8. Queueing systems. Contents. Simple teletraffic model. Pure queueing system

Size: px
Start display at page:

Download "8. Queueing systems. Contents. Simple teletraffic model. Pure queueing system"

Transcription

1 8. Quug sysms Cos 8. Quug sysms Rfrshr: Sml lraffc modl Quug dscl M/M/ srvr wag lacs Alcao o ack lvl modllg of daa raffc M/M/ srvrs wag lacs lc8. S Iroduco o Tlraffc Thory Srg 5 8. Quug sysms 8. Quug sysms Sml lraffc modl ur quug sysm Cusomrs arrv a ra cusomrs r m u / avrag r-arrval m Cusomrs ar srvd by aralll srvrs h busy a srvr srvs a ra cusomrs r m u / avrag srvc m of a cusomr Thr ar m cusomr lacs h sysm a las srvc lacs ad a mos m wag lacs I s assumd ha blockd cusomrs arrvg a full sysm ar los F umbr of srvrs < srvc lacs f umbr of wag lacs m If all srvrs ar occud wh a cusomr arrvs occus o of h wag lacs No cusomrs ar los bu som of hm hav o wa bfor gg srvd From h cusomr s o of vw s rsg o kow.g. wha s h robably ha has o wa oo log? m 3 4

2 8. Quug sysms 8. Quug sysms Cos Quug dscl Rfrshr: Sml lraffc modl Quug dscl M/M/ srvr wag lacs Alcao o ack lvl modllg of daa raffc M/M/ srvrs wag lacs Cosdr a sgl srvr quug sysm Quug dscl drms h way h srvr srvs h cusomrs I lls whhr h cusomrs ar srvd o-by-o or smulaously Furhrmor f h cusomrs ar srvd o-by-o lls whch ordr hy ar ak o h srvc Ad f h cusomrs ar srvd smulaously lls how h srvc caacy s shard amog hm No: I comur sysms h corrsodg coc s schdulg A quug dscl s calld work-cosrvg f cusomrs ar srvd wh full srvc ra whvr h sysm s o-my Quug sysms 8. Quug sysms ork-cosrvg quug dscls Cos Frs I Frs Ou FIFO Frs Com Frs Srvd FCFS ordary quug dscl quu arrval ordr srvc ordr cusomrs srvd o-by-o wh full srvc ra always srv h cusomr ha has b wag for h logs m dfaul quug dscl hs lcur Las I Frs Ou LIFO Las Com Frs Srvd LCFS rvrsd quug dscl sack cusomrs srvd o-by-o wh full srvc ra always srv h cusomr ha has b wag for h shors m rocssor Sharg S far quug cusomrs srvd smulaously wh cusomrs h sysm ach of hm srvd wh qual ra / s Lcur 9. Sharg sysms 7 Rfrshr: Sml lraffc modl Quug dscl M/M/ srvr wag lacs Alcao o ack lvl modllg of daa raffc M/M/ srvrs wag lacs 8

3 8. Quug sysms 8. Quug sysms M/M/ quu Rlad radom varabls Cosdr h followg sml lraffc modl: If umbr of dd cusomrs k Irarrval ms ar II ad xoally dsrbud wh ma / so cusomrs arrv accordg o a osso rocss wh sy O srvr Srvc ms ar II ad xoally dsrbud wh ma / If umbr of wag lacs m faul quug dscl: FIFO Usg Kdall s oao hs s a M/M/ quu mor rcsly: M/M/-FIFO quu Noao: /raffc load umbr of cusomrs h sysm a a arbrary m quu lgh qulbrum umbr of cusomrs h sysm a a ycal arrval m quu lgh s by a arrvg cusomr wag m of a ycal cusomr S srvc m of a ycal cusomr S oal m h sysm of a ycal cusomr dlay 9 8. Quug sysms 8. Quug sysms Sa raso dagram qulbrum dsrbuo L do h umbr of cusomrs h sysm a m Assum ha a som m ad cosdr wha has durg a shor m rval h: wh rob. h oh a w cusomr arrvs sa raso f h wh rob. h oh a cusomr lavs h sysm sa raso rocss s clarly a Markov rocss wh sa raso dagram No ha rocss s a rrducbl brh-dah rocss wh a f sa sac S... Local balac quaos LB: LB K N f < Normalzg codo N:

4 8. Quug sysms 8. Quug sysms qulbrum dsrbuo Ma quu lgh vs. raffc load Thus for a sabl sysm < h qulbrum dsrbuo xss ad s a gomrc dsrbuo: < Gom K Rmark: Ths rsul s vald for ay work-cosrvg quug dscl FIFO LIFO S... Ths rsul s o ssv o h srvc m dsrbuo for FIFO v h ma quu lgh dds o h dsrbuo Howvr for ay symmrc quug dscl such as LIFO or S h rsul s dd ssv o h srvc m dsrbuo Traffc load 4 8. Quug sysms 8. Quug sysms Ma dlay Ma dlay vs. raffc load L do h oal m dlay h sysm of a ycal cusomr cludg boh h wag m ad h srvc m S: S Ll s formula:. Thus Rmark: Th ma dlay s h sam for all work-cosrvg quug dscls FIFO LIFO S Bu h varac ad ohr moms ar dffr Traffc load 5 6

