SOME ASPECTS OF ARBITRATING
|
|
- Mavis Reed
- 5 years ago
- Views:
Transcription
1 Зборник на трудови од IV конгрес на математичарите на Македонија Струга, Македонија, 9-8 Стр Proceedgs of IV cogress of mahemacas of Republc of Macedoa ruga, Macedoa, 9-8 Pages OME APEC OF ARIRAIG ajaa Aaasova-Pacemsa, Lmoa Lazarova, ljaa Zlaaovsa Absrac Oe of he fudameal coceps uderlyg he heory of facal dervave prcg ad hedgg s ha of he arbrage hs cocep cera crcumsaces, allows us o defe he precse relaoshps amog prces ad hece her esablshme he mahemacal erpreao of hs cocep shows ha s ecessary o have owledge of moder heory of probably ad sochasc aalyss I hs paper we wll show ha here s a possbly of geg o rs prof o facal mare where he prces have radom characer Key words: prcg, facal mare,margal, arbrag, Iroduco Face s oe of he fases developg areas he moder bag ad corporae world hs, ogeher wh he sophscao of moder facal producs, provdes a rapdly growg mpeus for ew mahemacal models ad moder mahemacal mehods Amog he reasos o ge eresed facal mahemacs, he followg s oe: who has ever wodered, loog a he facal pages of a ewspaper, dsplayg he errac evoluos of quoaos o he oc Exchage, f hese were o govered by some models, lely o be probablsc hs queso was a he hear of he sudes coduced by Lous acheler, parcularly hs famous hess (9 ad he aswered he above queso erms of rowa Moo Laer, amuelso correced acheler ( 965 by replacg he rowa Moo by s expoeal ad he famous lac-choles formula bega ( 973 o play a esseal role he compuao of he opo prces Harrso ad Kreps (97 remared he exsece of a margale measure for he dscoued prce process mples he absece of he arbrage ce he 98s, we have wessed he exploso of probablsc models, alog wh facal producs, each ur becomg more ad more complex However, couous me case he absece of he arbrage s o loger a suffce codo for he exsece of a equvale margale measure A o-freeluch codo slghly sroger ha o-arbrage codo, was roduced by Kreps (98, who showed ha he exsece of a equvale margale measure f he dscoued prce process s bouded 374
2 I dscree-me case, he coverse saeme has bee proved by Dalag-Moro-Wllger (99 hs resul s referred o as he fudameal heorem of asse prcg Delbae ad chachermayer (994 wored ou a geeral verso of he fudameal heorem of asse prcg I hs paper we wll cosder a few versos of he proof of fudameal heorem of asse prcg We suppose ha he facal mare socs, fucos codos o suspese ad flucuao For s mahemacal descrpo s duced space of probably ( Ω, F, ( F, P, where: Ω s а space of elemeary eves for whch we suppose ha s fe; F s algebra of Ω subses (of all possble subses from Ω ; ( F s algebra flrao; P s probably rae (measure or probably he algebra flrao ( F we ca show le sream of formao avalable o all mare parcpas as of he me mome We cosder mare (, whch coss of d + asses followg sese: - s accou a ba ( o- rs (rs free asses d (,,, sor of asses ( rs asses he prce vecor a mare whch s cosdered has he mode d,,, acually (, mode, ha meas ha has he mode: ( d d (,,, (,,, he ba accou dyamcs ca be descrbe wh sochasc sequece ( whch has characer : ( : ( s F measurable, ha meas { ω Ω : ( ω x} { x} F al mome o he mare also occupes a specal place For we have a basc assumpo ha s sasfed F { O/, Ω} whch s rval algebra For dffere ypes of shares assume ha her dyamcs ca be,,, d descrbed also wh a posve sochasc sequeces ( ( ha have feaure ( : s { Ω : ( ω x} { x} F F measurable e ω hs shows ha here are dfferece bewee ba accou ad asses F measurable for sgfes ha ba accou prce s ow a he mome ha meas s predced O he oher sde F measurable for sgfes ha he values become ow afer all formao cludg he mome hs explas 375
