Parameters Estimation in a General Failure Rate Semi-Markov Reliability Model

Size: px
Start display at page:

Download "Parameters Estimation in a General Failure Rate Semi-Markov Reliability Model"

Transcription

1 Joura of Saca Theory ad Appcao Vo. No. (Sepember ) - Parameer Emao a Geera Faure Rae Sem-Marov Reaby Mode M. Fahzadeh ad K. Khorhda Deparme of Sac Facuy of Mahemaca Scece Va-e-Ar Uvery of Rafaja Rafaja Ira Deparme of Sac Shraz Uvery Shraz Ira m.fah@vru.ac.r horhda@uc.ac.r A em-marov proce wh four ae ha bee apped for modeg wo dmar u cod adby yem. A he mome ha operag u fa he adby u wched o operae by ug a wchg devce ha avaabe wh uow probaby. I ao aumed ha he faure rae of u ha he geera form h where are o-egave uow parameer. I favor of em-marov rucure of he yem maxmum ehood ad he Baye emaor of he uow parameer α are obaed whe are o-egave ow coa. Furhermore he emaor are obaed for yem wh mar u. Fay o compare he reu a muao udy doe. Keyword: Bayea emao od adby yem Maxmum ehood Sem-Marov proce. Iroduco Sem-Marov procee owaday have bee apped may area of cece uch a reaby heory. I fac may of he reaby yem ca be modeed by em-marov procee uch a wo dmar u cod adby yem whch ha wdey bee uded ad ued dury. Emao of parameer cuded reaby yem a commo job reaby aay. Recey h area emag he parameer of fe me drbuo have bee receved peca aeo. I 98 Sarmah ad Dharmadhar have obaed he mome emaor of he parameer cuded -ou-of-:g reparabe yem whe he faure ad repar me drbuo of he u are expoea wh uow parameer []. Afer wo decade Sarha ad E-Gohary () have emaed he parameer of h mode by maxmum ehood ad Bayea mehod []. They howed ha hee mehod perform beer ha mome emao mehod. Ao he parameer of he feme -ou-of-m cod adby yem wh mperfec wche have bee emaed ug wo dffere approache by A-Ruzaza ad Sarha []. I E-Gohary uded a peca cae of wo dmar u cod adby yem. He ued he Marov reewa heory o emae he uow parameer of wo mar u cod adby yem wh mperfec wche whe faure rae are eary deped o feme of he yem []. For deaed decrpo of em-marov ad Marov reewa procee ee []. The geera faure rae mode ha codered he pree paper deveop ear faure rae expoea Webu ad Rayegh drbuo mode a we a may oher reaby yem wh reac pecfed fe me drbuo. I h arce a em-marov proce wh four ae ha bee codered for modeg wo dmar u cod adby yem. A he mome ha operag u fa he adby u wched o operae by ug a wchg devce ha avaabe wh uow probaby. I ao aumed ha he faure rae of u ha he geera form h where are o-egave uow parameer. I favor of a em-marov rucure for he yem maxmum ehood ad he Baye emaor of he uow parameer Pubhed by Aa Pre opyrgh: he auhor

2 α have bee obaed whe are o-egave ow coa. By he fac ha he yem wh mar u a peca cae of he uded mode he emaor have ao bee obaed whe he yem ha mar u. Fay o compare he reu a muao udy ha bee doe. oder a em-marov proce wh fe ae pace ao e P p : be a probaby drbuo o : p p. A Marov reewa proce may be defed a foow: Le ξ T T deoe a wo-dmeoa ochac proce wh vaue he ξ T a Marov reewa proce f. P j T T T P j T. P T p. We ao aume ha he probabe (.) do o deped o ad deoe hem by P j T j j a he reewa ere ad j a he reewa ere marx. The aocaed coug j proce repreeg he oa umber of rao wh [ ] deoed by N : where N up : T. A ochac proce X : where X N caed he em-marov proce o geeraed by Marov reewa proce wh a drbuo P ad he ere.. Decrpo of he Mode We w coder a mechaca yem whch perform by he foowg eg:.. Noao ad aumpo The yem co of wo dmar u whch operae cod adby cofgurao a wch ad a repar facy. Whe operag u fa he adby u wched o operae by aco of a wchg devce. The eve ha wchg devce perform we whe requred deoed by A wh probaby P ( A ). The yem fa wheher operag u fa ad he repar job ha o bee fhed ye or boh he operag u ad he wch have bee faed. I h cae he whoe faed yem w be repaced by a ew deca oe. The faure rae of u ha he geera form h where are oegave uow parameer wherea are o-egave ow parameer. The fe me of operag u are o-egave radom varabe V wh drbuo fuco F.. The egh of repar perod of u a o-egave radom varabe wh drbuo fuco G.. Repacg me of he faed yem a o-egave radom varabe wh drbuo fuco M.. A above radom varabe are muuay depede... The em-marov mode I order o decrbe he em-marov reaby mode ad derve he aocaed reewa ere we w roduce he foowg ae:. he yem faed;. he u operag ad u uder repar;. he u operag ad u uder repar;. he u operag ad u adby mode. Le be he ucceve me a of he yem chage (ae rao). Ao e he proce X deoe he ae of he yem a me. Defe Z X wh he ae pace (.) Pubhed by Aa Pre opyrgh: he auhor 6

3 Z T a Marov reewa proce wh ae pace. The aocaed em-marov proce X ad he foowg reewa ere M F G xdf x G xdf x F G x df x G x df x F F. By aumg he geera faure rae h he dey fuco of he fe me are a:. Emao of he Parameer f exp[ ( )]. he I h eco we w oba he maxmum ehood ad Baye emaor of he uow vecor baed o a equece of obervao z from he radom vecor Z T T T a a rajecory of he em-marov proce... Maxmum ehood emao I favor of em-marov rucure of he yem he maxmum ehood fuco become: where ;α A exp[ ( )] G G L z By ag dervave of og L z ;α : j j j. A m G G w.r. uow parameer we have G G G G Pubhed by Aa Pre opyrgh: he auhor 7