5 7 8. Quug sysms Ma wag m L do h wag m of a ycal cusomr Sc S w hav S 8 8. Quug sysms ag m dsrbuo L do h wag m of a ycal cusomr L do h umbr of cusomrs h sysm a h arrval m ASTA:. Assum ow for a whl ha Srvc ms S S of h wag cusomrs ar II ad x u o h mmorylss rory of h xoal dsrbuo h rmag srvc m S of h cusomr srvc also follows x-dsrbuo ad s dd of vryhg ls u o h FIFO quug dscl S S S Cosruc a osso o rocss τ by dfg τ S ad τ S S S. Now sc : τ τ τ τ τ S S S 3 τ3 S S 9 8. Quug sysms ag m dsrbuo Sc w hav o by A h osso cour rocss corrsodg o τ I follows ha: τ A O h ohr had w kow ha A osso. Thus τ τ A τ 9 8. Quug sysms ag m dsrbuo 3 By combg h rvous formulas w g τ

6 8. Quug sysms 8. Quug sysms ag m dsrbuo 4 Cos ag m ca hus b rsd as a roduc J of wo dd radom varabls J Broull ad x: J J J J Rfrshr: Sml lraffc modl Quug dscl M/M/ srvr wag lacs Alcao o ack lvl modllg of daa raffc M/M/ srvrs wag lacs 8. Quug sysms 8. Quug sysms Alcao o ack lvl modllg of daa raffc Mullxg ga M/M/ modl may b ald o som x o ack lvl modllg of daa raffc cusomr I ack ack arrval ra acks r m u / avrag ack rasmsso m akayks. / raffc load Qualy of srvc s masurd.g. by h ack dlay z robably ha a ack has o wa oo log.. logr ha a gv rfrc valu z z z z drm load so ha rob. z < % for z m us Mullxg ga s dscrbd by h raffc load as a fuco of h srvc ra load srvc ra 4

7 8. Quug sysms 8. Quug sysms Cos M/M/ quu Rfrshr: Sml lraffc modl Quug dscl M/M/ srvr wag lacs Alcao o ack lvl modllg of daa raffc M/M/ srvrs wag lacs Cosdr h followg sml lraffc modl: If umbr of dd cusomrs k Irarrval ms ar II ad xoally dsrbud wh ma / so cusomrs arrv accordg o a osso rocss wh sy F umbr of srvrs < Srvc ms ar II ad xoally dsrbud wh ma / If umbr of wag lacs m faul quug dscl: FCFS Usg Kdall s oao hs s a M/M/ quu mor rcsly: M/M/-FCFS quu Noao: / raffc load Quug sysms 8. Quug sysms Sa raso dagram qulbrum dsrbuo L do h umbr of cusomrs h sysm a m Assum ha a som m ad cosdr wha has durg a shor m rval h: wh rob. h oh a w cusomr arrvs sa raso f h wh rob. mh oh a cusomr lavs h sysm sa raso rocss s clarly a Markov rocss wh sa raso dagram No ha rocss s a rrducbl brh-dah rocss wh a f sa sac S... 7 Local balac quaos LB for < : K Local balac quaos LB for : LB LB K 8

8 9 8. Quug sysms qulbrum dsrbuo Normalzg codo N: Noao : f N < Quug sysms qulbrum dsrbuo 3 Thus for a sabl sysm < ha s: < h qulbrum dsrbuo xss ad s as follows: < : : K K 3 8. Quug sysms robably of wag L do h robably ha a arrvg cusomr has o wa L do h umbr of cusomrs h sysm a a arrval m A arrvg cusomr has o wa whvr all h srvrs ar occud a hr arrval m. Thus ASTA:. Thus : : Quug sysms Ma umbr of wag cusomrs L do h umbr of wag cusomrs qulbrum Th 3 : :

9 33 8. Quug sysms Ma wag m L do h wag m of a ycal cusomr Ll s formula:. Thus : : Quug sysms Ma dlay L do h oal m dlay h sysm of a ycal cusomr cludg boh h wag m ad h srvc m S: S Th S : : Quug sysms Ma quu lgh L do h umbr of cusomrs h sysm quu lgh qulbrum Ll s formula:. Thus : : Quug sysms ag m dsrbuo L do h wag m of a ycal cusomr L do h umbr of cusomrs h sysm a h arrval m Th cusomr has o wa oly f. Ths has wh rob.. Udr h assumo ha h sysm howvr looks lk a ordary M/M/ quu wh arrval ra ad srvc ra. L do h wag m of a ycal cusomr hs M/M/ quu L do h umbr of cusomrs h sysm a h arrval m I follows ha ' '

10 37 8. Quug sysms ag m dsrbuo ag m ca hus b rsd as a roduc J of wo d. radom varabls J Broull ad x: ' ' ' J J J J Quug sysms xaml rr roblm Cosdr h followg wo dffr cofguraos: O rad rr II rg ms x Two slowr aralll rrs II rg ms x Crro: mmz ma dlay O rad rr M/M/ modl wh /: Two slowr rrs M/M/ modl wh /: Quug sysms xaml Traffc load /

8. Queueing systems lect08.ppt S Introduction to Teletraffic Theory - Fall

8. Queueing systems lect08.ppt S Introduction to Teletraffic Theory - Fall 8. Queueg sysems lec8. S-38.45 - Iroduco o Teleraffc Theory - Fall 8. Queueg sysems Coes Refresher: Smle eleraffc model M/M/ server wag laces M/M/ servers wag laces 8. Queueg sysems Smle eleraffc model

More information

Gauge Theories. Elementary Particle Physics Strong Interaction Fenomenology. Diego Bettoni Academic year