3 ad why he ba accou ame rs-free asses, ad why he aco s called rsy asses We wll cosder he followg facors r Δ Δ, p ad for whch F we ca mmedaely coclude ha r are F measurable ad p are measurable From he facors r ad p we have ha Δ r, Δ p ( ad from here we ge oe represeao for he facal mare, whch s ow le represeao smple perce: ( + r ( + p (, ow, we wll precse he codos mare whch s observed Frs assumpo s he assumpo abou he deal suao o he mare he sese ha he operag expeses assocaed wh he rasfer of fuds from oe asse o aoher are cosdered eglgble Eve go o he exreme o assume ha hey do o exs Aoher mpora assumpo regardg he acos ad her properes amely we assume ha he aco besoeco produc he sese ha s possble o buy or sell ay shares how a small amou of socs Defo: he sochasc predcoed sеquece ( β, γ d ( γ ( ω,, γ ( ω where γ ad ( : γ are F measurable, β ( β ( ω ad β are also F measurable for ( s called vesme porfolo,, mare of asses o ( d oce ha varables β ( β ( ω ad γ (, ( ω γ ω (, γ may be posve, ull or egave whch meas ha vesor ca borrow from a ba accou or o sell socs O he oher had, he assumpo for F measurably meas ha he sze β ( ω ad ( ω γ whch descrbes he poso vesor me ( amou of moey ha has he sregh of he ba accou ad amou of shares you ow are deermed by formao avalable a he mome ad o (he ex poso s fully deermed oday Porfolo vesmes are also called vesme sraegy o be sressed hs dyamcs Defo: he value of vesme porfolo o (, mare of asses s he sochasc sequece ( where β + γ 376
4 Furher o, we wll use he shorer symbol γ ( γ, γ herefore we do o have o dffereae bewee cases where d ad d > o we have: β From hese wo defos we coclude ha Δ β Δ Δ + Δβ + Δγ where β Δ Δ s he chage of he ba accou ad he chage of he asses ad Δβ + Δγ s he chage of he asses porfolo srucure self aurally, ca ow be cocluded ha he real chages capal porfolo cosss oly of real value Δ ad Δ ad o he chage of he sze Δ β ad Δ γ Defo: Real (effecve prof from he possesso of he cosdered asses G for whch he porfolo, s descrbed wh sochasc sequece ( followg ca be appled G, G βδ Δ G ad hs meas ha cosss of he aure of he ba accou ad he aure of he asses chage he real capal a he mome s + G Defo: Porfolo of asses s called self- facg, F f s capal ca be preseed he followg mode + β Δ Δ, ( hs equao s equvale o Δβ + Δγ ( Obvous meag of he prevous codos s o chage he ba accou oly happeg because of chages he srucure of a pacage of socs ha eeps he aco ad vce versa I s clear ha he aco wh he porfolo we have oo may asses ad would be good o smplfy he srucure or o reduce hs large umber of asses For hs purpose, loo he relaoshp of hese wo cosdered sochasc processes ad e her quoe hs rao we ca see as he proporo of shares he ba accou, of course uder he assumpo ha we have for bass sze he hs ba accou Ad always s > he we ca observe hs same mare ad he sasfed ha ( 377
5 oher way amely, ow hs s see he mare fully descrbed slghly dffere model (, where: (, where,where ( approprae capal ( porfolo ( β, γ s equal o: β β β If he porfolo s self-face o he mare (, would be selfface ad o he mare (, because s sasfed he followg: Δβ + Δγ Δ β + Δγ Whe, Δ for self-face porfolo : + γ Δ o, ge o he self-face porfolo, sadardzed capal mees he equaly hs equao plays a ey role may of calculao ha s based o he cocep of mare o-arbrao he prevous aalyss of porfolo capal,, o he mare (, s good f you oe a few assumpos he frs s ha hs mare o flue ad sally moey or oher fuds ad ha here s o rasfer of expeses or hey ca be cosdered eglgble Hedgg Prce Compleeess I ecoomc sese, hedgg s he reduco of he sesvy of a porfolo o he moveme of a uderlyg asse by ag oppose posos dffere facal srumes Defo: Porfolo asses ( β, γ s called superor - ( x, f hedge, acually feror - ( x, f hedge f he followg codos are fulflled: x, x a he begg; f ( ω, f ( ω, for all ω a fe mome 378
