New recipes for estimating default intensities

Size: px
Start display at page:

Download "New recipes for estimating default intensities"

Transcription

1 SFB 649 Dcuon Pape 9-4 New ecpe o emang deaul nene Alexande Baanov* Caen von Lee* Andé Wlch* * WeLB AG, Düeldo, Gemany SFB E C O N O M I C R I S K B E R L I N h eeach wa uppoed by he Deuche Fochunggemencha hough he SFB 649 "Economc R". hp://b649.ww.hu-beln.de ISSN SFB 649, Humbold-Unveä zu Beln Spandaue Saße, D-78 Beln

2 New ecpe o emang deaul nene Alexande Baanov, Caen von Lee and Andé Wlch all WeLB AG Abac: h pape peen a new appoach o devng deaul nene om CDS o bond pead ha yeld mooh neny cuve equed e.g. o pcng o managemen pupoe. Aumng connuou pemum o coupon paymen, he deaul neny can be obaned by olvng an negal equaon Volea equaon o nd nd. h negal equaon hown o be equvalen o an odnay lnea deenal equaon o nd ode wh me dependen coecen, whch numecally much eae o handle. Fo he pecal cae o Nelon Segel CDS em ucue model, he poblem pem a ully analycal oluon. A vey good and a he ame me mple appoxmaon o h analycal oluon deved, whch eve a a ecpe o eay mplemenaon. Fnally, hown how he new appoach can be employed o emae ochac em ucue model le he CIR model. Keywod: CDS pead, bond pead, deaul neny, ced devave pcng, pead modellng, ced modellng, loan boo valuaon, CIR model JEL clacaon: C3, C and C Dclame: he dea peened below elec he peonal vew o he auho and ae no necealy dencal o he ocal mehodology ued a WeLB AG Acnowledgemen: he paal nancal uppo om he Deuche Fochunggemencha va SFB 649 Öonomche Ro gaeully acnowledged. Inoducon CDS and bond pead cuve a well a he mpled deaul nene deved om hee pead cuve ae ey npu o many applcaon, o nance ced devave pcng o pead and ced model o managemen pupoe. ypcally, he oupu o uch pcng o model que enve o he way uch pead and neny cuve ae emaed om obevable mae quoe. I common pacce o mae cean mplyng aumpon n he emaon pocedue; a pomnen example he aumpon o pecewe conan deaul nene ha ae deved by booappng he CDS quoe obeved o deen maue. Howeve, uch pocedue ae oen no able wh epec o oule e.g. due o daa qualy ue, and geneally poduce cuve conanng dconnue and ump. h pape popoe a able emaon pocedue ha avod he hocomng oulned above and a he ame me numecally eay o mplemen. he mehod ele on a andad model cla commonly ued o he obeved em-ucue o quoe o CDS o bond pead, namely Nelon Segel ype model. he deaul neny epeened a he oluon o an negal equaon a Volea equaon o nd nd ha deved om he andad pcng appoach o CDS o deaulable deb numen by mang he aumpon o connuou pemum o coupon paymen. h negal equaon may be anomed o an odnay lnea deenal equaon o nd ode wh me dependen coecen, he numecal eamen o whch aghowad. he mechanc and he peomance o he new ng pocedue demonaed ung he example o Nelon Segel ype uncon ed o CDS pead obeved on an abaly choen ade day Nelon Segel uncon have he advanage o pemng a cloed om analycal Alexande_Baanov@WeLB.de, Caen_von_Lee@WeLB.de, Ande_Wlch@WeLB.de h model cla may be genealzed o a much boade cla wh ucen degee o eedom o accommodae almo any em-ucue hape eul wll be peened n a epaae pape.

3 oluon o he nd ode deenal equaon menoned above. Apa om h eaue, hee no necey o c o he Nelon Segel uncon o he ng pocedue o wo. In ac, we have developed a vey geneal cla o exponenal-polynomal uncon wh ucen degee o eedom o accommodae almo any em-ucue hape and conanng he Nelon Segel- o Svenonmodel a pecal cae. Deal wll be peened n a epaae acle. Fnally, he emaon o ochac deaul neny model baed on he new pocedue o deaul nene demonaed o he CIR model. Deaul neny a oluon o a Volea equaon o nd nd In he equel, he cae o CDS pead cuve condeed n ode o deve mooh deaul nene; howeve, he dea may be aneed n a aghowad way alo o bond pead. Followng he andad appoach o CDS pcng [Hull 3], he expeced value o deaul leg and pemum leg hould be equal. he expecaon o he peen value o he pemum paymen gven by E n PVP emum D E Iτ > whee denoe he quoed CDS pead o mauy, < < < n ae he pemum paymen dae, -, uually.5 yea, and τ he ochac mng o deaul. In he conex o a deaul neny model baed on a deemnc deaul neny and ho ae, h expeon ge he ollowng om: n n P E PV [ ] emum exp u u du he uncon dened a exp u u du [ ] he uual denon o dcoun aco and uvval pobably have been ued: D exp u du E I > τ > P τ exp u du he aumpon o a deemnc deaul neny and ho ae wll be elaxed below, whee a CIR model aumed o. Aumng connuou pemum paymen, equaon may be appoxmaed by E PVP d emum he peen value o he deaul leg gven by:

4 PV Deaul D τ Iτ Aumng a xed non-ochac lo gven deaul, he expecaon o he peen value o deaul paymen : E PV E D τ I Deaul whch may be ewen a τ E PV d Deaul D exp u du Summng up he pevou omulae, he pcng equaon E PV E PV become 3 d d h an negal equaon o he uncon dened n equaon. d P emúm Deaul Fo he me beng uppoe ha he CDS-pead cuve a well a he ee ho ae ae gven. Deenang he denng equaon o yeld he ollowng expeon: ' 4 Ineng equaon 4 no equaon 3, one oban he cenal eul: 5 d h a Volea equaon o nd nd o he uncon. Equvalen epeenaon a nd ode deenal equaon In pncple, he Volea equaon 5 may be olved numecally n ode o oban he deaul neny. Howeve, numecal pocedue o olvng negal equaon decly ae complex and omeme even unable. heeoe anohe appoach o olvng equaon 3 popoed ha ele on olvng an odnay lnea deenal equaon and heeoe numecally much eae o handle. By deenang equaon 5 once, one ge he ollowng expeon: 6 d Reaangng em lead o

5 7 d Fuhe deenaon o 6 yeld 8 d By neng equaon 7 n equaon 8, and eaangng em, one nally oban he ucue o a deenal equaon o : 9 h g wh g and h '. Equaon 9 a lnea deenal equaon o nd ode wh me dependen coecen and can ealy be olved numecally, ung he ollowng nal condon: ' Compaon o common pace: he booappng appoach eved Aumng he deaul neny o be conan ove he me neval [, ], he negal equaon 3 mple o [ ], Now uppoe ha obevaon o he pead ae gven o deen maue < < m e.g. m 5 and Y, 3Y, 3 5Y, 4 7Y, 5 Y and aume a pecewe conan ucue o o he om ] m I, wh. By neng h epeenaon no equaon 3, one oban he ollowng oluon: J J exp exp whee d D J exp,,,, m. h e o equaon may be olved ecuvely w...,,, m : he equaon o lead o he oluon / whch hen need no he econd equaon o. h n un poduce a nonlnea equaon o whch can be olved e.g. by he Newon mehod. Connung n a mla manne, one oban he deaul nene 3,, m o all he emanng mauy buce.