4 . α o eay. So umerca echque are requred o cacuae he MLE' of hee parameer. I he foowg peca cae he MLE may be drecy derved from he above yem of equao: Uuay evauag expc expreo for he MLE' of ) G G a repecvey ad ;.e. feme of he u are expoea. I h cae he MLE become ˆ m ˆ m ˆ m where m ad m m are carda umber of he e repecvey. The varace-covarace marx of he maxmum ehood emaor V may be obaed a ) G m m V. m G a repecvey ad ;.e. feme of he u are Webu wh ow hape parameer. I h cae ˆ m ˆ m adˆ m ao V m. m m Sce he yem wh mar u are apped more ha dmar oe we ao chec he above reu for he mar cae. By combg ae ad he ae pace reduce o he foowg form;. The yem faed;. Oe u operag ad aoher uder repar;. Oe u operag ad aoher adby mode. By coderg f exp a he dey fuco for he fe me of u he maxmum ehood equao become Pubhed by Aa Pre opyrgh: he auhor 8

5 where G G G G G are obaed from ovg he yem of equao: ad. The foowg reu G a ad ;.e. feme of he u are expoea. I h cae ˆ m ad ˆ m where m. Ao ) Le m V. m Remar. The reu of E-Gohary [] w be obaed by coderg he above formua. G a ad ;.e. feme of he yem u are Webu wh ow hape parameer. I h cae ˆ m ad ˆ m. Smar o he precedg cae we have ) Le m V. m Remar. The formua whch have bee obaed for h cae exed he reu of E-Gohary [] o he Webu drbuo whch a more geera drbuo... The Baye emao I order o oba he Baye emaor for he vecor of uow parameer aumpo are adoped: A: A: ha pror dey fuco behave a depede radom varabe h. A: The o fuco whe he vecor α emaed by ˆα quadrac. α he foowg By a cacuao proce mar o [] we w arrve a our ma heorem whch gve he. marga poeror pdf of Theorem. The h r mome of he marga poeror pdf of r Φ r r r r r Φ are gve by h r mome of he r (.) where he Kroeer dea ad Pubhed by Aa Pre opyrgh: he auhor 9

6 where ad Θ Θ Φ u u u u u D 8 h d Θ u h m 7 8 m 6 u e d G j j j j j j 6 G j j 6 j j j 6 j j 8 D j 8 7 j 8 j. he doma of. Theorem. Uder aumpo A-A we have: () The Baye emaor for z Φ Φ ˆ E. () The mmum poeror r aocaed o he Baye emaor Φ Φ z Φ Φ var. Proof. By ag r (.) he dered reu foow mmedaey.... Smar u Uder he aumpo A-A we ca oba he foowg reu for he yem wh mar u. where () The Baye emaor for z Φ Φ ˆ E. () The mmum poeror r aocaed o he Baye emaor ˆ Φ Φ var z Φ Φ Pubhed by Aa Pre opyrgh: he auhor

7 u Φ u u u D h d Θ Θ h m u d e... Exampe Dmar cae: Le ha a Bea pror pdf h ao e a he parameer Φ u u u u u become The r r β r ha Gamma drbuo h r r exp Γ r r Γr m 7 8 m r u Φ u u u u u D β r Smar cae: Le Φ u u u become r r u Γ m m r u ha a Bea pror drbuo. Ao e a he parameer Γr m r u. m r u Φ u u u D r u β r Γ r ha Gamma drbuo.. (.) Remar. I (.) e he we w oba exacy he ame reu of E-Gohary [].. A Smuao Sudy I h eco we geerae hree ampe of ze of em-marov adby mode wh mar u o compare he reu. I aumed ha he exac vaue of he uow parameer ued o geerae he ampe are.7. ad. Tabe. Obervao Sampe Obervao Pubhed by Aa Pre opyrgh: he auhor

8 Tabe. Sojour me Sampe T I obag Bayea emaor of uow parameer we aume ha α α ad are radom varabe wh pror drbuo Bea( 6) Gamma( ) ad Gamma( ) repecvey. Tabe. Emao of parameer by dffere mehod Sampe Maxmum ehood mehod Bayea mehod α α α α α α Tabe gve he perceage of reave error for he emaor obaed by each mehod. Tabe. Reave error(%) Sampe Maxmum ehood mehod Bayea mehod α α α α α α Reu of abe ad how ha he Bayea procedure gve beer emae ha maxmum ehood mehod. Referece. A-Ruzaza A. S. ad Sarha A. M. Emaor for parameer cuded cod adby yem wh mperfec wche I. J. Reab. App. 6 () ar E. Iroduco o ochac procee. Prece-Ha Ic 97.. E-Gohary A. Bayea emao of parameer a hree ae reaby em-marov mode App. Mah. compu. () Sarha A. ad E-Gohary A. Parameer emao of -ou-of-: G reparabe yem App. Mah. ompu. () Sarmah. P. ad Dharmadhar. A. D. Emao of parameer of -ou-of :G reparabe yem ommu. Sa. Theor. Meh. () (98) Pubhed by Aa Pre opyrgh: he auhor

Reliability Equivalence of a Parallel System with Non-Identical Components

Reliability Equivalence of a Parallel System with Non-Identical Components Ieraoa Mahemaca Forum 3 8 o. 34 693-7 Reaby Equvaece of a Parae Syem wh No-Ideca ompoe M. Moaer ad mmar M. Sarha Deparme of Sac & O.R. oege of Scece Kg Saud Uvery P.O.ox 455 Ryadh 45 Saud raba aarha@yahoo.com

More information

Efficient Estimators for Population Variance using Auxiliary Information

Efficient Estimators for Population Variance using Auxiliary Information Global Joural of Mahemacal cece: Theor ad Praccal. IN 97-3 Volume 3, Number (), pp. 39-37 Ieraoal Reearch Publcao Houe hp://www.rphoue.com Effce Emaor for Populao Varace ug Aular Iformao ubhah Kumar Yadav

More information

National Conference on Recent Trends in Synthesis and Characterization of Futuristic Material in Science for the Development of Society

National Conference on Recent Trends in Synthesis and Characterization of Futuristic Material in Science for the Development of Society ABSTRACT Naoa Coferece o Rece Tred Syhe ad Characerzao of Fuurc Maera Scece for he Deveome of Socey (NCRDAMDS-208) I aocao wh Ieraoa Joura of Scefc Reearch Scece ad Techoogy Some New Iegra Reao of I- Fuco

More information

ESTIMATION AND TESTING

ESTIMATION AND TESTING CHAPTER ESTIMATION AND TESTING. Iroduco Modfcao o he maxmum lkelhood (ML mehod of emao cera drbuo o overcome erave oluo of ML equao for he parameer were uggeed by may auhor (for example Tku (967; Mehrora

More information

Lecture 3 Topic 2: Distributions, hypothesis testing, and sample size determination