6 β β γ γ x, f ( ω, ω where (, ( f he hedge s called perfec Hedge s a secury ool, whch eables guaraee come ad realzed capal parcular surace goal o he mare Hedge s vesme ha s uderae wh he am o reduce he rs, rese aoher vesme For fxed x >, we roduce: * H x f ; P : x, f ( ω ω class of all superor hedges (, { } ( ( x f ; P { : x, f ( ω } ( ω H class of all feror hedges *, Defo: f s crculag bod he value: * * C ( f ; P f { x : H ( x, f ; P O/ } s called superor prce (demaded prce of hedge crculag bod C* ( f ; P f{ x : H *( x, f ; P O/ } s called feror prce (offered prce of hedge crculag bod If we sell a corac wh he fal payme f ( ω hey would le o sell for maxmum prce A he same me we have o h ha f someoe bough for he prce ha we offer o we ca o wh oe had, pu he prce lower ha ha for he full corac erms ad ca o pu so grea ha we have o-rs yeld ( free-luch for he buyer geerally wll o o see * x [ C *,C ] ow, buyers ad sellers have a prce rs s compesao for hm pecal aeo deserves he case whe he upper ad lower prce whe he mach s me: C C Defo: (, secures mare s called -complee f each performg facal oblgao he sese ha here s perfec hedge ha me: f ω, ( ω 3 Arbrao ( I facal erms, here are ever ay opporues for mag a saaeous rs-free prof Prcsely, such opporues cao exs for a sgfca legh of me before prces move ad hus elmae hem he facal applcao of hs prcple leads o some elega modelg he ey words he defo of arbrage are saaeous ad rs-free prof y vesg eques somebody ca probably bea he ba, bu hs cao be cera If oe was a greaer reur he oe mus accep greaer rs 379
7 Defo: he self-facg sraegy realzes arbrage possbly (a he mome f: I order for he self-facg sraegy o acualze arbrage possbly ( he mome, he followg should be me:, ω he al mome ( ( ω, for > early cera (sure ha meas P ( > > We wll deoe wh F arb class of all arbral self-facg sraeges If F arb ad he: P ( P( > > I hs paper we wll gve some proofs of dffere aspecs, o he fudameal heorem of asse prcg heorem: (Dalag-Moro-Wlger We suppose ha he (, mare o flrag probably space ( Ω, F, ( F s esablshed of ba accou (, > ad d fe umber of asses (,,, ( We ALO suppose ha hs facal mare fucos he followg perod momes,,, < ad ha F O/, Ω, F F he hs facal mare (, s whou arbrage f ad oly f here s (a leas oe probably measure P ( margale measure equvale o he measure P, such ha relao o, he sequece of lowered prces s oe margale ha s: E P <,,, d;,,, E F,,, P Proof: Frs, we ca assume ha For self-facg porfolo we used he formula Δ γ Δ Icludg hs we have: Δ γ Δ From he prevous equaos we ca coclude ha he capal porfolo may be represeed as: 38
8 Δ + G, G γ Δ o show oly predcao s ow eough o show ha apply: F :, P G (, G ad ( ( γ Δ, ( ( ω, G, ( ( ω As sequeces ( are margales relao o he measure o have o apply: E G F E γ Δ F γ Δ E Δ F G + ecause : F measurable, for ad γ : F measurable, for ( ( ( ( For margale G we have: E( G E( G E( G Ad uque o-egave values wh zero mahemacal expecao s smlar o zero e G he equvalece of measures P ad P comes from he relaos: P P ( G P( G ( G > P( > G Ω be a probably space equpped wh a fe dscree- be a adaped d - Le (, F, P me flrao ( F,,,, F F ad le ( dmesoal processes Le R { : ξ H, H P} ξ where P s a se of all predcable d -dmesoal processes (e H s F -measurable ad H H Δ Δ, Pu _ A R L+ ; A s he closure of A probably, L + s he se of o-egave radom varables heorem: he followg codos are equvale: ( A L + {}; ( A L + {} ad A A (3 L {} A + (4 here s a probably P P wh d P L dp such ha s a P -margale 38