Gauge Theories. Elementary Particle Physics Strong Interaction Fenomenology. Diego Bettoni Academic year Gau Thors Elmary Parcl Physcs Sro Iraco Fomoloy o Bo cadmc yar - Gau Ivarac Gau Ivarac Whr do Laraas or Hamloas com from? How do w kow ha a cra raco should dscrb a acual hyscal sysm? Why s h lcromac raco

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

Chapter 4. Continuous Time Markov Chains. Babita Goyal

Chapter 4. Continuous Time Markov Chains. Babita Goyal Chapr 4 Couous Tm Markov Chas Baba Goyal Ky words: Couous m sochasc procsss, Posso procss, brh procss, dah procss, gralzd brh-dah procss, succssv occurrcs, r-arrval m. Suggsd radgs:. Mdh, J. (996, Sochasc

More information

Chap 2: Reliability and Availability Models

Chap 2: Reliability and Availability Models Chap : lably ad valably Modls lably = prob{s s fully fucog [,]} Suppos from [,] m prod, w masur ou of N compos, of whch N : # of compos oprag corrcly a m N f : # of compos whch hav fald a m rlably of h

More information

Dr. Junchao Xia Center of Biophysics and Computational Biology. Fall /21/2016 1/23

Dr. Junchao Xia Center of Biophysics and Computational Biology. Fall /21/2016 1/23 BIO53 Bosascs Lcur 04: Cral Lm Thorm ad Thr Dsrbuos Drvd from h Normal Dsrbuo Dr. Juchao a Cr of Bophyscs ad Compuaoal Bology Fall 06 906 3 Iroduco I hs lcur w wll alk abou ma cocps as lsd blow, pcd valu

More information

Advanced Queueing Theory. M/G/1 Queueing Systems

Advanced Queueing Theory. M/G/1 Queueing Systems Advand Quung Thory Ths slds ar rad by Dr. Yh Huang of Gorg Mason Unvrsy. Sudns rgsrd n Dr. Huang's ourss a GMU an ma a sngl mahn-radabl opy and prn a sngl opy of ah sld for hr own rfrn, so long as ah sld

More information

Control Systems (Lecture note #6)

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

More information

Lecture 12: Introduction to nonlinear optics II.

Lecture 12: Introduction to nonlinear optics II. Lcur : Iroduco o olar opcs II r Kužl ropagao of srog opc sgals propr olar ffcs Scod ordr ffcs! Thr-wav mxg has machg codo! Scod harmoc grao! Sum frqucy grao! aramrc grao Thrd ordr ffcs! Four-wav mxg! Opcal

More information

Two-Dimensional Quantum Harmonic Oscillator

Two-Dimensional Quantum Harmonic Oscillator D Qa Haroc Oscllaor Two-Dsoal Qa Haroc Oscllaor 6 Qa Mchacs Prof. Y. F. Ch D Qa Haroc Oscllaor D Qa Haroc Oscllaor ch5 Schrödgr cosrcd h cohr sa of h D H.O. o dscrb a classcal arcl wh a wav ack whos cr

More information

CHAPTER: 3 INVERSE EXPONENTIAL DISTRIBUTION: DIFFERENT METHOD OF ESTIMATIONS

CHAPTER: 3 INVERSE EXPONENTIAL DISTRIBUTION: DIFFERENT METHOD OF ESTIMATIONS CHAPTER: 3 INVERSE EXPONENTIAL DISTRIBUTION: DIFFERENT METHOD OF ESTIMATIONS 3. INTRODUCTION Th Ivrs Expoal dsrbuo was roducd by Kllr ad Kamah (98) ad has b sudd ad dscussd as a lfm modl. If a radom varabl

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

Multi-fluid magnetohydrodynamics in the solar atmosphere

Multi-fluid magnetohydrodynamics in the solar atmosphere Mul-flud magohydrodyams h solar amoshr Tmuraz Zaqarashvl თეიმურაზ ზაქარაშვილი Sa Rsarh Isu of Ausra Aadmy of Ss Graz Ausra ISSI-orksho Parally ozd lasmas asrohyss 6 Jauary- Fbruary 04 ISSI-orksho Parally

More information

MECE 3320 Measurements & Instrumentation. Static and Dynamic Characteristics of Signals

MECE 3320 Measurements & Instrumentation. Static and Dynamic Characteristics of Signals MECE 330 MECE 330 Masurms & Isrumao Sac ad Damc Characrscs of Sgals Dr. Isaac Chouapall Dparm of Mchacal Egrg Uvrs of Txas Pa Amrca MECE 330 Sgal Cocps A sgal s h phscal formao abou a masurd varabl bg

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

Mathematical Statistics. Chapter VIII Sampling Distributions and the Central Limit Theorem

Mathematical Statistics. Chapter VIII Sampling Distributions and the Central Limit Theorem Mahmacal ascs 8 Chapr VIII amplg Dsrbos ad h Cral Lm Thorm Fcos of radom arabls ar sall of rs sascal applcao Cosdr a s of obsrabl radom arabls L For ampl sppos h arabls ar a radom sampl of s from a poplao

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

Anouncements. Conjugate Gradients. Steepest Descent. Outline. Steepest Descent. Steepest Descent