6 he pecal cae o Nelon Segel: Exac analycal oluon and numecal ecpe In ode o demonae how he emaon pocedue popoed n h pape wo n pacce, and how peom n compaon o he andad booappng appoach, appled ung a paamec cla o cuve popoed by C. Nelon and A. Segel [Nelon 987]. Howeve, he emaon pocedue doe no ely on any pecc model cla and wo wh ohe ype o cuve a well ee oonoe. In a ep, Nelon Segel cuve ae ed o he obeved CDS quoe on an abaly choen ade day 5.3.8, ouce: MaI. Alo, a Nelon Segel model o he ho ae ed o he quoed he EURIBOR em ucue. he eul ae ploed n gue. Eubo and aocaed ho ae CDS pead ae EURIBOR ho ae CDS pead [bp] AA A BBB BB B eno [monh] eno [monh] Fgue : Sho ae and CDS pead cuve om ng a Nelon-Segel model o quoe a o Subung he ed cuve and exp-.67 o he CDS pead and ho ae a well a a xed lo gven deaul.6 and he nal condon no he econd ode deenal equaon 9, a numecal negaon o he ODE lead o a oluon o. By ue o 4 one oban he deaul neny om and ubequenly he pobably o deaul PD accodng o he elaon PD exp u du he eul o and PD ae hown n gue. Deaul neny CDS mpled deaul ae on AA A BBB BB B Pobably o deaul PD em ucue AA A BBB BB B 9.% 6.% 8.% 5.% 7.% 6.% 4.% a e [% ] 5.% 4.% P D [% ] 3.% 3.%.%.%.%.%.% me [monh].% me [monh]

7 Fgue : Deaul neny and cumulave pobably o deaul deved om Nelon-Segel ng a o Compaon o booappng appoach Fo he ae o compaon, alo he andad booappng mehod appled o he daa e decbed above. Applyng ecuon omula o he aveaged quoe a o eeng o Euopean A-aed copoae, one oban he pece-we conan deaul neny epeened n gue 3. he coepondng mooh deaul neny cuve mpled by he popoed ng pocedue alo ploed. One clealy ee ha epecally o maue below 5 yea, he pecewe conan uncon dplay an economcally unnuve behavou lage ump ze and poenally vulneable o anomale n he daa zg-zag behavou. hee dadvanage ae avoded n he Nelon Segel ng appoach. deaul ae yea Fgue 3: Deaul neny o Euopean A-aed copoae a o a o Cloed om oluon o Nelon-Segel ng poblem A uhe advanage o he Nelon-Segel ng pocedue woh menonng: he deenal equaon 9 even pem a cloed om analycal oluon he po ae aumed o be conan. Fo example, he deaul pobably mpled om he Euopean A-aed CDS conac wh Nelon-Segel ng uncon.3.94 exp-γ on he adng day calculae a γ.5ν ν PD e ν e F,.85,.5ν ν whee γ.369, ν exp-γ,.434 and F a,b,z he Kumme conluen hype geomec uncon. he paamee γ goven he exponenal decay ae n a Nelon-Segel o he CDS pead. Clealy, he numecal oluon numecal o equaon 9 baed on he ed connuou po ae cuve de om he analycal oluon analycal baed on a conan aveage po ae. Bu un ou ha he PD-emae deved om numecal and analycal ae numecally dencal. Smple appoxmaon omula o deaul pobable When analyng he behavou o he ng pocedue, un ou ha ome o he paamee ae amazngly able ove me. h obevaon a he ba o an appoxmaon omula o he exac PD em ucue ha decly ee o he CDS pead cuve. he ocal pon ha he ao o CDS pead cuve and PD em ucue µ R,x CDS R,x / PD R,x,x / PD R,x able ove me o a lage ange o ang clae he ubcp R denoe ang clae, whle he agumen and x ee o em ucue and adng dae. Fo he ang clae om AA o BBB, he elaon µ R,x µ hold o he analyed peod om 7..8 unl.4.8. Numecally, one nd he ollowng elaon:

8 PD.39., x e.835e CDS, x, R x, R AA A BBB,.5 [ yea] he analyzed peod conan que an eac behavou o mpled cumulave deaul pobable, a hown n gue 4 o A-aed copoae. R deaul pobabl Fgue 4: Impled em-ucue o cumulave deaul pobable o A-aed copoae wh, 3, 5, 7, and yea. he undelyng obevaon peod cove quoe om 7..8 unl.4.8. h gve condence n he valdy o he appoxmaon omula alo o uue peod. Fgue 5 demonae he peomance o he appoxmaon by compang µ o he nonlnea µ R,x. day m-ao mauy@monhd Fgue 5: Compaon o µ doed cuve o he nonlnea µ R,x connuou cuve. Fng o ochac model o deaul neny: CIR Model he above appoach alo povde a good ang pon o ng ochac model o deaul nene and ho ae. In he equel, he cae o a ochac deaul neny obeyng he CIR Cox Ingeoll Ro model condeed a an example whch could be ealy exended o a moe geneal eng. In he amewo o he CIR model [Cox 985], he ochac deaul neny gven by θ d σ dw d α wh a Wene poce W. he equaon o genealze a ollow: 3 D E exp u du D exp A C CIR

9 wh an ane em ucue exp-a C dened hough by he coecen ee e.g. [McNel 5] o a devaon A C β β α β e αθ ln σ β α / β e β β α e β β α σ h uncon CIR olve he deenal equaon 9. he oluon can be obaned by he numecal mehod decbed above. Gven h oluon o CIR and he dcoun ae D, equaon 3 allow o emae he hee paamee α, σ and θ o he CIR poce o. Sandad nonlnea egeon echnque may be employed o olve he mnmzaon poblem: [ CIR D exp A C ] mn { α, θ, σ } he em n he bace epeen he uvval pobably mpled by he ng pocedue decbed above whle he econd em epeen he uvval pobably mpled by he CIR model dependng on α, σ and θ. Fgue 6 demonae he eul o uch a non-lnea egeon baed on he mae daa obeved on he ollowng value wee obaned o he CIR paamee: α.74, σ and θ uvval pobably yea Fgue 6: Suvval pobable mpled by he Nelon Segel ng pocedue blue cuve and CIR model wh ed paamee α, σ and θ ed cuve o Ouloo In h acle a new pocedue o emang deaul nene baed on obeved CDS pead o bond pead ha been peened. he pocedue ha wo man advanage:. he deaul neny naually become a connuou uncon o and no economcally unnuve dconnue ae.. he pocedue able w... oule and noy daa e.g. due o eoneou CDS-quoe becaue ele on a pecedng moohng pocedue. he new emaon pocedue alo eve a a able ba o ng ochac deaul neny model le.

10 Fuue eeach wll be conduced o analye he eec o moe geneal model clae n he conex o he new emaon pocedue o deaul nene. We expec uch genealzed model clae o povde neeng economc ngh no he me evoluon o CDS o bond pead and he aocaed deaul nene. Accodngly, he acal popee o uch model wll alo be ubec o uhe nvegaon. Reeence [Hull 3]: Hull, J.C.,and Whe, A., 3. he valuaon o Ced Deaul Swap Opon, Jounal o Devave, 3, 4-5 [Cox 985]: Cox, J.C., Ingeoll, J.E., and Ro, S.A., 985. A heoy o he em Sucue o Inee Rae, Economeca, 53, [Nelon 987]: C. Nelon and A. Segel, 987. Pamonou modellng o yeld cuve, J. o Bune 6 987, [McNel 5]: A. McNel, R. Fey and P. Embech. Quanave R Managemen, Pnceon Unvey Pe 5

11 SFB 649 Dcuon Pape See 9 Fo a complee l o Dcuon Pape publhed by he SFB 649, pleae v hp://b649.ww.hu-beln.de. "Impled Mae Pce o Weahe R" by Wolgang Hädle and Benda López Cabea, Januay 9. "On he Syemc Naue o Weahe R" by Guenhe Flle, Man Odenng, Oap Ohn and We Xu, Januay 9. 3 "Localzed Realzed Volaly Modellng" by Yng Chen, Wolgang Kal Hädle and Ua Pgoch, Januay 9. 4 "New ecpe o emang deaul nene" by Alexande Baanov, Caen von Lee and Andé Wlch, Januay 9. SFB 649, Spandaue Saße, D-78 Beln hp://b649.ww.hu-beln.de h eeach wa uppoed by he Deuche Fochunggemencha hough he SFB 649 "Economc R".