9 descrbes he evoluo of prces of rsy asses, ad H s he ermal value of a self-facg porfolo Codo ( s erpreed as he absece of arbrage; ca be wre he obvously equvale form R L + {} (or H H If Ω s fe he A s closed beg a polyhedral coe a fe-dmesoal space For fe Ω he se A may be o closed, whle R s always closed d L η R be such ha η lm f η < ha here are Lemma: Le ( d ( R η L such ha for all ω he sequece of η ( ω subsequece of he sequece of ( ω Lemma: Le L + {} s a coverge η K L be a closed covex coe L such ha + K he here s a probably P P E ξ for all ξ K wh d P L dp such ha Proof of a heorem: ( ( o show ha A s closed we proceed by duco Le uppose ha H Δ r ς as where measurable ad H H s F - r L + I s suffce o fd F -measurable radom varables whch are coverge as r L + such ha H Δ r ς as coverge Le Ω F form a fe paro of Ω Obvously, we may argue o each Ω separaely as o a auoomous measure space (cosderg he resrcos of radom varables ad races of σ -algebras Le H lmf H O he se Ω { H < } we ca ae, usg Lemma, F -measurable H such ha H ( ω s a coverge subsequece of ( ω for everyω ; r are defed correspodgly hus, f Ω s of full measure, he goal s acheved H O Ω { H } we pu G ad observe ha G Δ h as H y lemma we fd F -measurable G such ha ( ω H G s a coverge 38
10 subsequece of ( ω h ω G for every ω Deog he lm by G, we oba ha G Δ where h s o-egave, hece, vrue of (, G Δ G, here exss a paro of Ω o d dsjo subses Ω F, As ( such ha G o Ω Defe H H β G where β o G Ω he H Δ H Δ o Ω We repea he ere procedure o each Ω wh he sequece H owg ha H for all Apparely, afer a fe umber of seps we cosruc he desred sequece Le he clam be rue for ad le H Δ r ς as where are F -measurable ad r L + y he same argumes based o he elmao of o-zero compoes of he sequece H ad usg he duco hypohess we replace H ad r by H ad r such ha H coverges ( (3 rval (3 (4 oce ha for ay radom varable η here s a equvale probably P wh bouded desy such ha η L ( P Propery (3 s vara uder equvale chage of probably hs cosderao allows us o assume ha are egrable he covex se A A L s closed L ce A L {}, lemma esures he exsece of P P wh bouded + desy ad such ha E ξ for all ξ A, parcular, for ξ ±H Δ where H s bouded ad (4 ( Le A L+ ξ H F measurable hus, (, H E Δ F ξ As ( H F oba by codog ha E hus ξ H H E Δ, we 383
11 Refereces: [] Dalag R C, Moro A, Wllger W Equvale margale measure ad o-arbrage sochasc secures mare model ochascs ad ochasc Repors, (99 [] Dalbea F he Dalag-Moro-Wllger heorem [3] Par uloj, V I Perbarg, Elemes of Face Mahemacs, December 999 [4] Lecures Isue for Pure ad Appled Mahemacs, UCLA, Alber hrvaev, Esseals of he Arbrage heory, [5] R J Wllams, Iroduco o he Mahemacs of Face [6] Yur Kabaov, Chrsophe rcer, Laboraore de MAhemaques, A heachers oe o o-arbrage crera [7] Jelea Mlec, eorja Arbraza, 5 Uversy Goce Delcev p, Faculy of formacs, Republc of Macedoa e-mal: ajaapacemsa@ugdedum 384
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 informationQuantitative Portfolio Theory & Performance Analysis
550.447 Quaave Porfolo heory & Performace Aalyss Week February 4 203 Coceps. Assgme For February 4 (hs Week) ead: A&L Chaper Iroduco & Chaper (PF Maageme Evrome) Chaper 2 ( Coceps) Seco (Basc eur Calculaos)
More information14. 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(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 informationContinuous 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 informationThe 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 informationKey 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 informationThe textbook expresses the stock price as the present discounted value of the dividend paid and the price of the stock next period.
