROPY APPROACH Suma BHADRA Rsarch Scholar Dpartmt of Mathmatcs IIEST, Shbpur

More information

Reliability analysis of time - dependent stress - strength system when the number of cycles follows binomial distribution

Reliability analysis of time - dependent stress - strength system when the number of cycles follows binomial distribution raoal Joural of Sascs ad Ssms SSN 97-675 Volum, Numbr 7,. 575-58 sarch da Publcaos h://www.rublcao.com labl aalss of m - dd srss - srgh ssm wh h umbr of ccls follows bomal dsrbuo T.Sumah Umamahswar, N.Swah,

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

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

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

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

More information

Odd Generalized Exponential Flexible Weibull Extension Distribution

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

More information

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

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

More information

NP!= P. By Liu Ran. Table of Contents. The P versus NP problem is a major unsolved problem in computer

NP!= P. By Liu Ran. Table of Contents. The P versus NP problem is a major unsolved problem in computer NP!= P By Lu Ra Table of Cotets. Itroduce 2. Prelmary theorem 3. Proof 4. Expla 5. Cocluso. Itroduce The P versus NP problem s a major usolved problem computer scece. Iformally, t asks whether a computer

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

Thomas J. Osler. 1. INTRODUCTION. This paper gives another proof for the remarkable simple

Thomas J. Osler. 1. INTRODUCTION. This paper gives another proof for the remarkable simple 5/24/5 A PROOF OF THE CONTINUED FRACTION EXPANSION OF / Thomas J Oslr INTRODUCTION This ar givs aothr roof for th rmarkabl siml cotiud fractio = 3 5 / Hr is ay ositiv umbr W us th otatio x= [ a; a, a2,

More information

Comparison of Dual to Ratio-Cum-Product Estimators of Population Mean

Comparison of Dual to Ratio-Cum-Product Estimators of Population Mean Research Joural of Mathematcal ad Statstcal Sceces ISS 30 6047 Vol. 1(), 5-1, ovember (013) Res. J. Mathematcal ad Statstcal Sc. Comparso of Dual to Rato-Cum-Product Estmators of Populato Mea Abstract

More information

Folding of Regular CW-Complexes

Folding of Regular CW-Complexes Ald Mathmatcal Scncs, Vol. 6,, no. 83, 437-446 Foldng of Rgular CW-Comlxs E. M. El-Kholy and S N. Daoud,3. Dartmnt of Mathmatcs, Faculty of Scnc Tanta Unvrsty,Tanta,Egyt. Dartmnt of Mathmatcs, Faculty

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

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

IAEA-CN-184/61 Y. GOTO, T. KATO, K.NIDAIRA. Nuclear Material Control Center, Tokai-mura Japan.

IAEA-CN-184/61 Y. GOTO, T. KATO, K.NIDAIRA. Nuclear Material Control Center, Tokai-mura Japan. IAEA-CN-84/6 Establshmt of accurat calbrato curv for atoal vrfcato at a larg scal ut accoutablt tak RRP - For strgthg stat sstm for mtg safguards oblgato. GOO. KAO K.NIDAIRA Nuclar Matral Cotrol Ctr oka-mura

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

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

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

Fall 2012 Analysis of Experimental Measurements B. Eisenstein/rev. S. Errede. The Multivariate Gaussian/Normal Distribution: (,,...

Fall 2012 Analysis of Experimental Measurements B. Eisenstein/rev. S. Errede. The Multivariate Gaussian/Normal Distribution: (,,... Fall 0 alss of Ertal Masurts B. Esst/rv. S. Errd h Multvarat Gaussa/oral Dstrbuto: Gv ctrd (.. ˆ 0 ) rado varabls,,... : wth: ˆ E [] ˆ 0 ˆ ( th zro {aka th ull} vctor) Suos th ar all Gaussa/orall-dstrbutd

More information

NP!= P. By Liu Ran. Table of Contents. The P vs. NP problem is a major unsolved problem in computer

NP!= P. By Liu Ran. Table of Contents. The P vs. NP problem is a major unsolved problem in computer NP!= P By Lu Ra Table of Cotets. Itroduce 2. Strategy 3. Prelmary theorem 4. Proof 5. Expla 6. Cocluso. Itroduce The P vs. NP problem s a major usolved problem computer scece. Iformally, t asks whether

More information

APPENDIX: STATISTICAL TOOLS

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

More information

Linear Prediction Analysis of

Linear Prediction Analysis of Lr Prdcto Alyss of Sch Souds Brl Ch Drtt of Coutr Scc & Iforto grg Ntol Tw Norl Uvrsty frcs: X Hug t l So Lgug g Procssg Chtrs 5 6 J Dllr t l Dscrt-T Procssg of Sch Sgls Chtrs 4-6 3 J W Pco Sgl odlg tchqus

More information

Parameter, Statistic and Random Samples

Parameter, Statistic and Random Samples Parameter, Statstc ad Radom Samples A parameter s a umber that descrbes the populato. It s a fxed umber, but practce we do ot kow ts value. A statstc s a fucto of the sample data,.e., t s a quatty whose