Outline. GW approximation. Electrons in solids. The Green Function. Total energy---well solved Single particle excitation---under developing

Outline. GW approximation. Electrons in solids. The Green Function. Total energy---well solved Single particle excitation---under developing Peenaon fo Theoecal Condened Mae Phyc n TU Beln Geen-Funcon and GW appoxmaon Xnzheng L Theoy Depamen FHI May.8h 2005 Elecon n old Oulne Toal enegy---well olved Sngle pacle excaon---unde developng The Geen

More information

Calculus 241, section 12.2 Limits/Continuity & 12.3 Derivatives/Integrals notes by Tim Pilachowski r r r =, with a domain of real ( )

Calculus 241, section 12.2 Limits/Continuity & 12.3 Derivatives/Integrals notes by Tim Pilachowski r r r =, with a domain of real ( ) Clculu 4, econ Lm/Connuy & Devve/Inel noe y Tm Plchow, wh domn o el Wh we hve o : veco-vlued uncon, ( ) ( ) ( ) j ( ) nume nd ne o veco The uncon, nd A w done wh eul uncon ( x) nd connuy e he componen

More information

Maximum Likelihood Estimation

Maximum Likelihood Estimation Mau Lkelhood aon Beln Chen Depaen of Copue Scence & Infoaon ngneeng aonal Tawan oal Unvey Refeence:. he Alpaydn, Inoducon o Machne Leanng, Chape 4, MIT Pe, 4 Saple Sac and Populaon Paaee A Scheac Depcon

More information

An Approach to the Representation of Gradual Uncertainty Resolution in Stochastic Multiperiod Planning

An Approach to the Representation of Gradual Uncertainty Resolution in Stochastic Multiperiod Planning 9 h Euopean mpoum on Compue Aded oce Engneeng ECAE9 J. Jeow and J. hulle (Edo 009 Eleve B.V./Ld. All gh eeved. An Appoach o he epeenaon of Gadual Uncean eoluon n ochac ulpeod lannng Vcene co-amez a gnaco

More information

Numerical Study of Large-area Anti-Resonant Reflecting Optical Waveguide (ARROW) Vertical-Cavity Semiconductor Optical Amplifiers (VCSOAs)

Numerical Study of Large-area Anti-Resonant Reflecting Optical Waveguide (ARROW) Vertical-Cavity Semiconductor Optical Amplifiers (VCSOAs) USOD 005 uecal Sudy of Lage-aea An-Reonan Reflecng Opcal Wavegude (ARROW Vecal-Cavy Seconduco Opcal Aplfe (VCSOA anhu Chen Su Fung Yu School of Eleccal and Eleconc Engneeng Conen Inoducon Vecal Cavy Seconduco

More information

Stochastic Optimal Control of Structural Systems

Stochastic Optimal Control of Structural Systems he Open Aomaon and Conol Syem Jonal, 8,, -9 Sochac Opmal Conol o Scal Syem Open Acce Z.G. Yng Depamen o Mechanc, Zheang nvey, angzho 37, P. R. Chna Abac: he ochac opmal conol an mpoan eeach bec n cal engneeng.

More information

Optimal control of Goursat-Darboux systems in domains with curvilinear boundaries

Optimal control of Goursat-Darboux systems in domains with curvilinear boundaries Opmal conol of Goua-Daboux yem n doman wh cuvlnea boundae S. A. Belba Mahemac Depamen Unvey of Alabama Tucalooa, AL. 35487-0350. USA. e-mal: SBELBAS@G.AS.UA.EDU Abac. We deve neceay condon fo opmaly n

More information

ESS 265 Spring Quarter 2005 Kinetic Simulations

ESS 265 Spring Quarter 2005 Kinetic Simulations SS 65 Spng Quae 5 Knec Sulaon Lecue une 9 5 An aple of an lecoagnec Pacle Code A an eaple of a knec ulaon we wll ue a one denonal elecoagnec ulaon code called KMPO deeloped b Yohhau Oua and Hoh Mauoo.

More information

Multiple Batch Sizing through Batch Size Smoothing

Multiple Batch Sizing through Batch Size Smoothing Jounal of Indual Engneeng (9)-7 Mulple Bach Szng hough Bach Sze Smoohng M Bahadoghol Ayanezhad a, Mehd Kam-Naab a,*, Sudabeh Bakhh a a Depamen of Indual Engneeng, Ian Unvey of Scence and Technology, Tehan,

More information

Copula Effect on Scenario Tree

Copula Effect on Scenario Tree IAENG Inenaonal Jounal of Appled Mahemac 37: IJAM_37 8 Copula Effec on Scenao Tee K. Suene and H. Panevcu Abac Mulage ochac pogam ae effecve fo olvng long-em plannng poblem unde unceany. Such pogam ae

More information

FIRMS IN THE TWO-PERIOD FRAMEWORK (CONTINUED)

FIRMS IN THE TWO-PERIOD FRAMEWORK (CONTINUED) FIRMS IN THE TWO-ERIO FRAMEWORK (CONTINUE) OCTOBER 26, 2 Model Sucue BASICS Tmelne of evens Sa of economc plannng hozon End of economc plannng hozon Noaon : capal used fo poducon n peod (decded upon n

More information

CHAPTER 10: LINEAR DISCRIMINATION

CHAPTER 10: LINEAR DISCRIMINATION HAPER : LINEAR DISRIMINAION Dscmnan-based lassfcaon 3 In classfcaon h K classes ( k ) We defned dsmnan funcon g () = K hen gven an es eample e chose (pedced) s class label as f g () as he mamum among g

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

Memorandum COSOR 97-??, 1997, Eindhoven University of Technology

Memorandum COSOR 97-??, 1997, Eindhoven University of Technology Meoandu COSOR 97-??, 1997, Endhoven Unvey of Technology The pobably geneang funcon of he Feund-Ana-Badley ac M.A. van de Wel 1 Depaen of Maheac and Copung Scence, Endhoven Unvey of Technology, Endhoven,

More information

Flow Decomposition and Large Deviations

Flow Decomposition and Large Deviations ounal of funconal analy 14 2367 (1995) acle no. 97 Flow Decompoon and Lage Devaon Ge ad Ben Aou and Fabenne Caell Laboaoe de Mode laon ochaque e aque Unvee Pa-Sud (Ba^. 425) 91-45 Oay Cedex Fance Receved

More information

Valuation and Risk Assessment of a Portfolio of Variable Annuities: A Vector Autoregression Approach