Lecture 3 Topic 2: Distributions, hypothesis testing, and sample size determination Lecure 3 Topc : Drbuo, hypohe eg, ad ample ze deermao The Sude - drbuo Coder a repeaed drawg of ample of ze from a ormal drbuo of mea. For each ample, compue,,, ad aoher ac,, where: The ac he devao of

More information

On Probability Density Function of the Quotient of Generalized Order Statistics from the Weibull Distribution

On Probability Density Function of the Quotient of Generalized Order Statistics from the Weibull Distribution ISSN 684-843 Joua of Sac Voue 5 8 pp. 7-5 O Pobaby Dey Fuco of he Quoe of Geeaed Ode Sac fo he Webu Dbuo Abac The pobaby dey fuco of Muhaad Aee X k Y k Z whee k X ad Y k ae h ad h geeaed ode ac fo Webu

More information

The Signal, Variable System, and Transformation: A Personal Perspective

The Signal, Variable System, and Transformation: A Personal Perspective The Sgal Varable Syem ad Traformao: A Peroal Perpecve Sherv Erfa 35 Eex Hall Faculy of Egeerg Oule Of he Talk Iroduco Mahemacal Repreeao of yem Operaor Calculu Traformao Obervao O Laplace Traform SSB A

More information

Reliability and Sensitivity Analysis of a System with Warm Standbys and a Repairable Service Station

Reliability and Sensitivity Analysis of a System with Warm Standbys and a Repairable Service Station Ieraoa Joura of Operaos Research Vo. 1, No. 1, 61 7 (4) Reaby ad Sesvy Aayss of a Sysem wh Warm Sadbys ad a Reparabe Servce Sao Kuo-Hsug Wag, u-ju a, ad Jyh-B Ke Deparme of Apped Mahemacs, Naoa Chug-Hsg

More information

The MacWilliams Identity of the Linear Codes over the Ring F p +uf p +vf p +uvf p

The MacWilliams Identity of the Linear Codes over the Ring F p +uf p +vf p +uvf p Reearch Joural of Aled Scece Eeer ad Techoloy (6): 28-282 22 ISSN: 2-6 Maxwell Scefc Orazao 22 Submed: March 26 22 Acceed: Arl 22 Publhed: Auu 5 22 The MacWllam Idey of he Lear ode over he R F +uf +vf

More information

CS344: Introduction to Artificial Intelligence

CS344: Introduction to Artificial Intelligence C344: Iroduco o Arfcal Iellgece Puhpa Bhaacharyya CE Dep. IIT Bombay Lecure 3 3 32 33: Forward ad bacward; Baum elch 9 h ad 2 March ad 2 d Aprl 203 Lecure 27 28 29 were o EM; dae 2 h March o 8 h March

More information

Some Probability Inequalities for Quadratic Forms of Negatively Dependent Subgaussian Random Variables

Some Probability Inequalities for Quadratic Forms of Negatively Dependent Subgaussian Random Variables Joural of Sceces Islamc epublc of Ira 6(: 63-67 (005 Uvers of ehra ISSN 06-04 hp://scecesuacr Some Probabl Iequales for Quadrac Forms of Negavel Depede Subgaussa adom Varables M Am A ozorga ad H Zare 3

More information

Bayesian Separation of Non-Stationary Mixtures of Dependent Gaussian Sources

Bayesian Separation of Non-Stationary Mixtures of Dependent Gaussian Sources Bayea earao of No-aoary Mure of Deede Gaua ource Dez Geçağa rca. uruoğu Ayşı rüzü Boğazç Uvery ecrca ad ecroc geerg DearmeBebek3434 Đabu Turkey ITI ogo Nazoae dee cerche va G. Moruzz 564 Pa Iay Abrac.

More information

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

Generalized Entropy of Kumaraswamy Distribution Based on Order Statistics

Generalized Entropy of Kumaraswamy Distribution Based on Order Statistics Geeaed Eop o Kumaawam Dbuo Baed o Ode Sac Ra Na M.A.K Bag 2 Javd Ga Da 3 Reeach Schoa Depame o Sac Uve o Kahm Saga Ida 2 Aocae Poeo Depame o Sac Uve o Kahm Saga Ida 3 Depame o Mahemac Iamc Uve o Scece

More information

Moments of Order Statistics from Nonidentically Distributed Three Parameters Beta typei and Erlang Truncated Exponential Variables

Moments of Order Statistics from Nonidentically Distributed Three Parameters Beta typei and Erlang Truncated Exponential Variables Joural of Mahemacs ad Sascs 6 (4): 442-448, 200 SSN 549-3644 200 Scece Publcaos Momes of Order Sascs from Nodecally Dsrbued Three Parameers Bea ype ad Erlag Trucaed Expoeal Varables A.A. Jamoom ad Z.A.

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

Analysis of a Stochastic Lotka-Volterra Competitive System with Distributed Delays

Analysis of a Stochastic Lotka-Volterra Competitive System with Distributed Delays Ieraoal Coferece o Appled Maheac Sulao ad Modellg (AMSM 6) Aaly of a Sochac Loa-Volerra Copeve Sye wh Drbued Delay Xagu Da ad Xaou L School of Maheacal Scece of Togre Uvery Togre 5543 PR Cha Correpodg

More information

Practice Final Exam (corrected formulas, 12/10 11AM)

Practice Final Exam (corrected formulas, 12/10 11AM) Ecoomc Meze. Ch Fall Socal Scece 78 Uvery of Wco-Mado Pracce Fal Eam (correced formula, / AM) Awer all queo he (hree) bluebook provded. Make cera you wre your ame, your ude I umber, ad your TA ame o all

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

Some Improved Estimators for Population Variance Using Two Auxiliary Variables in Double Sampling

Some Improved Estimators for Population Variance Using Two Auxiliary Variables in Double Sampling Vplav Kumar gh Rajeh gh Deparme of ac Baara Hdu Uver Varaa-00 Ida Flore maradache Uver of ew Meco Gallup UA ome Improved Emaor for Populao Varace Ug Two Aular Varable Double amplg Publhed : Rajeh gh Flore

More information

Competitive Facility Location Problem with Demands Depending on the Facilities

Competitive Facility Location Problem with Demands Depending on the Facilities Aa Pacc Maageme Revew 4) 009) 5-5 Compeve Facl Locao Problem wh Demad Depedg o he Facle Shogo Shode a* Kuag-Yh Yeh b Hao-Chg Ha c a Facul of Bue Admrao Kobe Gau Uver Japa bc Urba Plag Deparme Naoal Cheg