ublc Affars 974 Meze D. Ch Fall Socal Sceces 748 Uversy of Wscos-Madso Sock rces, News ad he Effce Markes Hypohess (rev d //) The rese Value Model Approach o Asse rcg The exbook expresses he sock prce
More informationThe 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 informationAsymptotic 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 informationSynopsis of Various Rates of Return
Syopss of Varous Raes of Reur (Noe: Much of hs s ake from Cuhberso) I he world of face here are may dffere ypes of asses. Whe aalysg hese, a ecoomc sese, we aemp o characerse hem by reducg hem o some of
More informationContinuous 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 informationSome 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 informationThe textbook expresses the stock price as the present discounted value of the dividend paid and the price of the stock next period.
coomcs 435 Meze. Ch Fall 07 Socal Sceces 748 Uversy of Wscos-Madso Sock rces, News ad he ffce Markes Hypohess The rese Value Model Approach o Asse rcg The exbook expresses he sock prce as he prese dscoued
More informationReal-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 informationCyclone. Anti-cyclone
Adveco Cycloe A-cycloe Lorez (963) Low dmesoal aracors. Uclear f hey are a good aalogy o he rue clmae sysem, bu hey have some appealg characerscs. Dscusso Is he al codo balaced? Is here a al adjusme
More informationProbability 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 informationQR 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 informationLeast 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 informationIMPROVED 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 information4. 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 informationLeast squares and motion. Nuno Vasconcelos ECE Department, UCSD
Leas squares ad moo uo Vascocelos ECE Deparme UCSD Pla for oda oda we wll dscuss moo esmao hs s eresg wo was moo s ver useful as a cue for recogo segmeao compresso ec. s a grea eample of leas squares problem
More informationQuantum Mechanics II Lecture 11 Time-dependent perturbation theory. Time-dependent perturbation theory (degenerate or non-degenerate starting state)
Pro. O. B. Wrgh, Auum Quaum Mechacs II Lecure Tme-depede perurbao heory Tme-depede perurbao heory (degeerae or o-degeerae sarg sae) Cosder a sgle parcle whch, s uperurbed codo wh Hamloa H, ca exs a superposo
More informationFor the plane motion of a rigid body, an additional equation is needed to specify the state of rotation of the body.