Valuation and Risk Assessment of a Portfolio of Variable Annuities: A Vector Autoregression Approach Jounal of Mahemacal Fnance, 8, 8, 49-7 hp://www.cp.og/jounal/jmf ISSN Onlne: 6-44 ISSN Pn: 6-44 Valuaon and Rk Aemen of a Pofolo of Vaable Annue: A Veco Auoegeon Appoach Albna Olando, Gay Pake Iuo pe le

More information

ScienceDirect. Behavior of Integral Curves of the Quasilinear Second Order Differential Equations. Alma Omerspahic *

ScienceDirect. Behavior of Integral Curves of the Quasilinear Second Order Differential Equations. Alma Omerspahic * Avalable onlne a wwwscencedeccom ScenceDec oceda Engneeng 69 4 85 86 4h DAAAM Inenaonal Smposum on Inellgen Manufacung and Auomaon Behavo of Inegal Cuves of he uaslnea Second Ode Dffeenal Equaons Alma

More information

Name of the Student:

Name of the Student: Engneeng Mahemacs 05 SUBJEC NAME : Pobably & Random Pocess SUBJEC CODE : MA645 MAERIAL NAME : Fomula Maeal MAERIAL CODE : JM08AM007 REGULAION : R03 UPDAED ON : Febuay 05 (Scan he above QR code fo he dec

More information

I-POLYA PROCESS AND APPLICATIONS Leda D. Minkova

I-POLYA PROCESS AND APPLICATIONS Leda D. Minkova The XIII Inenaonal Confeence Appled Sochasc Models and Daa Analyss (ASMDA-009) Jne 30-Jly 3, 009, Vlns, LITHUANIA ISBN 978-9955-8-463-5 L Sakalaskas, C Skadas and E K Zavadskas (Eds): ASMDA-009 Seleced

More information

Physics 120 Spring 2007 Exam #1 April 20, Name

Physics 120 Spring 2007 Exam #1 April 20, Name Phc 0 Spng 007 E # pl 0, 007 Ne P Mulple Choce / 0 Poble # / 0 Poble # / 0 Poble # / 0 ol / 00 In eepng wh he Unon College polc on cdec hone, ued h ou wll nehe ccep no pode unuhozed nce n he copleon o

More information

Chapter Finite Difference Method for Ordinary Differential Equations

Chapter Finite Difference Method for Ordinary Differential Equations Chape 8.7 Fne Dffeence Mehod fo Odnay Dffeenal Eqaons Afe eadng hs chape, yo shold be able o. Undesand wha he fne dffeence mehod s and how o se o solve poblems. Wha s he fne dffeence mehod? The fne dffeence

More information

( ) ( )) ' j, k. These restrictions in turn imply a corresponding set of sample moment conditions:

( ) ( )) ' j, k. These restrictions in turn imply a corresponding set of sample moment conditions: esng he Random Walk Hypohess If changes n a sees P ae uncoelaed, hen he followng escons hold: va + va ( cov, 0 k 0 whee P P. k hese escons n un mply a coespondng se of sample momen condons: g µ + µ (,,

More information

Support Vector Machines

Support Vector Machines Suppo Veco Machine CSL 3 ARIFICIAL INELLIGENCE SPRING 4 Suppo Veco Machine O, Kenel Machine Diciminan-baed mehod olean cla boundaie Suppo veco coni of eample cloe o bounday Kenel compue imilaiy beeen eample

More information

FX-IR Hybrids Modeling

FX-IR Hybrids Modeling FX-IR Hybr Moeln Yauum Oajma Mubh UFJ Secure Dervave Reearch Dep. Reearch & Developmen Dvon Senor Manaer oajma-yauum@c.mu.jp Oaka Unvery Workhop December 5 h preenaon repreen he vew o he auhor an oe no

More information

Chebyshev Polynomial Solution of Nonlinear Fredholm-Volterra Integro- Differential Equations

Chebyshev Polynomial Solution of Nonlinear Fredholm-Volterra Integro- Differential Equations Çny Ünvee Fen-Edeby Füle Jounl of A nd Scence Sy : 5 y 6 Chebyhev Polynol Soluon of onlne Fedhol-Vole Inego- Dffeenl Equon Hndn ÇERDİK-YASA nd Ayşegül AKYÜZ-DAŞCIOĞU Abc In h ppe Chebyhev collocon ehod

More information

Matrix reconstruction with the local max norm

Matrix reconstruction with the local max norm Marx reconrucon wh he local max norm Rna oygel Deparmen of Sac Sanford Unvery rnafb@anfordedu Nahan Srebro Toyoa Technologcal Inue a Chcago na@cedu Rulan Salakhudnov Dep of Sac and Dep of Compuer Scence

More information

5-1. We apply Newton s second law (specifically, Eq. 5-2). F = ma = ma sin 20.0 = 1.0 kg 2.00 m/s sin 20.0 = 0.684N. ( ) ( )

5-1. We apply Newton s second law (specifically, Eq. 5-2). F = ma = ma sin 20.0 = 1.0 kg 2.00 m/s sin 20.0 = 0.684N. ( ) ( ) 5-1. We apply Newon s second law (specfcally, Eq. 5-). (a) We fnd he componen of he foce s ( ) ( ) F = ma = ma cos 0.0 = 1.00kg.00m/s cos 0.0 = 1.88N. (b) The y componen of he foce s ( ) ( ) F = ma = ma

More information

Cooling of a hot metal forging. , dt dt

Cooling of a hot metal forging. , dt dt Tranen Conducon Uneady Analy - Lumped Thermal Capacy Model Performed when; Hea ranfer whn a yem produced a unform emperaure drbuon n he yem (mall emperaure graden). The emperaure change whn he yem condered

More information

Modern Energy Functional for Nuclei and Nuclear Matter. By: Alberto Hinojosa, Texas A&M University REU Cyclotron 2008 Mentor: Dr.

Modern Energy Functional for Nuclei and Nuclear Matter. By: Alberto Hinojosa, Texas A&M University REU Cyclotron 2008 Mentor: Dr. Moden Enegy Funconal fo Nucle and Nuclea Mae By: lbeo noosa Teas &M Unvesy REU Cycloon 008 Meno: D. Shalom Shlomo Oulne. Inoducon.. The many-body poblem and he aee-fock mehod. 3. Skyme neacon. 4. aee-fock

More information

H = d d q 1 d d q N d d p 1 d d p N exp

H = d d q 1 d d q N d d p 1 d d p N exp 8333: Sacal Mechanc I roblem Se # 7 Soluon Fall 3 Canoncal Enemble Non-harmonc Ga: The Hamlonan for a ga of N non neracng parcle n a d dmenonal box ha he form H A p a The paron funcon gven by ZN T d d

More information

Lecture 11: Stereo and Surface Estimation

Lecture 11: Stereo and Surface Estimation Lecure : Sereo and Surface Emaon When camera poon have been deermned, ung rucure from moon, we would lke o compue a dene urface model of he cene. In h lecure we wll udy he o called Sereo Problem, where

More information

1 Constant Real Rate C 1

1 Constant Real Rate C 1 Consan Real Rae. Real Rae of Inees Suppose you ae equally happy wh uns of he consumpon good oday o 5 uns of he consumpon good n peod s me. C 5 Tha means you ll be pepaed o gve up uns oday n eun fo 5 uns

More information

Monetary policy and models

Monetary policy and models Moneay polcy and odels Kes Næss and Kes Haae Moka Noges Bank Moneay Polcy Unvesy of Copenhagen, 8 May 8 Consue pces and oney supply Annual pecenage gowh. -yea ovng aveage Gowh n oney supply Inflaon - 9

More information

Suppose we have observed values t 1, t 2, t n of a random variable T.