Anouncements. Conjugate Gradients. Steepest Descent. Outline. Steepest Descent. Steepest Descent oucms Couga Gas Mchal Kazha (6.657) Ifomao abou h Sma (6.757) hav b pos ol: hp://www.cs.hu.u/~msha Tch Spcs: o M o Tusay afoo. o Two paps scuss ach w. o Vos fo w s caa paps u by Thusay vg. Oul Rvw of Sps

More information

t=0 t>0: + vr - i dvc Continuation

t=0 t>0: + vr - i dvc Continuation hapr Ga Dlay and rcus onnuaon s rcu Equaon >: S S Ths dffrnal quaon, oghr wh h nal condon, fully spcfs bhaor of crcu afr swch closs Our n challng: larn how o sol such quaons TUE/EE 57 nwrk analys 4/5 NdM

More information

CHAPTER 7. X and 2 = X

CHAPTER 7. X and 2 = X CHATR 7 Sco 7-7-. d r usd smors o. Th vrcs r d ; comr h S vrc hs cs / / S S Θ Θ Sc oh smors r usd mo o h vrcs would coclud h s h r smor wh h smllr vrc. 7-. [ ] Θ 7 7 7 7 7 7 [ ] Θ ] [ 7 6 Boh d r usd sms

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

Consider a system of 2 simultaneous first order linear equations

Consider a system of 2 simultaneous first order linear equations Soluon of sysms of frs ordr lnar quaons onsdr a sysm of smulanous frs ordr lnar quaons a b c d I has h alrna mar-vcor rprsnaon a b c d Or, n shorhand A, f A s alrady known from con W know ha h abov sysm

More information

Asymptotic Behavior of Finite-Time Ruin Probability in a By-Claim Risk Model with Constant Interest Rate

Asymptotic Behavior of Finite-Time Ruin Probability in a By-Claim Risk Model with Constant Interest Rate Th Uvrsy of Souhr Msssspp Th Aqula Dgal Commuy Sud ublcaos 8-5-4 Asympoc Bhavor of F-Tm Ru robably a By-Clam Rs Modl wh Cosa Irs Ra L Wag Uvrsy of Souhr Msssspp Follow hs ad addoal wors a: hps://aqula.usm.du/sud_pubs

More information

Summary: Solving a Homogeneous System of Two Linear First Order Equations in Two Unknowns

Summary: Solving a Homogeneous System of Two Linear First Order Equations in Two Unknowns Summary: Solvng a Homognous Sysm of Two Lnar Frs Ordr Equaons n Two Unknowns Gvn: A Frs fnd h wo gnvalus, r, and hr rspcv corrspondng gnvcors, k, of h coffcn mar A Dpndng on h gnvalus and gnvcors, h gnral

More information

The Poisson Process Properties of the Poisson Process

The Poisson Process Properties of the Poisson Process Posso Processes Summary The Posso Process Properes of he Posso Process Ierarrval mes Memoryless propery ad he resdual lfeme paradox Superposo of Posso processes Radom seleco of Posso Pos Bulk Arrvals ad

More information

Almost unbiased exponential estimator for the finite population mean

Almost unbiased exponential estimator for the finite population mean Almos ubasd poal smaor for f populao ma Rajs Sg, Pakaj aua, ad rmala Saa, Scool of Sascs, DAVV, Idor (M.P., Ida (rsgsa@aoo.com Flor Smaradac ar of Dparm of Mamacs, Uvrs of Mco, Gallup, USA (smarad@um.du

More information

k of the incident wave) will be greater t is too small to satisfy the required kinematics boundary condition, (19)

k of the incident wave) will be greater t is too small to satisfy the required kinematics boundary condition, (19) TOTAL INTRNAL RFLTION Kmacs pops Sc h vcos a coplaa, l s cosd h cd pla cocds wh h X pla; hc 0. y y y osd h cas whch h lgh s cd fom h mdum of hgh dx of faco >. Fo cd agls ga ha h ccal agl s 1 ( /, h hooal

More information

An N-Component Series Repairable System with Repairman Doing Other Work and Priority in Repair

An N-Component Series Repairable System with Repairman Doing Other Work and Priority in Repair Mor ppl Novmbr 8 N-Compo r Rparabl m h Rparma Dog Ohr ork a ror Rpar Jag Yag E-mal: jag_ag7@6om Xau Mg a uo hg ollag arb Normal Uvr Yaq ua Taoao ag uppor b h Fouao or h aural o b prov o Cha 5 uppor b h

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

On the Hubbard-Stratonovich Transformation for Interacting Bosons

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

More information

On the Existence and uniqueness for solution of system Fractional Differential Equations

On the Existence and uniqueness for solution of system Fractional Differential Equations OSR Jourl o Mhms OSR-JM SSN: 78-578. Volum 4 ssu 3 Nov. - D. PP -5 www.osrjourls.org O h Es d uquss or soluo o ssm rol Drl Equos Mh Ad Al-Wh Dprm o Appld S Uvrs o holog Bghdd- rq Asr: hs ppr w d horm o

More information

Lecture 1: Numerical Integration The Trapezoidal and Simpson s Rule

Lecture 1: Numerical Integration The Trapezoidal and Simpson s Rule Lcur : Numrical ngraion Th Trapzoidal and Simpson s Rul A problm Th probabiliy of a normally disribud (man µ and sandard dviaion σ ) vn occurring bwn h valus a and b is B A P( a x b) d () π whr a µ b -

More information

Let's revisit conditional probability, where the event M is expressed in terms of the random variable. P Ax x x = =

Let's revisit conditional probability, where the event M is expressed in terms of the random variable. P Ax x x = = L's rvs codol rol whr h v M s rssd rs o h rdo vrl. L { M } rrr v such h { M } Assu. { } { A M} { A { } } M < { } { } A u { } { } { A} { A} ( A) ( A) { A} A A { A } hs llows us o cosdr h cs wh M { } [ (

More information

By choosing to view this document, you agree to all provisions of the copyright laws protecting it.