The kecs of rgd bodes reas he relaoshps bewee he exeral forces acg o a body ad he correspodg raslaoal ad roaoal moos of he body. he kecs of he parcle, we foud ha wo force equaos of moo were requred o defe
More informationFORCED 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 informationChapter 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 informationBrownian Motion and Stochastic Calculus. Brownian Motion and Stochastic Calculus
Browa Moo Sochasc Calculus Xogzh Che Uversy of Hawa a Maoa earme of Mahemacs Seember, 8 Absrac Ths oe s abou oob decomoso he bascs of Suare egrable margales Coes oob-meyer ecomoso Suare Iegrable Margales
More informationThe 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 informationThe Linear Regression Of Weighted Segments
The Lear Regresso Of Weghed Segmes George Dael Maeescu Absrac. We proposed a regresso model where he depede varable s made o up of pos bu segmes. Ths suao correspods o he markes hroughou he da are observed
More information8. 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 informationComplementary 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 informationFundamentals of Speech Recognition Suggested Project The Hidden Markov Model
. Projec Iroduco Fudameals of Speech Recogo Suggesed Projec The Hdde Markov Model For hs projec, s proposed ha you desg ad mpleme a hdde Markov model (HMM) ha opmally maches he behavor of a se of rag sequeces
More informationMoments 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 informationOptimal Eye Movement Strategies in Visual Search (Supplement)
Opmal Eye Moveme Sraeges Vsual Search (Suppleme) Jr Naemk ad Wlso S. Gesler Ceer for Percepual Sysems ad Deparme of Psychology, Uversy of exas a Aus, Aus X 787 Here we derve he deal searcher for he case
More informationAML710 CAD LECTURE 12 CUBIC SPLINE CURVES. Cubic Splines Matrix formulation Normalised cubic splines Alternate end conditions Parabolic blending
CUIC SLINE CURVES Cubc Sples Marx formulao Normalsed cubc sples Alerae ed codos arabolc bledg AML7 CAD LECTURE CUIC SLINE The ame sple comes from he physcal srume sple drafsme use o produce curves A geeral
More informationFault 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 informationLecture 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 informationPartial Molar Properties of solutions
Paral Molar Properes of soluos A soluo s a homogeeous mxure; ha s, a soluo s a oephase sysem wh more ha oe compoe. A homogeeous mxures of wo or more compoes he gas, lqud or sold phase The properes of a
More informationSolution. The straightforward approach is surprisingly difficult because one has to be careful about the limits.
ose ad Varably Homewor # (8), aswers Q: Power spera of some smple oses A Posso ose A Posso ose () s a sequee of dela-fuo pulses, eah ourrg depedely, a some rae r (More formally, s a sum of pulses of wdh
More informationStabilization of LTI Switched Systems with Input Time Delay. Engineering Letters, 14:2, EL_14_2_14 (Advance online publication: 16 May 2007) Lin Lin
Egeerg Leers, 4:2, EL_4_2_4 (Advace ole publcao: 6 May 27) Sablzao of LTI Swched Sysems wh Ipu Tme Delay L L Absrac Ths paper deals wh sablzao of LTI swched sysems wh pu me delay. A descrpo of sysems sablzao
More informationModel 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 informationRedundancy System Fault Sampling Under Imperfect Maintenance
A publcao of CHEMICAL EGIEERIG TRASACTIOS VOL. 33, 03 Gues Edors: Erco Zo, Pero Barald Copyrgh 03, AIDIC Servz S.r.l., ISB 978-88-95608-4-; ISS 974-979 The Iala Assocao of Chemcal Egeerg Ole a: www.adc./ce
More informationThe ray paths and travel times for multiple layers can be computed using ray-tracing, as demonstrated in Lab 3.
C. Trael me cures for mulple reflecors The ray pahs ad rael mes for mulple layers ca be compued usg ray-racg, as demosraed Lab. MATLAB scrp reflec_layers_.m performs smple ray racg. (m) ref(ms) ref(ms)
More informationDetermination of Antoine Equation Parameters. December 4, 2012 PreFEED Corporation Yoshio Kumagae. Introduction
refeed Soluos for R&D o Desg Deermao of oe Equao arameers Soluos for R&D o Desg December 4, 0 refeed orporao Yosho Kumagae refeed Iroduco hyscal propery daa s exremely mpora for performg process desg ad
More informationExam Supply Chain Management January 17, 2008
Exam Supply Cha Maageme Jauary 7, 008 IMPORTANT GUIELINES: The exam s closed book. Sudes may use a calculaor. The formularum s aached a he back of he assgme budle. Please wre your aswers o he blak pages
More informationSupplement Material for Inverse Probability Weighted Estimation of Local Average Treatment Effects: A Higher Order MSE Expansion
Suppleme Maeral for Iverse Probably Weged Esmao of Local Average Treame Effecs: A Hger Order MSE Expaso Sepe G. Doald Deparme of Ecoomcs Uversy of Texas a Aus Yu-C Hsu Isue of Ecoomcs Academa Sca Rober
More informationGlobal Financial Management
- - Global Facal Maaeme Dscou ad Prese alue Techques opyrh 999 by Ers Mau. All rhs reserved. No par of hs lecure may be reproduced whou he permsso of he auhor.. Overvew Las Revso: Sepember 9, 999 I hs
More informationFALL HOMEWORK NO. 6 - SOLUTION Problem 1.: Use the Storage-Indication Method to route the Input hydrograph tabulated below.