Suppose we have observed values t 1, t 2, t n of a random variable T. Sppose we have obseved vales, 2, of a adom vaable T. The dsbo of T s ow o belog o a cea ype (e.g., expoeal, omal, ec.) b he veco θ ( θ, θ2, θp ) of ow paamees assocaed wh s ow (whee p s he mbe of ow paamees).

More information

Molecular Evolution and Phylogeny. Based on: Durbin et al Chapter 8

Molecular Evolution and Phylogeny. Based on: Durbin et al Chapter 8 Molecula Evoluion and hylogeny Baed on: Dubin e al Chape 8. hylogeneic Tee umpion banch inenal node leaf Topology T : bifucaing Leave - N Inenal node N+ N- Lengh { i } fo each banch hylogeneic ee Topology

More information

Exponential and Logarithmic Equations and Properties of Logarithms. Properties. Properties. log. Exponential. Logarithmic.

Exponential and Logarithmic Equations and Properties of Logarithms. Properties. Properties. log. Exponential. Logarithmic. Eponenial and Logaihmic Equaions and Popeies of Logaihms Popeies Eponenial a a s = a +s a /a s = a -s (a ) s = a s a b = (ab) Logaihmic log s = log + logs log/s = log - logs log s = s log log a b = loga

More information

Chapter 3: Vectors and Two-Dimensional Motion

Chapter 3: Vectors and Two-Dimensional Motion Chape 3: Vecos and Two-Dmensonal Moon Vecos: magnude and decon Negae o a eco: eese s decon Mulplng o ddng a eco b a scala Vecos n he same decon (eaed lke numbes) Geneal Veco Addon: Tangle mehod o addon

More information

s = rθ Chapter 10: Rotation 10.1: What is physics?

s = rθ Chapter 10: Rotation 10.1: What is physics? Chape : oaon Angula poson, velocy, acceleaon Consan angula acceleaon Angula and lnea quanes oaonal knec enegy oaonal nea Toque Newon s nd law o oaon Wok and oaonal knec enegy.: Wha s physcs? In pevous

More information

Labor Supply and Human Capital in a Three-Sector Growth Model

Labor Supply and Human Capital in a Three-Sector Growth Model Labo Supply an Human Capal n a hee-seco Gowh Moel We-Bn hang JEL coe: 5 Abac h pape nouce enogenou me buon beween wo an leue no a hee-eco gowh heoy he economy con o capal goo eco conumpon goo eco an unvey

More information

CptS 570 Machine Learning School of EECS Washington State University. CptS Machine Learning 1

CptS 570 Machine Learning School of EECS Washington State University. CptS Machine Learning 1 ps 57 Machne Leann School of EES Washnon Sae Unves ps 57 - Machne Leann Assume nsances of classes ae lneal sepaable Esmae paamees of lnea dscmnan If ( - -) > hen + Else - ps 57 - Machne Leann lassfcaon

More information

Nanoparticles. Educts. Nucleus formation. Nucleus. Growth. Primary particle. Agglomeration Deagglomeration. Agglomerate

Nanoparticles. Educts. Nucleus formation. Nucleus. Growth. Primary particle. Agglomeration Deagglomeration. Agglomerate ucs Nucleus Nucleus omaon cal supesauaon Mng o eucs, empeaue, ec. Pmay pacle Gowh Inegaon o uson-lme pacle gowh Nanopacles Agglomeaon eagglomeaon Agglomeae Sablsaon o he nanopacles agans agglomeaon! anspo

More information

Physics 15 Second Hour Exam

Physics 15 Second Hour Exam hc 5 Second Hou e nwe e Mulle hoce / ole / ole /6 ole / ------------------------------- ol / I ee eone ole lee how ll wo n ode o ecee l ced. I ou oluon e llegle no ced wll e gen.. onde he collon o wo 7.

More information

V.Abramov - FURTHER ANALYSIS OF CONFIDENCE INTERVALS FOR LARGE CLIENT/SERVER COMPUTER NETWORKS

V.Abramov - FURTHER ANALYSIS OF CONFIDENCE INTERVALS FOR LARGE CLIENT/SERVER COMPUTER NETWORKS R&RATA # Vol.) 8, March FURTHER AALYSIS OF COFIDECE ITERVALS FOR LARGE CLIET/SERVER COMPUTER ETWORKS Vyacheslav Abramov School of Mahemacal Scences, Monash Unversy, Buldng 8, Level 4, Clayon Campus, Wellngon

More information

p E p E d ( ) , we have: [ ] [ ] [ ] Using the law of iterated expectations, we have:

p E p E d ( ) , we have: [ ] [ ] [ ] Using the law of iterated expectations, we have: Poblem Se #3 Soluons Couse 4.454 Maco IV TA: Todd Gomley, gomley@m.edu sbued: Novembe 23, 2004 Ths poblem se does no need o be uned n Queson #: Sock Pces, vdends and Bubbles Assume you ae n an economy

More information

Volatility Interpolation

Volatility Interpolation Volaly Inerpolaon Prelmnary Verson March 00 Jesper Andreasen and Bran Huge Danse Mares, Copenhagen wan.daddy@danseban.com brno@danseban.com Elecronc copy avalable a: hp://ssrn.com/absrac=69497 Inro Local

More information

Handling Fuzzy Constraints in Flow Shop Problem

Handling Fuzzy Constraints in Flow Shop Problem Handlng Fuzzy Consans n Flow Shop Poblem Xueyan Song and Sanja Peovc School of Compue Scence & IT, Unvesy of Nongham, UK E-mal: {s sp}@cs.no.ac.uk Absac In hs pape, we pesen an appoach o deal wh fuzzy

More information

Lecture-V Stochastic Processes and the Basic Term-Structure Equation 1 Stochastic Processes Any variable whose value changes over time in an uncertain

Lecture-V Stochastic Processes and the Basic Term-Structure Equation 1 Stochastic Processes Any variable whose value changes over time in an uncertain Lecue-V Sochasic Pocesses and he Basic Tem-Sucue Equaion 1 Sochasic Pocesses Any vaiable whose value changes ove ime in an unceain way is called a Sochasic Pocess. Sochasic Pocesses can be classied as

More information

X-Ray Notes, Part III

X-Ray Notes, Part III oll 6 X-y oe 3: Pe X-Ry oe, P III oe Deeo Coe oupu o x-y ye h look lke h: We efe ue of que lhly ffee efo h ue y ovk: Co: C ΔS S Sl o oe Ro: SR S Co o oe Ro: CR ΔS C SR Pevouly, we ee he SR fo ye hv pxel

More information

Journal of Engineering Science and Technology Review 7 (1) (2014) Research Article

Journal of Engineering Science and Technology Review 7 (1) (2014) Research Article Jes Jounal o Engneeng Scence and echnology Revew 7 5 5 Reseach Acle JOURNAL OF Engneeng Scence and echnology Revew www.jes.og Sudy on Pedcve Conol o ajecoy ackng o Roboc Manpulao Yang Zhao Dep. o Eleconc

More information

Recursive segmentation procedure based on the Akaike information criterion test

Recursive segmentation procedure based on the Akaike information criterion test ecuve egmenaon pocedue baed on he Aae nfomaon ceon e A-Ho SAO Depamen of Appled Mahemac and Phyc Gaduae School of Infomac Kyoo Unvey a@.yoo-u.ac.jp JAPAN Oulne Bacgound and Movaon Segmenaon pocedue baed

More information

Then the number of elements of S of weight n is exactly the number of compositions of n into k parts.