More information

A moment closure method for stochastic reaction networks

A moment closure method for stochastic reaction networks THE JOURNAL OF CHEMICAL PHYSICS 3, 347 29 A mome cloure mehod for ochac reaco ewor Chag Hyeog Lee,,a Kyeog-Hu Km, 2,b ad Plwo Km 3,c Deparme of Mahemacal Scece, Worceer Polyechc Iue, Iue Road, Worceer,

More information

The Mean Residual Lifetime of (n k + 1)-out-of-n Systems in Discrete Setting

The Mean Residual Lifetime of (n k + 1)-out-of-n Systems in Discrete Setting Appled Mahemacs 4 5 466-477 Publshed Ole February 4 (hp//wwwscrporg/oural/am hp//dxdoorg/436/am45346 The Mea Resdual Lfeme of ( + -ou-of- Sysems Dscree Seg Maryam Torab Sahboom Deparme of Sascs Scece ad

More information

International Journal Of Engineering And Computer Science ISSN: Volume 5 Issue 12 Dec. 2016, Page No.

International Journal Of Engineering And Computer Science ISSN: Volume 5 Issue 12 Dec. 2016, Page No. www.jecs. Ieraoal Joural Of Egeerg Ad Compuer Scece ISSN: 19-74 Volume 5 Issue 1 Dec. 16, Page No. 196-1974 Sofware Relably Model whe mulple errors occur a a me cludg a faul correco process K. Harshchadra

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

PARAMETER OPTIMIZATION FOR ACTIVE SHAPE MODELS. Contact:

PARAMETER OPTIMIZATION FOR ACTIVE SHAPE MODELS. Contact: PARAMEER OPIMIZAION FOR ACIVE SHAPE MODELS Chu Che * Mg Zhao Sa Z.L Jaju Bu School of Compuer Scece ad echology, Zhejag Uvery, Hagzhou, Cha Mcroof Reearch Cha, Bejg Sgma Ceer, Bejg, Cha Coac: chec@zju.edu.c

More information

Comparison of the Bayesian and Maximum Likelihood Estimation for Weibull Distribution

Comparison of the Bayesian and Maximum Likelihood Estimation for Weibull Distribution Joural of Mahemacs ad Sascs 6 (2): 1-14, 21 ISSN 1549-3644 21 Scece Publcaos Comarso of he Bayesa ad Maxmum Lkelhood Esmao for Webull Dsrbuo Al Omar Mohammed Ahmed, Hadeel Salm Al-Kuub ad Noor Akma Ibrahm

More information

Standby Redundancy Allocation for a Coherent System under Its Signature Point Process Representation

Standby Redundancy Allocation for a Coherent System under Its Signature Point Process Representation merca Joural of Operao Reearch, 26, 6, 489-5 hp://www.crp.org/joural/ajor ISSN Ole: 26-8849 ISSN Pr: 26-883 Sadby Redudacy llocao for a Cohere Syem uder I Sgaure Po Proce Repreeao Vaderle da Coa ueo Deparme

More information

A Remark on Generalized Free Subgroups. of Generalized HNN Groups

A Remark on Generalized Free Subgroups. of Generalized HNN Groups Ieraoal Mahemacal Forum 5 200 o 503-509 A Remar o Geeralzed Free Subroup o Geeralzed HNN Group R M S Mahmood Al Ho Uvery Abu Dhab POBo 526 UAE raheedmm@yahoocom Abrac A roup ermed eeralzed ree roup a ree

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

APPLICATION OF A Z-TRANSFORMS METHOD FOR INVESTIGATION OF MARKOV G-NETWORKS

APPLICATION OF A Z-TRANSFORMS METHOD FOR INVESTIGATION OF MARKOV G-NETWORKS Joa of Aed Mahema ad Comaoa Meha 4 3( 6-73 APPLCATON OF A Z-TRANSFORMS METHOD FOR NVESTGATON OF MARKOV G-NETWORKS Mha Maay Vo Nameo e of Mahema Ceohowa Uey of Tehoogy Cęohowa Poad Fay of Mahema ad Come

More information

Modeling by Meshless Method LRPIM (local radial point interpolation method)

Modeling by Meshless Method LRPIM (local radial point interpolation method) ème ogrè Fraça de Mécaque Lyo, 4 au 8 Aoû 5 Modelg by Mehle Mehod LRPM (local radal po erpolao mehod) Abrac: A. Mouaou a,. Bouzae b a. Deparme of phyc, Faculy of cece, Moulay mal Uvery B.P. Meke, Morocco,

More information

QR factorization. Let P 1, P 2, P n-1, be matrices such that Pn 1Pn 2... PPA

QR factorization. Let P 1, P 2, P n-1, be matrices such that Pn 1Pn 2... PPA QR facorzao Ay x real marx ca be wre as AQR, where Q s orhogoal ad R s upper ragular. To oba Q ad R, we use he Householder rasformao as follows: Le P, P, P -, be marces such ha P P... PPA ( R s upper ragular.

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

Reliability Analysis. Basic Reliability Measures

Reliability Analysis. Basic Reliability Measures elably /6/ elably Aaly Perae faul Πelably decay Teporary faul ΠOfe Seady ae characerzao Deg faul Πelably growh durg eg & debuggg A pace hule Challeger Lauch, 986 Ocober 6, Bac elably Meaure elably:

More information

New approach for numerical solution of Fredholm integral equations system of the second kind by using an expansion method

New approach for numerical solution of Fredholm integral equations system of the second kind by using an expansion method Ieraoal Reearch Joural o Appled ad Bac Scece Avalable ole a wwwrabcom ISSN 5-88X / Vol : 8- Scece xplorer Publcao New approach or umercal oluo o Fredholm eral equao yem o he ecod d by u a expao mehod Nare

More information

Solution set Stat 471/Spring 06. Homework 2

Solution set Stat 471/Spring 06. Homework 2 oluo se a 47/prg 06 Homework a Whe he upper ragular elemes are suppressed due o smmer b Le Y Y Y Y A weep o he frs colum o oba: A ˆ b chagg he oao eg ad ec YY weep o he secod colum o oba: Aˆ YY weep o

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

θ = θ Π Π Parametric counting process models θ θ θ Log-likelihood: Consider counting processes: Score functions:

θ = θ Π Π Parametric counting process models θ θ θ Log-likelihood: Consider counting processes: Score functions: Paramerc coug process models Cosder coug processes: N,,..., ha cou he occurreces of a eve of eres for dvduals Iesy processes: Lelhood λ ( ;,,..., N { } λ < Log-lelhood: l( log L( Score fucos: U ( l( log

More information

Key words: Fractional difference equation, oscillatory solutions,

Key words: Fractional difference equation, oscillatory solutions, OSCILLATION PROPERTIES OF SOLUTIONS OF FRACTIONAL DIFFERENCE EQUATIONS Musafa BAYRAM * ad Ayd SECER * Deparme of Compuer Egeerg, Isabul Gelsm Uversy Deparme of Mahemacal Egeerg, Yldz Techcal Uversy * Correspodg

More information

ELEC 6041 LECTURE NOTES WEEK 3 Dr. Amir G. Aghdam Concordia University

ELEC 6041 LECTURE NOTES WEEK 3 Dr. Amir G. Aghdam Concordia University ecre Noe Prepared b r G. ghda EE 64 ETUE NTE WEE r. r G. ghda ocorda Uer eceraled orol e - Whe corol heor appled o a e ha co of geographcall eparaed copoe or a e cog of a large ber of p-op ao ofe dered

More information

A note on Turán number Tk ( 1, kn, )

A note on Turán number Tk ( 1, kn, ) A oe o Turá umber T (,, ) L A-Pg Beg 00085, P.R. Cha apl000@sa.com Absrac: Turá umber s oe of prmary opcs he combaorcs of fe ses, hs paper, we wll prese a ew upper boud for Turá umber T (,, ). . Iroduco

More information

Probability Bracket Notation and Probability Modeling. Xing M. Wang Sherman Visual Lab, Sunnyvale, CA 94087, USA. Abstract

Probability Bracket Notation and Probability Modeling. Xing M. Wang Sherman Visual Lab, Sunnyvale, CA 94087, USA. Abstract Probably Bracke Noao ad Probably Modelg Xg M. Wag Sherma Vsual Lab, Suyvale, CA 94087, USA Absrac Ispred by he Drac oao, a ew se of symbols, he Probably Bracke Noao (PBN) s proposed for probably modelg.

More information

Fault Diagnosis in Stationary Rotor Systems through Correlation Analysis and Artificial Neural Network

Fault Diagnosis in Stationary Rotor Systems through Correlation Analysis and Artificial Neural Network Faul Dago Saoary oor Syem hrough Correlao aly ad rfcal Neural Newor leadre Carlo duardo a ad obo Pederva b a Federal Uvery of Ma Gera (UFMG). Deparme of Mechacal geerg (DMC) aceduard@homal.com b Sae Uvery

More information

The conditional density p(x s ) Bayes rule explained. Bayes rule for a classification problem INF

The conditional density p(x s ) Bayes rule explained. Bayes rule for a classification problem INF INF 4300 04 Mulvarae clafcao Ae Solberg ae@fuoo Baed o Chaper -6 Duda ad Har: Paer Clafcao Baye rule for a clafcao proble Suppoe we have J, =,J clae he cla label for a pel, ad he oberved feaure vecor We

More information

Nonsynchronous covariation process and limit theorems

Nonsynchronous covariation process and limit theorems Sochac Procee ad her Applcao 121 (211) 2416 2454 www.elever.com/locae/pa Noychroou covarao proce ad lm heorem Takak Hayah a,, Nakahro Yohda b a Keo Uvery, Graduae School of Bue Admrao, 4-1-1 Hyoh, Yokohama

More information

Optimality of Distributed Control for n n Hyperbolic Systems with an Infinite Number of Variables

Optimality of Distributed Control for n n Hyperbolic Systems with an Infinite Number of Variables Advaces Pure Mahemacs 3 3 598-68 hp://dxdoorg/436/apm33677 Pubshed Oe Sepember 3 (hp://wwwscrporg/joura/apm) Opmay of Dsrbued Coro for Hyperboc Sysems wh a Ife Number of Varabes Aham Hasa amo Deparme of

More information

PART ONE. Solutions to Exercises

PART ONE. Solutions to Exercises PART ONE Soutos to Exercses Chapter Revew of Probabty Soutos to Exercses 1. (a) Probabty dstrbuto fucto for Outcome (umber of heads) 0 1 probabty 0.5 0.50 0.5 Cumuatve probabty dstrbuto fucto for Outcome

More information

Model for Optimal Management of the Spare Parts Stock at an Irregular Distribution of Spare Parts

Model for Optimal Management of the Spare Parts Stock at an Irregular Distribution of Spare Parts Joural of Evromeal cece ad Egeerg A 7 (08) 8-45 do:0.765/6-598/08.06.00 D DAVID UBLIHING Model for Opmal Maageme of he pare ars ock a a Irregular Dsrbuo of pare ars veozar Madzhov Fores Research Isue,

More information

Speech, NLP and the Web

Speech, NLP and the Web peech NL ad he Web uhpak Bhaacharyya CE Dep. IIT Bombay Lecure 38: Uuperved learg HMM CFG; Baum Welch lecure 37 wa o cogve NL by Abh Mhra Baum Welch uhpak Bhaacharyya roblem HMM arg emac ar of peech Taggg

More information

Chapter 3: Maximum-Likelihood & Bayesian Parameter Estimation (part 1)

Chapter 3: Maximum-Likelihood & Bayesian Parameter Estimation (part 1) Aoucemes Reags o E-reserves Proec roosal ue oay Parameer Esmao Bomercs CSE 9-a Lecure 6 CSE9a Fall 6 CSE9a Fall 6 Paer Classfcao Chaer 3: Mamum-Lelhoo & Bayesa Parameer Esmao ar All maerals hese sles were

More information

Fault Tolerant Computing. Fault Tolerant Computing CS 530 Reliability Analysis

Fault Tolerant Computing. Fault Tolerant Computing CS 530 Reliability Analysis Probably /4/6 CS 5 elably Aaly Yahwa K. Malaya Colorado Sae very Ocober 4, 6 elably Aaly: Oule elably eaure: elably, avalably, Tra. elably, T M MTTF ad (, MTBF Bac Cae Sgle u wh perae falure, falure rae

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

Chapter 1 - Free Vibration of Multi-Degree-of-Freedom Systems - I

Chapter 1 - Free Vibration of Multi-Degree-of-Freedom Systems - I CEE49b Chaper - Free Vbrao of M-Degree-of-Freedo Syses - I Free Udaped Vbrao The basc ype of respose of -degree-of-freedo syses s free daped vbrao Aaogos o sge degree of freedo syses he aayss of free vbrao