By choosing to view this document, you agree to all provisions of the copyright laws protecting it. oyrh I. Rrd from " PRODING Aual RLIAILITY ad MAINTAINAILITY ymosum" UA Jauary -. Ths maral s osd hr wh rmsso of h I. uch rmsso of h I dos o ay way mly I dorsm of ay of Rlaof ororao's roducs or srvcs. Iral

More information

ASYMPTOTIC BEHAVIOR OF FINITE-TIME RUIN PROBABILITY IN A BY-CLAIM RISK MODEL WITH CONSTANT INTEREST RATE

ASYMPTOTIC BEHAVIOR OF FINITE-TIME RUIN PROBABILITY IN A BY-CLAIM RISK MODEL WITH CONSTANT INTEREST RATE Joural of Mahmacs ad Sascs 3: 339-357 4 ISSN: 549-3644 4 Scc Publcaos do:.3844/mssp.4.339.357 Publshd Ol 3 4 hp://www.hscpub.com/mss.oc ASYMPTOTIC BEHAVIOR OF FINITE-TIME RUIN PROBABILITY IN A BY-CLAIM

More information

Least squares and motion. Nuno Vasconcelos ECE Department, UCSD

Least squares and motion. Nuno Vasconcelos ECE Department, UCSD Las squars ad moo uo Vascoclos ECE Dparm UCSD Pla for oda oda w wll dscuss moo smao hs s rsg wo was moo s vr usful as a cu for rcogo sgmao comprsso c. s a gra ampl of las squars problm w wll also wrap

More information

The Linear Regression Of Weighted Segments

The Linear Regression Of Weighted Segments The Lear Regresso Of Weghed Segmes George Dael Maeescu Absrac. We proposed a regresso model where he depede varable s made o up of pos bu segmes. Ths suao correspods o he markes hroughou he da are observed

More information

1973 AP Calculus BC: Section I

1973 AP Calculus BC: Section I 97 AP Calculus BC: Scio I 9 Mius No Calculaor No: I his amiaio, l dos h aural logarihm of (ha is, logarihm o h bas ).. If f ( ) =, h f ( ) = ( ). ( ) + d = 7 6. If f( ) = +, h h s of valus for which f

More information

14. Poisson Processes

14. Poisson Processes 4. Posso Processes I Lecure 4 we roduced Posso arrvals as he lmg behavor of Bomal radom varables. Refer o Posso approxmao of Bomal radom varables. From he dscusso here see 4-6-4-8 Lecure 4 " arrvals occur

More information

Poisson Arrival Process

Poisson Arrival Process 1 Poisso Arrival Procss Arrivals occur i) i a mmorylss mar ii) [ o arrival durig Δ ] = λδ + ( Δ ) P o [ o arrival durig Δ ] = 1 λδ + ( Δ ) P o P j arrivals durig Δ = o Δ for j = 2,3, ( ) o Δ whr lim =

More information

Least Squares Fitting (LSQF) with a complicated function Theexampleswehavelookedatsofarhavebeenlinearintheparameters

Least Squares Fitting (LSQF) with a complicated function Theexampleswehavelookedatsofarhavebeenlinearintheparameters Leas Squares Fg LSQF wh a complcaed fuco Theeampleswehavelookedasofarhavebeelearheparameers ha we have bee rg o deerme e.g. slope, ercep. For he case where he fuco s lear he parameers we ca fd a aalc soluo

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

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

Response of LTI Systems to Complex Exponentials

Response of LTI Systems to Complex Exponentials 3 Fourir sris coiuous-im Rspos of LI Sysms o Complx Expoials Ouli Cosidr a LI sysm wih h ui impuls rspos Suppos h ipu sigal is a complx xpoial s x s is a complx umbr, xz zis a complx umbr h or h h w will

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

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

Chapter 3 Linear Equations of Higher Order (Page # 144)

Chapter 3 Linear Equations of Higher Order (Page # 144) Ma Modr Dirial Equaios Lcur wk 4 Jul 4-8 Dr Firozzama Darm o Mahmaics ad Saisics Arizoa Sa Uivrsi This wk s lcur will covr har ad har 4 Scios 4 har Liar Equaios o Highr Ordr Pag # 44 Scio Iroducio: Scod

More information

The Variance-Covariance Matrix

The Variance-Covariance Matrix Th Varanc-Covaranc Marx Our bggs a so-ar has bn ng a lnar uncon o a s o daa by mnmzng h las squars drncs rom h o h daa wh mnsarch. Whn analyzng non-lnar daa you hav o us a program l Malab as many yps o

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

Analysis of System Performance IN2072 Chapter 5 Analysis of Non Markov Systems

Analysis of System Performance IN2072 Chapter 5 Analysis of Non Markov Systems Char for Network Archtectures ad Servces Prof. Carle Departmet of Computer Scece U Müche Aalyss of System Performace IN2072 Chapter 5 Aalyss of No Markov Systems Dr. Alexader Kle Prof. Dr.-Ig. Georg Carle

More information

Major: All Engineering Majors. Authors: Autar Kaw, Luke Snyder

Major: All Engineering Majors. Authors: Autar Kaw, Luke Snyder Nolr Rgrsso Mjor: All Egrg Mjors Auhors: Aur Kw, Luk Sydr hp://urclhodsgusfdu Trsforg Nurcl Mhods Educo for STEM Udrgrdus 3/9/5 hp://urclhodsgusfdu Nolr Rgrsso hp://urclhodsgusfdu Nolr Rgrsso So populr

More information

Outline. Queuing Theory Framework. Delay Models. Fundamentals of Computer Networking: Introduction to Queuing Theory. Delay Models.