Jorge A. Ramírez HOMEWORK NO. 6 - SOLUTION Problem 1.: Use he Sorage-Idcao Mehod o roue he Ipu hydrograph abulaed below. Tme (h) Ipu Hydrograph (m 3 /s) Tme (h) Ipu Hydrograph (m 3 /s) 0 0 90 450 6 50
More informationSolution 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 informationMixed Integral Equation of Contact Problem in Position and Time
Ieraoal Joural of Basc & Appled Sceces IJBAS-IJENS Vol: No: 3 ed Iegral Equao of Coac Problem Poso ad me. A. Abdou S. J. oaquel Deparme of ahemacs Faculy of Educao Aleadra Uversy Egyp Deparme of ahemacs
More informationA 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 informationReal-time Classification of Large Data Sets using Binary Knapsack
Real-me Classfcao of Large Daa Ses usg Bary Kapsack Reao Bru bru@ds.uroma. Uversy of Roma La Sapeza AIRO 004-35h ANNUAL CONFERENCE OF THE ITALIAN OPERATIONS RESEARCH Sepember 7-0, 004, Lecce, Ialy Oule
More informationProbability Bracket Notation, Probability Vectors, Markov Chains and Stochastic Processes. Xing M. Wang Sherman Visual Lab, Sunnyvale, CA, USA
Probably Bracke Noao, Probably Vecors, Markov Chas ad Sochasc Processes Xg M. Wag Sherma Vsual Lab, Suyvale, CA, USA Table of Coes Absrac page1 1. Iroduco page. PBN ad Tme-depede Dscree Radom Varable.1.
More informationPricing Asian Options with Fourier Convolution
Prcg Asa Opos wh Fourer Covoluo Cheg-Hsug Shu Deparme of Compuer Scece ad Iformao Egeerg Naoal Tawa Uversy Coes. Iroduco. Backgroud 3. The Fourer Covoluo Mehod 3. Seward ad Hodges facorzao 3. Re-ceerg
More informationAsymptotic Regional Boundary Observer in Distributed Parameter Systems via Sensors Structures
Sesors,, 37-5 sesors ISSN 44-8 by MDPI hp://www.mdp.e/sesors Asympoc Regoal Boudary Observer Dsrbued Parameer Sysems va Sesors Srucures Raheam Al-Saphory Sysems Theory Laboraory, Uversy of Perpga, 5, aveue
More informationGeneral Complex Fuzzy Transformation Semigroups in Automata
Joural of Advaces Compuer Research Quarerly pissn: 345-606x eissn: 345-6078 Sar Brach Islamc Azad Uversy Sar IRIra Vol 7 No May 06 Pages: 7-37 wwwacrausaracr Geeral Complex uzzy Trasformao Semgroups Auomaa
More informationJORIND 9(2) December, ISSN
JORIND 9() December, 011. ISSN 1596 8308. www.rascampus.org., www.ajol.o/jourals/jord THE EXONENTIAL DISTRIBUTION AND THE ALICATION TO MARKOV MODELS Usma Yusu Abubakar Deparme o Mahemacs/Sascs Federal
More informationNOTE ON SIMPLE AND LOGARITHMIC RETURN
Appled udes Agrbusess ad Commerce AAC Ceer-r ublshg House, Debrece DOI:.94/AAC/27/-2/6 CIENIFIC AE NOE ON IME AND OGAIHMIC EUN aa Mskolcz Uversy of Debrece, Isue of Accoug ad Face mskolczpaa@gmal.com Absrac:
More informationFully Fuzzy Linear Systems Solving Using MOLP
World Appled Sceces Joural 12 (12): 2268-2273, 2011 ISSN 1818-4952 IDOSI Publcaos, 2011 Fully Fuzzy Lear Sysems Solvg Usg MOLP Tofgh Allahvraloo ad Nasser Mkaelvad Deparme of Mahemacs, Islamc Azad Uversy,