More information

Policy optimization. Stochastic approach

Policy optimization. Stochastic approach Polcy opmzo Sochc pproch Dcree-me Mrkov Proce Sory Mrkov ch Sochc proce over fe e e S S {.. 2 S} Oe ep ro probbly: Prob j - p j Se ro me: geomerc drbuo Prob j T p j p - 2 Dcree-me Mrkov Proce Sory corollble

More information

Available online at ScienceDirect. Procedia CIRP 63 (2017 ) The 50th CIRP Conference on Manufacturing Systems

Available online at   ScienceDirect. Procedia CIRP 63 (2017 ) The 50th CIRP Conference on Manufacturing Systems Avaabe oe a www.scecedrec.com SceceDrec Proceda CIRP 63 (27 ) 242 247 The 5h CIRP Coferece o aufacur Sysems Reaby easureme for usae aufacur Sysems wh Faure Ieraco Lyu Xu a, *, Yahao Che a, Fore Brad a,

More information

Solution of Impulsive Differential Equations with Boundary Conditions in Terms of Integral Equations

Solution of Impulsive Differential Equations with Boundary Conditions in Terms of Integral Equations Joural of aheacs ad copuer Scece (4 39-38 Soluo of Ipulsve Dffereal Equaos wh Boudary Codos Ters of Iegral Equaos Arcle hsory: Receved Ocober 3 Acceped February 4 Avalable ole July 4 ohse Rabba Depare

More information

EE 6885 Statistical Pattern Recognition

EE 6885 Statistical Pattern Recognition EE 6885 Sascal Paer Recogo Fall 005 Prof. Shh-Fu Chag hp://www.ee.columba.edu/~sfchag Lecure 5 (9//05 4- Readg Model Parameer Esmao ML Esmao, Chap. 3. Mure of Gaussa ad EM Referece Boo, HTF Chap. 8.5 Teboo,

More information

The Histogram. Non-parametric Density Estimation. Non-parametric Approaches

The Histogram. Non-parametric Density Estimation. Non-parametric Approaches The Hogram Chaper 4 No-paramerc Techque Kerel Pare Wdow Dey Emao Neare Neghbor Rule Approach Neare Neghbor Emao Mmum/Mamum Dace Clafcao No-paramerc Approache A poeal problem wh he paramerc approache The

More information

Abstract. 1. Introduction

Abstract. 1. Introduction Joura of Mathematca Sceces: Advaces ad Appcatos Voume 4 umber 2 2 Pages 33-34 COVERGECE OF HE PROJECO YPE SHKAWA ERAO PROCESS WH ERRORS FOR A FE FAMY OF OSEF -ASYMPOCAY QUAS-OEXPASVE MAPPGS HUA QU ad S-SHEG

More information

Ruled surfaces are one of the most important topics of differential geometry. The

Ruled surfaces are one of the most important topics of differential geometry. The CONSTANT ANGLE RULED SURFACES IN EUCLIDEAN SPACES Yuuf YAYLI Ere ZIPLAR Deparme of Mahemaic Faculy of Sciece Uieriy of Aara Tadoğa Aara Turey yayli@cieceaaraedur Deparme of Mahemaic Faculy of Sciece Uieriy

More information

Machine Learning. Hopfield networks. Prof. Dr. Volker Sperschneider

Machine Learning. Hopfield networks. Prof. Dr. Volker Sperschneider Mache Learg Hopfed eor Prof. Dr. Voer Spercheder AG Machee Lere ud Naürchprachche Seme Iu für Iforma Techche Fauä Aber-Ludg-Uverä Freburg percheder@forma.u-freburg.de 30.05.3 Hopfed eor I. Movao II. Bac

More information

(,,, ) (,,, ). In addition, there are three other consumers, -2, -1, and 0. Consumer -2 has the utility function

(,,, ) (,,, ). In addition, there are three other consumers, -2, -1, and 0. Consumer -2 has the utility function MACROECONOMIC THEORY T J KEHOE ECON 87 SPRING 5 PROBLEM SET # Conder an overlappng generaon economy le ha n queon 5 on problem e n whch conumer lve for perod The uly funcon of he conumer born n perod,

More information

Supervised Learning! B." Neural Network Learning! Typical Artificial Neuron! Feedforward Network! Typical Artificial Neuron! Equations!

Supervised Learning! B. Neural Network Learning! Typical Artificial Neuron! Feedforward Network! Typical Artificial Neuron! Equations! Part 4B: Neura Networ earg 10/22/08 Superved earg B. Neura Networ earg Produce dered output for trag put Geeraze reaoaby appropratey to other put Good exampe: patter recogto Feedforward mutayer etwor 10/22/08

More information

Chapter 8. Simple Linear Regression

Chapter 8. Simple Linear Regression Chaper 8. Smple Lear Regresso Regresso aalyss: regresso aalyss s a sascal mehodology o esmae he relaoshp of a respose varable o a se of predcor varable. whe here s jus oe predcor varable, we wll use smple

More information

On Metric Dimension of Two Constructed Families from Antiprism Graph

On Metric Dimension of Two Constructed Families from Antiprism Graph Mah S Le 2, No, -7 203) Mahemaal Sees Leers A Ieraoal Joural @ 203 NSP Naural Sees Publhg Cor O Mer Dmeso of Two Cosrued Famles from Aprm Graph M Al,2, G Al,2 ad M T Rahm 2 Cere for Mahemaal Imagg Tehques

More information

Calibration of factor models with equity data: parade of correlations

Calibration of factor models with equity data: parade of correlations MPRA Much Peroal RePEc Archve Calbrao of facor model wh equy daa: parade of correlao Alexader L. Baraovk WeLB AG 30. Jauary 0 Ole a hp://mpra.ub.u-mueche.de/36300/ MPRA Paper No. 36300, poed 30. Jauary

More information

Calibration Approach Based Estimators of Finite Population Mean in Two - Stage Stratified Random Sampling

Calibration Approach Based Estimators of Finite Population Mean in Two - Stage Stratified Random Sampling I.J.Curr.crobol.App.Sc (08) 7(): 808-85 Ieraoal Joural of Curre crobolog ad Appled Scece ISS: 39-7706 olue 7 uber 0 (08) Joural hoepage: hp://www.jca.co Orgal Reearch Arcle hp://do.org/0.0546/jca.08.70.9