Outline. Queuing Theory Framework. Delay Models. Fundamentals of Computer Networking: Introduction to Queuing Theory. Delay Models. Oule Fudaeals of Couer Neworg: Iroduco o ueug Theory eadg: Texboo chaer 3. Guevara Noubr CSG5, lecure 3 Delay Models Lle s Theore The M/M/ queug syse The M/G/ queug syse F3, CSG5 Fudaeals of Couer Neworg

More information

Continous system: differential equations

Continous system: differential equations /6/008 Coious sysm: diffrial quaios Drmiisic modls drivaivs isad of (+)-( r( compar ( + ) R( + r ( (0) ( R ( 0 ) ( Dcid wha hav a ffc o h sysm Drmi whhr h paramrs ar posiiv or gaiv, i.. giv growh or rducio

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

Frequency Response. Response of an LTI System to Eigenfunction

Frequency Response. Response of an LTI System to Eigenfunction Frquncy Rsons Las m w Rvsd formal dfnons of lnary and m-nvaranc Found an gnfuncon for lnar m-nvaran sysms Found h frquncy rsons of a lnar sysm o gnfuncon nu Found h frquncy rsons for cascad, fdbac, dffrnc

More information

Continuous Time Markov Chains

Continuous Time Markov Chains Couous me Markov chas have seay sae probably soluos f a oly f hey are ergoc, us lke scree me Markov chas. Fg he seay sae probably vecor for a couous me Markov cha s o more ffcul ha s he scree me case,

More information

Microscopic Flow Characteristics Time Headway - Distribution

Microscopic Flow Characteristics Time Headway - Distribution CE57: Traffic Flow Thory Spring 20 Wk 2 Modling Hadway Disribuion Microscopic Flow Characrisics Tim Hadway - Disribuion Tim Hadway Dfiniion Tim Hadway vrsus Gap Ahmd Abdl-Rahim Civil Enginring Dparmn,

More information

Fourier Series: main points

Fourier Series: main points BIOEN 3 Lcur 6 Fourir rasforms Novmbr 9, Fourir Sris: mai pois Ifii sum of sis, cosis, or boh + a a cos( + b si( All frqucis ar igr mulipls of a fudamal frqucy, o F.S. ca rprs ay priodic fucio ha w ca

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

ISSN No. (Print) :

ISSN No. (Print) : Iraoal Joural o Emrgg Tchologs (Scal Issu NCETST-07) 8(): 88-94(07) (Publshd by Rsarch Trd, Wbs: www.rsarchrd.) ISSN No. (Pr) : 0975-8364 ISSN No. (Ol) : 49-355 Comarso bw Baysa ad Mamum Lklhood Esmao

More information

) and furthermore all X. The definition of the term stationary requires that the distribution fulfills the condition:

) and furthermore all X. The definition of the term stationary requires that the distribution fulfills the condition: Assigm Thomas Aam, Spha Brumm, Haik Lor May 6 h, 3 8 h smsr, 357, 7544, 757 oblm For R b X a raom variabl havig ormal isribuio wih ma µ a variac σ (his is wri as ~ (,) X. by: R a. Is X ) a urhrmor all

More information

Phys Nov. 3, 2017 Today s Topics. Continue Chapter 2: Electromagnetic Theory, Photons, and Light Reading for Next Time

Phys Nov. 3, 2017 Today s Topics. Continue Chapter 2: Electromagnetic Theory, Photons, and Light Reading for Next Time Phys 31. No. 3, 17 Today s Topcs Cou Chap : lcomagc Thoy, Phoos, ad Lgh Radg fo Nx Tm 1 By Wdsday: Radg hs Wk Fsh Fowls Ch. (.3.11 Polazao Thoy, Jos Macs, Fsl uaos ad Bws s Agl Homwok hs Wk Chap Homwok

More information

Poisson Arrival Process

Poisson Arrival Process Poisso Arrival Procss Arrivals occur i) i a mmylss mar ii) [ o arrival durig Δ ] = λδ + ( Δ ) P o [ o arrival durig Δ ] = λδ + ( Δ ) P o P j arrivals durig Δ = o Δ f j = 2,3, o Δ whr lim =. Δ Δ C C 2 C

More information

Chapter 9 Transient Response

Chapter 9 Transient Response har 9 Transn sons har 9: Ouln N F n F Frs-Ordr Transns Frs-Ordr rcus Frs ordr crcus: rcus conan onl on nducor or on caacor gornd b frs-ordr dffrnal quaons. Zro-nu rsons: h crcu has no ald sourc afr a cran

More information

Introduction to Laplace Transforms October 25, 2017

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

More information

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

CSE 245: Computer Aided Circuit Simulation and Verification

CSE 245: Computer Aided Circuit Simulation and Verification CSE 45: Compur Aidd Circui Simulaion and Vrificaion Fall 4, Sp 8 Lcur : Dynamic Linar Sysm Oulin Tim Domain Analysis Sa Equaions RLC Nwork Analysis by Taylor Expansion Impuls Rspons in im domain Frquncy