Then the number of elements of S of weight n is exactly the number of compositions of n into k parts. Geneating Function In a geneal combinatoial poblem, we have a univee S of object, and we want to count the numbe of object with a cetain popety. Fo example, if S i the et of all gaph, we might want to

More information

Physics 2A Chapter 11 - Universal Gravitation Fall 2017

Physics 2A Chapter 11 - Universal Gravitation Fall 2017 Physcs A Chapte - Unvesal Gavtaton Fall 07 hese notes ae ve pages. A quck summay: he text boxes n the notes contan the esults that wll compse the toolbox o Chapte. hee ae thee sectons: the law o gavtaton,

More information

arxiv: v1 [math.pr] 4 Jul 2014

arxiv: v1 [math.pr] 4 Jul 2014 Mean-feld ochac dffeenal equaon and aocaed PDE Rane Buckdahn 1,3, Juan L 2, Shge Peng 3, Cahene Rane 1 1 Laoaoe de Mahémaque LMBA, CNRS-UMR 6205, Unveé de Beagne Occdenale, 6, avenue Vco-le-Gogeu, CS 93837,

More information

The Unique Solution of Stochastic Differential Equations. Dietrich Ryter. Midartweg 3 CH-4500 Solothurn Switzerland

The Unique Solution of Stochastic Differential Equations. Dietrich Ryter. Midartweg 3 CH-4500 Solothurn Switzerland The Unque Soluon of Sochasc Dffeenal Equaons Dech Rye RyeDM@gawne.ch Mdaweg 3 CH-4500 Solohun Swzeland Phone +4132 621 13 07 Tme evesal n sysems whou an exenal df sngles ou he an-iô negal. Key wods: Sochasc

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

Summary of Experimental Uncertainty Assessment Methodology With Example

Summary of Experimental Uncertainty Assessment Methodology With Example Summa of Epemenal ncean Aemen Mehodolog Wh Eample F. Sen, M. Mue, M-L. M enna,, and W.E. Echnge 5//00 1 Table of Conen A hlooph Temnolog ncean opagaon Equaon A fo Sngle Te A fo Mulple Te Eample Recommendaon

More information

, the. L and the L. x x. max. i n. It is easy to show that these two norms satisfy the following relation: x x n x = (17.3) max

, the. L and the L. x x. max. i n. It is easy to show that these two norms satisfy the following relation: x x n x = (17.3) max ecure 8 7. Sabiliy Analyi For an n dimenional vecor R n, he and he vecor norm are defined a: = T = i n i (7.) I i eay o how ha hee wo norm aify he following relaion: n (7.) If a vecor i ime-dependen, hen

More information

MCTDH Approach to Strong Field Dynamics

MCTDH Approach to Strong Field Dynamics MCTDH ppoach o Song Feld Dynamcs Suen Sukasyan Thomas Babec and Msha Ivanov Unvesy o Oawa Canada Impeal College ondon UK KITP Sana Babaa. May 8 009 Movaon Song eld dynamcs Role o elecon coelaon Tunnel

More information

The Backpropagation Algorithm

The Backpropagation Algorithm The Backpopagaton Algothm Achtectue of Feedfowad Netwok Sgmodal Thehold Functon Contuctng an Obectve Functon Tanng a one-laye netwok by teepet decent Tanng a two-laye netwok by teepet decent Copyght Robet

More information

General Non-Arbitrage Model. I. Partial Differential Equation for Pricing A. Traded Underlying Security

General Non-Arbitrage Model. I. Partial Differential Equation for Pricing A. Traded Underlying Security 1 Geneal Non-Abiage Model I. Paial Diffeenial Equaion fo Picing A. aded Undelying Secuiy 1. Dynamics of he Asse Given by: a. ds = µ (S, )d + σ (S, )dz b. he asse can be eihe a sock, o a cuency, an index,

More information

DEEP UNFOLDING FOR MULTICHANNEL SOURCE SEPARATION SUPPLEMENTARY MATERIAL

DEEP UNFOLDING FOR MULTICHANNEL SOURCE SEPARATION SUPPLEMENTARY MATERIAL DEEP UNFOLDING FOR MULTICHANNEL SOURCE SEPARATION SUPPLEMENTARY MATERIAL Sco Wsdom, John Hershey 2, Jonahan Le Roux 2, and Shnj Waanabe 2 Deparmen o Elecrcal Engneerng, Unversy o Washngon, Seale, WA, USA

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

-HYBRID LAPLACE TRANSFORM AND APPLICATIONS TO MULTIDIMENSIONAL HYBRID SYSTEMS. PART II: DETERMINING THE ORIGINAL

-HYBRID LAPLACE TRANSFORM AND APPLICATIONS TO MULTIDIMENSIONAL HYBRID SYSTEMS. PART II: DETERMINING THE ORIGINAL UPB Sc B See A Vo 72 I 3 2 ISSN 223-727 MUTIPE -HYBRID APACE TRANSORM AND APPICATIONS TO MUTIDIMENSIONA HYBRID SYSTEMS PART II: DETERMININ THE ORIINA Ve PREPEIŢĂ Te VASIACHE 2 Ace co copeeă oă - pce he

More information

TRINOMIAL TREE OPTION PRICING VIA THRESHOLD-GARCH MODEL

TRINOMIAL TREE OPTION PRICING VIA THRESHOLD-GARCH MODEL IJRRAS 7 () Ma wwwapapesscom/volumes/vol7issue/ijrras_7 5pd TRINOMIAL TREE OPTION PRICING VIA THRESHOLD-GARCH MODEL Su-Ing Lu Depamen o Fnance S Hsn Unves # Mu-Ca Road Sec Tape 64 Tawan ROC ABSTRACT In

More information

( ) ( ) Weibull Distribution: k ti. u u. Suppose t 1, t 2, t n are times to failure of a group of n mechanisms. The likelihood function is

( ) ( ) Weibull Distribution: k ti. u u. Suppose t 1, t 2, t n are times to failure of a group of n mechanisms. The likelihood function is Webll Dsbo: Des Bce Dep of Mechacal & Idsal Egeeg The Uvesy of Iowa pdf: f () exp Sppose, 2, ae mes o fale of a gop of mechasms. The lelhood fco s L ( ;, ) exp exp MLE: Webll 3//2002 page MLE: Webll 3//2002

More information

A hybrid method to find cumulative distribution function of completion time of GERT networks

A hybrid method to find cumulative distribution function of completion time of GERT networks Jounal of Indusal Engneeng Inenaonal Sepembe 2005, Vol., No., - 9 Islamc Azad Uvesy, Tehan Souh Banch A hybd mehod o fnd cumulave dsbuon funcon of compleon me of GERT newos S. S. Hashemn * Depamen of Indusal

More information

Degree of Approximation of a Class of Function by (C, 1) (E, q) Means of Fourier Series

Degree of Approximation of a Class of Function by (C, 1) (E, q) Means of Fourier Series IAENG Inenaional Jounal of Applied Mahemaic, 4:, IJAM_4 7 Degee of Appoximaion of a Cla of Funcion by C, E, q Mean of Fouie Seie Hae Kihna Nigam and Kuum Shama Abac In hi pape, fo he fi ime, we inoduce

More information

An axisymmetric incompressible lattice BGK model for simulation of the pulsatile ow in a circular pipe

An axisymmetric incompressible lattice BGK model for simulation of the pulsatile ow in a circular pipe INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS In. J. Nume. Meh. Fluds 005; 49:99 116 Publshed onlne 3 June 005 n Wley IneScence www.nescence.wley.com). DOI: 10.100/d.997 An axsymmec ncompessble