More information

THE ROYAL STATISTICAL SOCIETY 2016 EXAMINATIONS SOLUTIONS HIGHER CERTIFICATE MODULE 5

THE ROYAL STATISTICAL SOCIETY 2016 EXAMINATIONS SOLUTIONS HIGHER CERTIFICATE MODULE 5 THE ROYAL STATISTICAL SOCIETY 06 EAMINATIONS SOLUTIONS HIGHER CERTIFICATE MODULE 5 The Socety s provdg these solutos to assst cadtes preparg for the examatos 07. The solutos are teded as learg ads ad should

More information

TR/05/92 JUNE The 3D Version of the Finite Element Program FESTER. A Technical Report. Ruibn Qu and Martin B. Reed

TR/05/92 JUNE The 3D Version of the Finite Element Program FESTER. A Technical Report. Ruibn Qu and Martin B. Reed TR/5/9 UNE 99 The D Vero of he Fe Eee Progra FESTER A Techca Repor Rub Qu ad Mar B. Reed 69 The D Vero of he Fe Eee Progra FESTER A Techca Repor Rub Qu ad Mar B. Reed Depare of Maheac ad Sac Brue Uver

More information

Linear Regression Linear Regression with Shrinkage

Linear Regression Linear Regression with Shrinkage Lear Regresso Lear Regresso h Shrkage Iroduco Regresso meas predcg a couous (usuall scalar oupu from a vecor of couous pus (feaures x. Example: Predcg vehcle fuel effcec (mpg from 8 arbues: Lear Regresso

More information

A Family of Generalized Stirling Numbers of the First Kind

A Family of Generalized Stirling Numbers of the First Kind Apped Mathematc, 4, 5, 573-585 Pubhed Oe Jue 4 ScRe. http://www.crp.org/oura/am http://d.do.org/.436/am.4.55 A Famy of Geerazed Strg Number of the Frt Kd Beh S. E-Deouy, Nabea A. E-Bedwehy, Abdefattah

More information

Complementary Tree Paired Domination in Graphs

Complementary Tree Paired Domination in Graphs IOSR Joural of Mahemacs (IOSR-JM) e-issn: 2278-5728, p-issn: 239-765X Volume 2, Issue 6 Ver II (Nov - Dec206), PP 26-3 wwwosrjouralsorg Complemeary Tree Pared Domao Graphs A Meeaksh, J Baskar Babujee 2

More information

Fault Tolerant Computing. Fault Tolerant Computing CS 530 Probabilistic methods: overview

Fault Tolerant Computing. Fault Tolerant Computing CS 530 Probabilistic methods: overview Probably 1/19/ CS 53 Probablsc mehods: overvew Yashwa K. Malaya Colorado Sae Uversy 1 Probablsc Mehods: Overvew Cocree umbers presece of uceray Probably Dsjo eves Sascal depedece Radom varables ad dsrbuos

More information

BEST PATTERN OF MULTIPLE LINEAR REGRESSION

BEST PATTERN OF MULTIPLE LINEAR REGRESSION ERI COADA GERMAY GEERAL M.R. SEFAIK AIR FORCE ACADEMY ARMED FORCES ACADEMY ROMAIA SLOVAK REPUBLIC IERAIOAL COFERECE of SCIEIFIC PAPER AFASES Brov 6-8 M BES PAER OF MULIPLE LIEAR REGRESSIO Corel GABER PEROLEUM-GAS

More information

FORCED VIBRATION of MDOF SYSTEMS

FORCED VIBRATION of MDOF SYSTEMS FORCED VIBRAION of DOF SSES he respose of a N DOF sysem s govered by he marx equao of moo: ] u C] u K] u 1 h al codos u u0 ad u u 0. hs marx equao of moo represes a sysem of N smulaeous equaos u ad s me

More information

Continuous Indexed Variable Systems

Continuous Indexed Variable Systems Ieraoal Joural o Compuaoal cece ad Mahemacs. IN 0974-389 Volume 3, Number 4 (20), pp. 40-409 Ieraoal Research Publcao House hp://www.rphouse.com Couous Idexed Varable ysems. Pouhassa ad F. Mohammad ghjeh

More information

IMPROVED PORTFOLIO OPTIMIZATION MODEL WITH TRANSACTION COST AND MINIMAL TRANSACTION LOTS

IMPROVED PORTFOLIO OPTIMIZATION MODEL WITH TRANSACTION COST AND MINIMAL TRANSACTION LOTS Vol.7 No.4 (200) p73-78 Joural of Maageme Scece & Sascal Decso IMPROVED PORTFOLIO OPTIMIZATION MODEL WITH TRANSACTION COST AND MINIMAL TRANSACTION LOTS TIANXIANG YAO AND ZAIWU GONG College of Ecoomcs &

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

ONE APPROACH FOR THE OPTIMIZATION OF ESTIMATES CALCULATING ALGORITHMS A.A. Dokukin

ONE APPROACH FOR THE OPTIMIZATION OF ESTIMATES CALCULATING ALGORITHMS A.A. Dokukin Iero Jor "Iforo Theore & co" Vo 463 ONE PPROH FOR THE OPTIIZTION OF ETITE UTING GORITH Do rc: I h rce he ew roch for ozo of eo ccg gorh ggeed I c e ed for fdg he correc gorh of coexy he coex of gerc roch

More information

Upper Bound For Matrix Operators On Some Sequence Spaces

Upper Bound For Matrix Operators On Some Sequence Spaces Suama Uer Bou formar Oeraors Uer Bou For Mar Oeraors O Some Sequece Saces Suama Dearme of Mahemacs Gaah Maa Uersy Yogyaara 558 INDONESIA Emal: suama@ugmac masomo@yahoocom Isar D alam aer aa susa masalah

More information

STK4011 and STK9011 Autumn 2016

STK4011 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 information

Deterioration-based Maintenance Management Algorithm

Deterioration-based Maintenance Management Algorithm Aca Polyechca Hugarca Vol. 4 No. 2007 Deerorao-baed Maeace Maageme Algorhm Koréla Ambru-Somogy Iue of Meda Techology Budape Tech Doberdó ú 6 H-034 Budape Hugary a_omogy.korela@rkk.bmf.hu Abrac: The Road