More information

Chapter 5 Transient Analysis

Chapter 5 Transient Analysis hpr 5 rs Alyss Jsug Jg ompl rspos rs rspos y-s rspos m os rs orr co orr Dffrl Equo. rs Alyss h ffrc of lyss of crcus wh rgy sorg lms (ucors or cpcors) & m-ryg sgls wh rss crcus s h h quos rsulg from r

More information

Midterm Exam. Tuesday, September hour, 15 minutes

Midterm Exam. Tuesday, September hour, 15 minutes Ecoomcs of Growh, ECON560 Sa Fracsco Sae Uvers Mchael Bar Fall 203 Mderm Exam Tuesda, Sepember 24 hour, 5 mues Name: Isrucos. Ths s closed boo, closed oes exam. 2. No calculaors of a d are allowed. 3.

More information

A Proportional Differentiation Model Based on Service Level

A Proportional Differentiation Model Based on Service Level ppl. ah. If. c. 6 o. pp. 453-46 ppld ahmacs & Ifomao ccs Iaoal Joual @ aual ccs ublshg Co. opooal Dffao odl d o vc Lvl K-o Cho Dpam of Idusal & aagm gg Hau Uvsy of og uds Yog 449-79 Koa Cospodg auho: mal:

More information

Real-Time Systems. Example: scheduling using EDF. Feasibility analysis for EDF. Example: scheduling using EDF

Real-Time Systems. Example: scheduling using EDF. Feasibility analysis for EDF. Example: scheduling using EDF EDA/DIT6 Real-Tme Sysems, Chalmers/GU, 0/0 ecure # Updaed February, 0 Real-Tme Sysems Specfcao Problem: Assume a sysem wh asks accordg o he fgure below The mg properes of he asks are gve he able Ivesgae

More information

Improved Exponential Estimator for Population Variance Using Two Auxiliary Variables

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

More information

An Indian Journal FULL PAPER. Trade Science Inc. A stage-structured model of a single-species with density-dependent and birth pulses ABSTRACT

An Indian Journal FULL PAPER. Trade Science Inc. A stage-structured model of a single-species with density-dependent and birth pulses ABSTRACT [Typ x] [Typ x] [Typ x] ISSN : 974-7435 Volum 1 Issu 24 BioTchnology 214 An Indian Journal FULL PAPE BTAIJ, 1(24), 214 [15197-1521] A sag-srucurd modl of a singl-spcis wih dnsiy-dpndn and birh pulss LI

More information

Physics 160 Lecture 3. R. Johnson April 6, 2015

Physics 160 Lecture 3. R. Johnson April 6, 2015 Physics 6 Lcur 3 R. Johnson April 6, 5 RC Circui (Low-Pass Filr This is h sam RC circui w lookd a arlir h im doma, bu hr w ar rsd h frquncy rspons. So w pu a s wav sad of a sp funcion. whr R C RC Complx

More information

Economics 302 (Sec. 001) Intermediate Macroeconomic Theory and Policy (Spring 2011) 3/28/2012. UW Madison

Economics 302 (Sec. 001) Intermediate Macroeconomic Theory and Policy (Spring 2011) 3/28/2012. UW Madison Economics 302 (Sc. 001) Inrmdia Macroconomic Thory and Policy (Spring 2011) 3/28/2012 Insrucor: Prof. Mnzi Chinn Insrucor: Prof. Mnzi Chinn UW Madison 16 1 Consumpion Th Vry Forsighd dconsumr A vry forsighd

More information

(1) Cov(, ) E[( E( ))( E( ))]

(1) Cov(, ) E[( E( ))( E( ))] Impac of Auocorrelao o OLS Esmaes ECON 3033/Evas Cosder a smple bvarae me-seres model of he form: y 0 x The four key assumpos abou ε hs model are ) E(ε ) = E[ε x ]=0 ) Var(ε ) =Var(ε x ) = ) Cov(ε, ε )

More information

Outline. Computer Networks: Theory, Modeling, and Analysis. Delay Models. Queuing Theory Framework. Delay Models. Little s Theorem

Outline. Computer Networks: Theory, Modeling, and Analysis. Delay Models. Queuing Theory Framework. Delay Models. Little s Theorem Oule Couer Newors: Theory, Modelg, ad Aalyss Guevara Noubr COM35, lecure 3 Delay Models Lle s Theore The M/M/ queug syse The M/G/ queug syse F, COM35 Couer Newors Lecure 3, F, COM35 Couer Newors Lecure

More information

Double Slits in Space and Time

Double Slits in Space and Time Doubl Slis in Sac an Tim Gorg Jons As has bn ror rcnly in h mia, a am l by Grhar Paulus has monsra an inrsing chniqu for ionizing argon aoms by using ulra-shor lasr ulss. Each lasr uls is ffcivly on an

More information

State Observer Design

State Observer Design Sa Obsrvr Dsgn A. Khak Sdgh Conrol Sysms Group Faculy of Elcrcal and Compur Engnrng K. N. Toos Unvrsy of Tchnology Fbruary 2009 1 Problm Formulaon A ky assumpon n gnvalu assgnmn and sablzng sysms usng

More information

Almost Unbiased Exponential Estimator for the Finite Population Mean

Almost Unbiased Exponential Estimator for the Finite Population Mean Rajs Sg, Pakaj aua, rmala Saa Scool of Sascs, DAVV, Idor (M.P., Ida Flor Smaradac Uvrs of Mco, USA Almos Ubasd Epoal Esmaor for F Populao Ma Publsd : Rajs Sg, Pakaj aua, rmala Saa, Flor Smaradac (Edors

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 s device signals an interrupt. time-> time T. A s device. starts device. starts device. A s ISR. WAIT/block. Process A. interrupt.