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

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

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

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

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

More information

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

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

More information

New bounds on Poisson approximation to the distribution of a sum of negative binomial random variables

New bounds on Poisson approximation to the distribution of a sum of negative binomial random variables Sogklaaka J. Sc. Tchol. 4 () 4-48 Ma. -. 8 Ogal tcl Nw bouds o Posso aomato to th dstbuto of a sum of gatv bomal adom vaabls * Kat Taabola Datmt of Mathmatcs Faculty of Scc Buaha Uvsty Muag Chobu 3 Thalad

More information

ONLY AVAILABLE IN ELECTRONIC FORM

ONLY AVAILABLE IN ELECTRONIC FORM OPERTIONS RESERH o.287/opr.8.559c pp. c c8 -copao ONLY VILLE IN ELETRONI FORM fors 28 INFORMS Elctroc opao Optzato Mols of scrt-evt Syst yacs by Wa K (Vctor ha a L Schrub, Opratos Rsarch, o.287/opr.8.559.

More information

Linear Prediction Analysis of Speech Sounds

Linear Prediction Analysis of Speech Sounds Lr Prdcto Alyss of Sch Souds Brl Ch 4 frcs: X Hug t l So Lgug Procssg Chtrs 5 6 J Dllr t l Dscrt-T Procssg of Sch Sgls Chtrs 4-6 3 J W Pco Sgl odlg tchqus sch rcogto rocdgs of th I Stbr 993 5-47 Lr Prdctv

More information

T and V be the total kinetic energy and potential energy stored in the dynamic system. The Lagrangian L, can be defined by

T and V be the total kinetic energy and potential energy stored in the dynamic system. The Lagrangian L, can be defined by From MEC '05 Itrgratg Prosthtcs ad Mdc, Procdgs of th 005 MyoElctrc Cotrols/Powrd Prosthtcs Symposum, hld Frdrcto, Nw Bruswc, Caada, ugust 7-9, 005. EECROMECHNIC NYSIS OF COMPEE RM PROSHESIS (EMS) Prmary

More information

Nuclear Chemistry -- ANSWERS

Nuclear Chemistry -- ANSWERS Hoor Chstry Mr. Motro 5-6 Probl St Nuclar Chstry -- ANSWERS Clarly wrt aswrs o sparat shts. Show all work ad uts.. Wrt all th uclar quatos or th radoactv dcay srs o Urau-38 all th way to Lad-6. Th dcay

More information

Chain Rules for Entropy

Chain Rules for Entropy Cha Rules for Etroy The etroy of a collecto of radom varables s the sum of codtoal etroes. Theorem: Let be radom varables havg the mass robablty x x.x. The...... The roof s obtaed by reeatg the alcato

More information

A NEW GENERALISATION OF SAM-SOLAI S MULTIVARIATE ADDITIVE GAMMA DISTRIBUTION*

A NEW GENERALISATION OF SAM-SOLAI S MULTIVARIATE ADDITIVE GAMMA DISTRIBUTION* A NEW GENERALISATION OF SAM-SOLAI S MULTIVARIATE ADDITIVE GAMMA DISTRIBUTION* Dr. G.S. Davd Sam Jayakumar, Assstant Profssor, Jamal Insttut of Managmnt, Jamal Mohamd Collg, Truchraall 620 020, South Inda,

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

Worksheet: Taylor Series, Lagrange Error Bound ilearnmath.net

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

More information

Grand Canonical Ensemble

Grand Canonical Ensemble Th nsmbl of systms mmrsd n a partcl-hat rsrvor at constant tmpratur T, prssur P, and chmcal potntal. Consdr an nsmbl of M dntcal systms (M =,, 3,...M).. Thy ar mutually sharng th total numbr of partcls

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

A Study of Fundamental Law of Thermal Radiation and Thermal Equilibrium Process

A Study of Fundamental Law of Thermal Radiation and Thermal Equilibrium Process Itratoal Joural of Hgh Ergy Physcs 5; (3): 38-46 Publshd ol May 6, 5 (http://www.sccpublshggroup.com/j/jhp) do:.648/j.jhp.53. ISSN: 376-745 (Prt); ISSN: 376-7448 (Ol) A Study of Fudamtal Law of Thrmal

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

' 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

ON THE COMPLEXITY OF K-STEP AND K-HOP DOMINATING SETS IN GRAPHS

ON THE COMPLEXITY OF K-STEP AND K-HOP DOMINATING SETS IN GRAPHS MATEMATICA MONTISNIRI Vol XL (2017) MATEMATICS ON TE COMPLEXITY OF K-STEP AN K-OP OMINATIN SETS IN RAPS M FARAI JALALVAN AN N JAFARI RA partmnt of Mathmatcs Shahrood Unrsty of Tchnology Shahrood Iran Emals:

More information

Technical Support Document Bias of the Minimum Statistic

Technical Support Document Bias of the Minimum Statistic Tchical Support Documt Bias o th Miimum Stattic Itroductio Th papr pla how to driv th bias o th miimum stattic i a radom sampl o siz rom dtributios with a shit paramtr (also kow as thrshold paramtr. Ths