More information

Ordinary Differential Equations in Neuroscience with Matlab examples. Aim 1- Gain understanding of how to set up and solve ODE s

Ordinary Differential Equations in Neuroscience with Matlab examples. Aim 1- Gain understanding of how to set up and solve ODE s Ordnary Dfferenal Equaons n Neuroscence wh Malab eamples. Am - Gan undersandng of how o se up and solve ODE s Am Undersand how o se up an solve a smple eample of he Hebb rule n D Our goal a end of class

More information

Fall 2004/05 Solutions to Assignment 5: The Stationary Phase Method Provided by Mustafa Sabri Kilic. I(x) = e ixt e it5 /5 dt (1) Z J(λ) =

Fall 2004/05 Solutions to Assignment 5: The Stationary Phase Method Provided by Mustafa Sabri Kilic. I(x) = e ixt e it5 /5 dt (1) Z J(λ) = 8.35 Fall 24/5 Solution to Aignment 5: The Stationay Phae Method Povided by Mutafa Sabi Kilic. Find the leading tem fo each of the integal below fo λ >>. (a) R eiλt3 dt (b) R e iλt2 dt (c) R eiλ co t dt

More information

Transcription: Messenger RNA, mrna, is produced and transported to Ribosomes

Transcription: Messenger RNA, mrna, is produced and transported to Ribosomes Quanave Cenral Dogma I Reference hp//book.bonumbers.org Inaon ranscrpon RNA polymerase and ranscrpon Facor (F) s bnds o promoer regon of DNA ranscrpon Meenger RNA, mrna, s produced and ranspored o Rbosomes

More information

Set of square-integrable function 2 L : function space F

Set of square-integrable function 2 L : function space F Set of squae-ntegable functon L : functon space F Motvaton: In ou pevous dscussons we have seen that fo fee patcles wave equatons (Helmholt o Schödnge) can be expessed n tems of egenvalue equatons. H E,

More information

EECE 301 Signals & Systems Prof. Mark Fowler

EECE 301 Signals & Systems Prof. Mark Fowler EECE 31 Signal & Syem Prof. Mark Fowler Noe Se #27 C-T Syem: Laplace Tranform Power Tool for yem analyi Reading Aignmen: Secion 6.1 6.3 of Kamen and Heck 1/18 Coure Flow Diagram The arrow here how concepual

More information

Sections 3.1 and 3.4 Exponential Functions (Growth and Decay)

Sections 3.1 and 3.4 Exponential Functions (Growth and Decay) Secions 3.1 and 3.4 Eponenial Funcions (Gowh and Decay) Chape 3. Secions 1 and 4 Page 1 of 5 Wha Would You Rahe Have... $1million, o double you money evey day fo 31 days saing wih 1cen? Day Cens Day Cens

More information

Risky Swaps. Munich Personal RePEc Archive. Gikhman, Ilya Independent Research. 08. February 2008

Risky Swaps. Munich Personal RePEc Archive. Gikhman, Ilya Independent Research. 08. February 2008 MPR Munch Peronal RePEc rchve Ry Swap Ghman Ilya Independen Reearch 8. February 28 Onlne a hp://mpra.ub.un-muenchen.de/779/ MPR Paper o. 779 poed 9. February 28 / 4:45 Ry Swap. Ilya Ghman 677 Ivy Wood

More information

Solving the Dirac Equation: Using Fourier Transform

Solving the Dirac Equation: Using Fourier Transform McNa Schola Reeach Jounal Volume Atcle Solvng the ac quaton: Ung oue Tanfom Vncent P. Bell mby-rddle Aeonautcal Unvety, Vncent.Bell@my.eau.edu ollow th and addtonal wok at: http://common.eau.edu/na Recommended

More information

Consider a Binary antipodal system which produces data of δ (t)

Consider a Binary antipodal system which produces data of δ (t) Modulaion Polem PSK: (inay Phae-hi keying) Conide a inay anipodal yem whih podue daa o δ ( o + δ ( o inay and epeively. Thi daa i paed o pule haping ile and he oupu o he pule haping ile i muliplied y o(

More information

Relative controllability of nonlinear systems with delays in control

Relative controllability of nonlinear systems with delays in control Relave conrollably o nonlnear sysems wh delays n conrol Jerzy Klamka Insue o Conrol Engneerng, Slesan Techncal Unversy, 44- Glwce, Poland. phone/ax : 48 32 37227, {jklamka}@a.polsl.glwce.pl Keywor: Conrollably.

More information

Field due to a collection of N discrete point charges: r is in the direction from

Field due to a collection of N discrete point charges: r is in the direction from Physcs 46 Fomula Shee Exam Coulomb s Law qq Felec = k ˆ (Fo example, f F s he elecc foce ha q exes on q, hen ˆ s a un veco n he decon fom q o q.) Elecc Feld elaed o he elecc foce by: Felec = qe (elecc

More information

dm dt = 1 V The number of moles in any volume is M = CV, where C = concentration in M/L V = liters. dcv v

dm dt = 1 V The number of moles in any volume is M = CV, where C = concentration in M/L V = liters. dcv v Mg: Pcess Aalyss: Reac ae s defed as whee eac ae elcy lue M les ( ccea) e. dm he ube f les ay lue s M, whee ccea M/L les. he he eac ae beces f a hgeeus eac, ( ) d Usually s csa aqueus eeal pcesses eac,

More information

The Non-Truncated Bulk Arrival Queue M x /M/1 with Reneging, Balking, State-Dependent and an Additional Server for Longer Queues

The Non-Truncated Bulk Arrival Queue M x /M/1 with Reneging, Balking, State-Dependent and an Additional Server for Longer Queues Alied Maheaical Sciece Vol. 8 o. 5 747-75 The No-Tucaed Bul Aival Queue M x /M/ wih Reei Bali Sae-Deede ad a Addiioal Seve fo Loe Queue A. A. EL Shebiy aculy of Sciece Meofia Uiveiy Ey elhebiy@yahoo.co

More information

( ) α is determined to be a solution of the one-dimensional minimization problem: = 2. min = 2

( ) α is determined to be a solution of the one-dimensional minimization problem: = 2. min = 2 Homewo (Patal Solton) Posted on Mach, 999 MEAM 5 Deental Eqaton Methods n Mechancs. Sole the ollowng mat eqaton A b by () Steepest Descent Method and/o Pecondtoned SD Method Snce the coecent mat A s symmetc,

More information

On The Estimation of Two Missing Values in Randomized Complete Block Designs

On The Estimation of Two Missing Values in Randomized Complete Block Designs Mahemaical Theoy and Modeling ISSN 45804 (Pape ISSN 505 (Online Vol.6, No.7, 06 www.iise.og On The Esimaion of Two Missing Values in Randomized Complee Bloc Designs EFFANGA, EFFANGA OKON AND BASSE, E.

More information

STUDY OF THE STRESS-STRENGTH RELIABILITY AMONG THE PARAMETERS OF GENERALIZED INVERSE WEIBULL DISTRIBUTION

STUDY OF THE STRESS-STRENGTH RELIABILITY AMONG THE PARAMETERS OF GENERALIZED INVERSE WEIBULL DISTRIBUTION Inenaional Jounal of Science, Technology & Managemen Volume No 04, Special Issue No. 0, Mach 205 ISSN (online): 2394-537 STUDY OF THE STRESS-STRENGTH RELIABILITY AMONG THE PARAMETERS OF GENERALIZED INVERSE

More information

International Journal of Mathematical Archive-5(6), 2014, Available online through ISSN

International Journal of Mathematical Archive-5(6), 2014, Available online through   ISSN Inenaional Jonal o Mahemaical Achive-6, 0, 09-8 Availale online hogh www.ijma.ino ISSN 9 06 EXISENCE OF NONOSCILLAORY SOLUIONS OF A CLASS OF NONLINEAR NEURAL DELAY DIFFERENIAL EQUAIONS OF HIRD ORDER K

More information

NON-HOMOGENEOUS SEMI-MARKOV REWARD PROCESS FOR THE MANAGEMENT OF HEALTH INSURANCE MODELS.