A s device signals an interrupt. time-> time T. A s device. starts device. starts device. A s ISR. WAIT/block. Process A. interrupt. /1 /1 BAA F 67 :; - -, - % 67 :; = : J 3KJ AA L A s dvic signas an intrrut A s dvic tim T tim-> A s starts dvic starts dvic rocss A AT/bock rocss B AT/bock intrrut B s starts dvic B s dvic B s dvic signas

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

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

First Lecture of Machine Learning. Hung-yi Lee

First Lecture of Machine Learning. Hung-yi Lee Firs Lcur of Machin Larning Hung-yi L Larning o say ys/no Binary Classificaion Larning o say ys/no Sam filring Is an -mail sam or no? Rcommndaion sysms rcommnd h roduc o h cusomr or no? Malwar dcion Is

More information

Some Applications of the Poisson Process

Some Applications of the Poisson Process Applid Maaics, 24, 5, 3-37 Publishd Oli Novbr 24 i SciRs. hp://www.scirp.org/oural/a hp://dx.doi.org/.4236/a.24.59288 So Applicaios of Poisso Procss Kug-Ku s Dpar of Maaics, Ka Uivrsiy, Uio, USA Eail:

More information

Existence of Nonoscillatory Solutions for a Class of N-order Neutral Differential Systems

Existence of Nonoscillatory Solutions for a Class of N-order Neutral Differential Systems Vo 3 No Mod Appd Scc Exsc of Nooscaoy Souos fo a Cass of N-od Nua Dffa Sysms Zhb Ch & Apg Zhag Dpam of Ifomao Egg Hua Uvsy of Tchoogy Hua 4 Cha E-ma: chzhbb@63com Th sach s facd by Hua Povc aua sccs fud

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

Residual Wage Disparity. in Directed Search Equilibrium

Residual Wage Disparity. in Directed Search Equilibrium Rsdual Wag Dsar Drcd Sarch qulbrum Joh Ks, Ia Kg ad Boî Jul* Smbr 7, Absrac W xam how much of h obsrvd wag dsrso amog smlar worrs ca b xlad as a cosquc of a lac of coordao amog mlors. To do hs, w cosruc

More information

Reliability of time dependent stress-strength system for various distributions

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

More information

Random Access Techniques: ALOHA (cont.)

Random Access Techniques: ALOHA (cont.) Random Accss Tchniqus: ALOHA (cont.) 1 Exampl [ Aloha avoiding collision ] A pur ALOHA ntwork transmits a 200-bit fram on a shard channl Of 200 kbps at tim. What is th rquirmnt to mak this fram collision

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

A Simple Representation of the Weighted Non-Central Chi-Square Distribution

A Simple Representation of the Weighted Non-Central Chi-Square Distribution SSN: 9-875 raoa Joura o ovav Rarch Scc grg a Tchoogy (A S 97: 7 Cr rgaao) Vo u 9 Sbr A S Rrao o h Wgh No-Cra Ch-Squar Drbuo Dr ay A hry Dr Sahar A brah Dr Ya Y Aba Proor D o Mahaca Sac u o Saca Su a Rarch

More information

Mellin Transform Method for the Valuation of the American Power Put Option with Non-Dividend and Dividend Yields

Mellin Transform Method for the Valuation of the American Power Put Option with Non-Dividend and Dividend Yields Joural of Mahmacal Fac, 5, 5, 49-7 Publshd Ol Augus 5 ScRs. h://www.scr.org/joural/jmf h://dx.do.org/.436/jmf.5.533 Mll Trasform Mhod for h Valuao of h Amrca Powr Pu Oo wh No-Dvdd ad Dvdd Ylds Suday Emmaul

More information

Poisson process Markov process

Poisson process Markov process E2200 Quuing hory and lraffic 2nd lcur oion proc Markov proc Vikoria Fodor KTH Laboraory for Communicaion nwork, School of Elcrical Enginring 1 Cour oulin Sochaic proc bhind quuing hory L2-L3 oion proc

More information

Washington State University

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

More information

Classification. Linear Classification. What is a Linear Disciminant? Representing Classes. Decision Boundaries. What can be expressed?

Classification. Linear Classification. What is a Linear Disciminant? Representing Classes. Decision Boundaries. What can be expressed? Classfcao Lar Classfcao Ro Parr CPS 7 Survsd larg framork Faurs ca b ayhg args ar dscr classs: Saf mushrooms vs. osoous Malga vs. bg Good crd rsk vs. bad Ca ra classs as umbrs? Sgl class? Mul class? Wh

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

Convergence tests for the cluster DFT calculations

Convergence tests for the cluster DFT calculations Covgc ss o h clus DF clculos. Covgc wh spc o bss s. s clculos o bss s covgc hv b po usg h PBE ucol o 7 os gg h-b. A s o h Guss bss ss wh csg s usss hs b us clug h -G -G** - ++G(p). A l sc o. Å h c bw h

More information