More information

BEM COMPARATIVE ANALYSIS OF DYNAMIC RESPONSES OF POROVISCOELASTIC MEDIA USING VISCOELASTIC MODEL

BEM COMPARATIVE ANALYSIS OF DYNAMIC RESPONSES OF POROVISCOELASTIC MEDIA USING VISCOELASTIC MODEL Proceedg o he 7h Ieraoa Coerece o Mechac ad Maera Deg Abuera/Poruga -5 Jue 7. Edor J.. Sva ome ad S.A. Megud. Pub. INEI/EUP 7 PAPER RE: 6957 BEM COMPARATIVE ANALYSIS O DYNAMIC RESPONSES O POROVISCOELASTIC

More information

Multiphase Flow Simulation Based on Unstructured Grid

Multiphase Flow Simulation Based on Unstructured Grid 200 Tuoral School o Flud Dyamcs: Topcs Turbulece Uversy of Marylad, May 24-28, 200 Oule Bacgroud Mulphase Flow Smulao Based o Usrucured Grd Bubble Pacg Mehod mehod Based o he Usrucured Grd Remar B CHEN,

More information

Asymptotic Behavior of Solutions of Nonlinear Delay Differential Equations With Impulse

Asymptotic Behavior of Solutions of Nonlinear Delay Differential Equations With Impulse P a g e Vol Issue7Ver,oveber Global Joural of Scece Froer Research Asypoc Behavor of Soluos of olear Delay Dffereal Equaos Wh Ipulse Zhag xog GJSFR Classfcao - F FOR 3 Absrac Ths paper sudes he asypoc

More information

Econometric Methods. Review of Estimation

Econometric 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 information

Some Identities Relating to Degenerate Bernoulli Polynomials

Some Identities Relating to Degenerate Bernoulli Polynomials Fioma 30:4 2016), 905 912 DOI 10.2298/FIL1604905K Pubishe by Facuy of Scieces a Mahemaics, Uiversiy of Niš, Serbia Avaiabe a: hp://www.pmf.i.ac.rs/fioma Some Ieiies Reaig o Degeerae Beroui Poyomias Taekyu

More information

with finite mean t is a conditional probability of having n, n 0 busy servers in the model at moment t, if at starting time t = 0 the model is empty.

with finite mean t is a conditional probability of having n, n 0 busy servers in the model at moment t, if at starting time t = 0 the model is empty. INFINITE-SERVER M G QUEUEING MODELS WITH CATASTROPHES K Kerobya The fe-erver qeeg model M G, BM G, BM G wh homogeeo ad o-homogeeo arrval of comer ad caarophe are codered The probably geerag fco (PGF) of

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

The algebraic immunity of a class of correlation immune H Boolean functions

The algebraic immunity of a class of correlation immune H Boolean functions Ieraoal Coferece o Advaced Elecroc Scece ad Techology (AEST 06) The algebrac mmuy of a class of correlao mmue H Boolea fucos a Jgla Huag ad Zhuo Wag School of Elecrcal Egeerg Norhwes Uversy for Naoales

More information

An Efficient Dual to Ratio and Product Estimator of Population Variance in Sample Surveys

An Efficient Dual to Ratio and Product Estimator of Population Variance in Sample Surveys "cece as True Here" Joural of Mahemacs ad ascal cece, Volume 06, 78-88 cece gpos Publshg A Effce Dual o Rao ad Produc Esmaor of Populao Varace ample urves ubhash Kumar Yadav Deparme of Mahemacs ad ascs

More information

CHAPTER 7: CLUSTERING

CHAPTER 7: CLUSTERING CHAPTER 7: CLUSTERING Semparamerc Densy Esmaon 3 Paramerc: Assume a snge mode for p ( C ) (Chapers 4 and 5) Semparamerc: p ( C ) s a mure of denses Mupe possbe epanaons/prooypes: Dfferen handwrng syes,

More information

X 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

X 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 information

A Demand System for Input Factors when there are Technological Changes in Production

A Demand System for Input Factors when there are Technological Changes in Production A Demand Syem for Inpu Facor when here are Technologcal Change n Producon Movaon Due o (e.g.) echnologcal change here mgh no be a aonary relaonhp for he co hare of each npu facor. When emang demand yem

More information

COMPARISON OF ESTIMATORS OF PARAMETERS FOR THE RAYLEIGH DISTRIBUTION

COMPARISON OF ESTIMATORS OF PARAMETERS FOR THE RAYLEIGH DISTRIBUTION COMPARISON OF ESTIMATORS OF PARAMETERS FOR THE RAYLEIGH DISTRIBUTION Eldesoky E. Affy. Faculy of Eg. Shbee El kom Meoufa Uv. Key word : Raylegh dsrbuo, leas squares mehod, relave leas squares, leas absolue

More information

4. THE DENSITY MATRIX

4. THE DENSITY MATRIX 4. THE DENSTY MATRX The desy marx or desy operaor s a alerae represeao of he sae of a quaum sysem for whch we have prevously used he wavefuco. Alhough descrbg a quaum sysem wh he desy marx s equvale o

More information

Laplace Transform. Definition of Laplace Transform: f(t) that satisfies The Laplace transform of f(t) is defined as.

Laplace Transform. Definition of Laplace Transform: f(t) that satisfies The Laplace transform of f(t) is defined as. Lplce Trfor The Lplce Trfor oe of he hecl ool for olvg ordry ler dfferel equo. - The hoogeeou equo d he prculr Iegrl re olved oe opero. - The Lplce rfor cover he ODE o lgerc eq. σ j ple do. I he pole o

More information

On the Quasi-Hyperbolic Kac-Moody Algebra QHA7 (2)

On the Quasi-Hyperbolic Kac-Moody Algebra QHA7 (2) Ieaoal Reeach Joual of Egeeg ad Techology (IRJET) e-issn: 9 - Volume: Iue: May- www.e.e -ISSN: 9-7 O he Qua-Hyebolc Kac-Moody lgeba QH7 () Uma Mahewa., Khave. S Deame of Mahemac Quad-E-Mllah Goveme College

More information

Cyclically Interval Total Colorings of Cycles and Middle Graphs of Cycles

Cyclically Interval Total Colorings of Cycles and Middle Graphs of Cycles Ope Joural of Dsree Mahemas 2017 7 200-217 hp://wwwsrporg/joural/ojdm ISSN Ole: 2161-7643 ISSN Pr: 2161-7635 Cylally Ierval Toal Colorgs of Cyles Mddle Graphs of Cyles Yogqag Zhao 1 Shju Su 2 1 Shool of

More information