NON-HOMOGENEOUS SEMI-MARKOV REWARD PROCESS FOR THE MANAGEMENT OF HEALTH INSURANCE MODELS. NON-HOOGENEOU EI-AKO EWA POCE FO THE ANAGEENT OF HEATH INUANCE OE. Jacque Janen CEIAF ld Paul Janon 84 e 9 6 Charlero EGIU Fax: 32735877 E-mal: ceaf@elgacom.ne and amondo anca Unverà a apenza parmeno d

More information

A. Inventory model. Why are we interested in it? What do we really study in such cases.

A. Inventory model. Why are we interested in it? What do we really study in such cases. Some general yem model.. Inenory model. Why are we nereed n? Wha do we really udy n uch cae. General raegy of machng wo dmlar procee, ay, machng a fa proce wh a low one. We need an nenory or a buffer or

More information

c- : r - C ' ',. A a \ V

c- : r - C ' ',. A a \ V HS PAGE DECLASSFED AW EO 2958 c C \ V A A a HS PAGE DECLASSFED AW EO 2958 HS PAGE DECLASSFED AW EO 2958 = N! [! D!! * J!! [ c 9 c 6 j C v C! ( «! Y y Y ^ L! J ( ) J! J ~ n + ~ L a Y C + J " J 7 = [ " S!

More information

Chapter 11. Supplemental Text Material. The method of steepest ascent can be derived as follows. Suppose that we have fit a firstorder

Chapter 11. Supplemental Text Material. The method of steepest ascent can be derived as follows. Suppose that we have fit a firstorder S-. The Method of Steepet cent Chapter. Supplemental Text Materal The method of teepet acent can be derved a follow. Suppoe that we have ft a frtorder model y = β + β x and we wh to ue th model to determne

More information

Adaptive Voice Smoothing with Optimal Playback Delay Based on the ITU-T E-Model

Adaptive Voice Smoothing with Optimal Playback Delay Based on the ITU-T E-Model Adapve Voce Smoohng wh Opmal Playback Delay Baed on he ITU-T E-Model Shyh-Fang Huang, Ec Hao-Kuang Wu 2, and Pao-Ch Chang 3 Depamen of Eleconc Engneeng, Naonal Cenal Unvey, Tawan, hf@vaplab.ee.ncu.edu.w

More information

5.2 GRAPHICAL VELOCITY ANALYSIS Polygon Method

5.2 GRAPHICAL VELOCITY ANALYSIS Polygon Method ME 352 GRHICL VELCITY NLYSIS 52 GRHICL VELCITY NLYSIS olygon Mehod Velociy analyi form he hear of kinemaic and dynamic of mechanical yem Velociy analyi i uually performed following a poiion analyi; ie,

More information

Second Order Fuzzy S-Hausdorff Spaces

Second Order Fuzzy S-Hausdorff Spaces Inten J Fuzzy Mathematical Achive Vol 1, 013, 41-48 ISSN: 30-34 (P), 30-350 (online) Publihed on 9 Febuay 013 wwweeachmathciog Intenational Jounal o Second Ode Fuzzy S-Haudo Space AKalaichelvi Depatment

More information

Optimized Braking Force Distribution during a Braking-in- Turn Maneuver for Articulated Vehicles

Optimized Braking Force Distribution during a Braking-in- Turn Maneuver for Articulated Vehicles 56 Opmzed Bakng Foce Dsbuon dung a Bakng-n- Tun Maneuve o Aculaed Vehcles E. Esmalzadeh, A. Goodaz and M. Behmad 3 Downloaded om www.us.ac. a 3:04 IRST on Fday Novembe 3d 08,* Faculy o Engneeng and Appled

More information

Online Appendix for. Strategic safety stocks in supply chains with evolving forecasts

Online Appendix for. Strategic safety stocks in supply chains with evolving forecasts Onlne Appendx for Sraegc safey socs n supply chans wh evolvng forecass Tor Schoenmeyr Sephen C. Graves Opsolar, Inc. 332 Hunwood Avenue Hayward, CA 94544 A. P. Sloan School of Managemen Massachuses Insue

More information

L N O Q. l q l q. I. A General Case. l q RANDOM LAGRANGE MULTIPLIERS AND TRANSVERSALITY. Econ. 511b Spring 1998 C. Sims

L N O Q. l q l q. I. A General Case. l q RANDOM LAGRANGE MULTIPLIERS AND TRANSVERSALITY. Econ. 511b Spring 1998 C. Sims Econ. 511b Sprng 1998 C. Sm RAD AGRAGE UPERS AD RASVERSAY agrange mulpler mehod are andard fare n elemenary calculu coure, and hey play a cenral role n economc applcaon of calculu becaue hey ofen urn ou

More information

) from i = 0, instead of i = 1, we have =

) from i = 0, instead of i = 1, we have = Chape 3: Adjusmen Coss n he abou Make I Movaonal Quesons and Execses: Execse 3 (p 6): Illusae he devaon of equaon (35) of he exbook Soluon: The neempoal magnal poduc of labou s epesened by (3) = = E λ

More information

HEAT FLUX ESTIMATION IN THIN-LAYER DRYING. Olivier Fudym Christine Carrère-Gée Didier Lecomte Bruno Ladevie

HEAT FLUX ESTIMATION IN THIN-LAYER DRYING. Olivier Fudym Christine Carrère-Gée Didier Lecomte Bruno Ladevie Invese Poblems n Engneeng : Theoy and Pacce d In Coneence on Invese Poblems n Engneeng June -8, 999, Po Ludlow, WA, UA HT5 HEAT FLUX ETIMATION IN THIN-LAYER DRYING Olve Fudym Chsne Caèe-Gée Dde Lecome

More information

Robust Centralized Fusion Kalman Filters with Uncertain Noise Variances

Robust Centralized Fusion Kalman Filters with Uncertain Noise Variances ELKOMNIKA Indonean Jounal of Eleal Engneeng Vol., No.6, June 04, pp. 4705 ~ 476 DOI: 0.59/elkomnka.v6.5490 4705 Robu Cenalzed Fuon Kalman Fle wh Unean Noe Vaane Wen-juan Q, Peng Zhang, Z-l Deng* Depamen

More information

Public debt competition and policy coordination

Public debt competition and policy coordination 03/04/5 Pulc de compeon and polcy coodnaon Aa Yaa Nagoya Cy Unvey Aac Th pape analye he conequence o de polce n a wo-peod/wo-couny model. Whehe o no pulc de compeon eul n le ecen eouce allocaon eween pvae

More information

6.302 Feedback Systems Recitation : Phase-locked Loops Prof. Joel L. Dawson

6.302 Feedback Systems Recitation : Phase-locked Loops Prof. Joel L. Dawson 6.32 Feedback Syem Phae-locked loop are a foundaional building block for analog circui deign, paricularly for communicaion circui. They provide a good example yem for hi cla becaue hey are an excellen

More information