More information

IFYMB002 Mathematics Business Appendix C Formula Booklet

IFYMB002 Mathematics Business Appendix C Formula Booklet Iteratoal Foudato Year (IFY IFYMB00 Mathematcs Busess Apped C Formula Booklet Related Documet: IFY Mathematcs Busess Syllabus 07/8 IFYMB00 Maths Busess Apped C Formula Booklet Cotets lease ote that the

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

New families of p-ary sequences with low correlation and large linear span

New families of p-ary sequences with low correlation and large linear span THE JOURNAL OF CHINA UNIVERSITIES OF POSTS AND TELECOMMUNICATIONS Volu 4 Issu 4 Dcbr 7 TONG X WEN Qao-ya Nw fals of -ary sucs wth low corrlato ad larg lar sa CLC ubr TN98 Docut A Artcl ID 5-8885 (7 4-53-4

More information

ON THE RELATION BETWEEN THE CAUSAL BESSEL DERIVATIVE AND THE MARCEL RIESZ ELLIPTIC AND HYPERBOLIC KERNELS

ON THE RELATION BETWEEN THE CAUSAL BESSEL DERIVATIVE AND THE MARCEL RIESZ ELLIPTIC AND HYPERBOLIC KERNELS ACENA Vo.. 03-08 005 03 ON THE RELATION BETWEEN THE CAUSAL BESSEL DERIVATIVE AND THE MARCEL RIESZ ELLIPTIC AND HYPERBOLIC KERNELS Rub A. CERUTTI RESUMEN: Cosrao os úcos Rsz coo casos artcuars úco causa

More information

FOURIER SERIES. Series expansions are a ubiquitous tool of science and engineering. The kinds of

FOURIER SERIES. Series expansions are a ubiquitous tool of science and engineering. The kinds of Do Bgyoko () FOURIER SERIES I. INTRODUCTION Srs psos r ubqutous too o scc d grg. Th kds o pso to utz dpd o () th proprts o th uctos to b studd d (b) th proprts or chrctrstcs o th systm udr vstgto. Powr

More information

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

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

More information

Series of New Information Divergences, Properties and Corresponding Series of Metric Spaces

Series of New Information Divergences, Properties and Corresponding Series of Metric Spaces Srs of Nw Iforao Dvrgcs, Proprs ad Corrspodg Srs of Mrc Spacs K.C.Ja, Praphull Chhabra Profssor, Dpar of Mahacs, Malavya Naoal Isu of Tchology, Japur (Rajasha), Ida Ph.d Scholar, Dpar of Mahacs, Malavya

More information

Entropy Equation for a Control Volume

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

More information

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

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

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

More information

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

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

More information

Lucas Test is based on Euler s theorem which states that if n is any integer and a is coprime to n, then a φ(n) 1modn.

Lucas Test is based on Euler s theorem which states that if n is any integer and a is coprime to n, then a φ(n) 1modn. Modul 10 Addtonal Topcs 10.1 Lctur 1 Prambl: Dtrmnng whthr a gvn ntgr s prm or compost s known as prmalty tstng. Thr ar prmalty tsts whch mrly tll us whthr a gvn ntgr s prm or not, wthout gvng us th factors

More information

pn Junction Under Reverse-Bias Conditions 3.3 Physical Operation of Diodes

pn Junction Under Reverse-Bias Conditions 3.3 Physical Operation of Diodes 3.3 Physcal Orato of os Jucto Ur vrs-bas Cotos rft Currt S : ato to th ffuso Currt comot u to majorty carrr ffuso, caus by thrmally grat morty carrrs, thr ar two currt comots lctros mov by rft from to

More information

Study of Correlation using Bayes Approach under bivariate Distributions

Study of Correlation using Bayes Approach under bivariate Distributions Iteratoal Joural of Scece Egeerg ad Techolog Research IJSETR Volume Issue Februar 4 Stud of Correlato usg Baes Approach uder bvarate Dstrbutos N.S.Padharkar* ad. M.N.Deshpade** *Govt.Vdarbha Isttute of

More information

Differentiation of Exponential Functions

Differentiation of Exponential Functions Calculus Modul C Diffrntiation of Eponntial Functions Copyright This publication Th Northrn Albrta Institut of Tchnology 007. All Rights Rsrvd. LAST REVISED March, 009 Introduction to Diffrntiation of

More information

ON ESTIMATION OF STRESS STRENGTH MODEL FOR GENERALIZED INVERTED EXPONENTIAL DISTRIBUTION

ON ESTIMATION OF STRESS STRENGTH MODEL FOR GENERALIZED INVERTED EXPONENTIAL DISTRIBUTION Joural of Rlablt ad Statstcal Studs; ISSN Prt: 974-84, Ol:9-5666 Vol. 6, Issu 3: 55-63 ON ESTIMATION OF STRESS STRENGTH MODEL FOR GENERALIZED INVERTED EXPONENTIAL DISTRIBUTION Mohad A. Hussa Dpartt of

More information