More informationFinal Exam Applied Econometrics
Fal Eam Appled Ecoomercs. 0 Sppose we have he followg regresso resl: Depede Varable: SAT Sample: 437 Iclded observaos: 437 Whe heeroskedasc-cosse sadard errors & covarace Varable Coeffce Sd. Error -Sasc
More informationResearch on portfolio model based on information entropy theory
Avalable ole www.jocpr.com Joural of Chemcal ad Pharmaceucal esearch, 204, 6(6):286-290 esearch Arcle ISSN : 0975-7384 CODEN(USA) : JCPC5 esearch o porfolo model based o formao eropy heory Zhag Jusha,
More informationUpper 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 informationThe Optimal Combination Forecasting Based on ARIMA,VAR and SSM
Advaces Compuer, Sgals ad Sysems (206) : 3-7 Clausus Scefc Press, Caada The Opmal Combao Forecasg Based o ARIMA,VAR ad SSM Bebe Che,a, Mgya Jag,b* School of Iformao Scece ad Egeerg, Shadog Uversy, Ja,
More informationHedging default risks of CDOs in Markovian contagion models
Hedgg defaul rss of CDOs Marova coago models J.-P. Laure, A. Cous, J-D. Fermaa May 27 Absrac We descrbe a hedgg sraegy of CDO raches based upo dyamc radg of he correspodg cred defaul swap dex. We rely
More informationThe 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 informationA Constitutive Model for Multi-Line Simulation of Granular Material Behavior Using Multi-Plane Pattern
Joural of Compuer Scece 5 (): 8-80, 009 ISSN 549-009 Scece Publcaos A Cosuve Model for Mul-Le Smulao of Graular Maeral Behavor Usg Mul-Plae Paer S.A. Sadread, A. Saed Darya ad M. Zae KN Toos Uversy of
More information-distributed random variables consisting of n samples each. Determine the asymptotic confidence intervals for
Assgme Sepha Brumme Ocober 8h, 003 9 h semeser, 70544 PREFACE I 004, I ed o sped wo semesers o a sudy abroad as a posgraduae exchage sude a he Uversy of Techology Sydey, Ausrala. Each opporuy o ehace my
More informationInternational 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 informationIntegral Φ0-Stability of Impulsive Differential Equations
Ope Joural of Appled Sceces, 5, 5, 65-66 Publsed Ole Ocober 5 ScRes p://wwwscrporg/joural/ojapps p://ddoorg/46/ojapps5564 Iegral Φ-Sably of Impulsve Dffereal Equaos Aju Sood, Sajay K Srvasava Appled Sceces
More informationθ = 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 informationSYRIAN SEISMIC CODE :
SYRIAN SEISMIC CODE 2004 : Two sac mehods have bee ssued Syra buldg code 2004 o calculae he laeral sesmc forces he buldg. The Frs Sac Mehod: I s he same mehod he prevous code (995) wh few modfcaos. I s
More informationThe Properties of Probability of Normal Chain
I. J. Coep. Mah. Sceces Vol. 8 23 o. 9 433-439 HIKARI Ld www.-hkar.co The Properes of Proaly of Noral Cha L Che School of Maheacs ad Sascs Zheghou Noral Uversy Zheghou Cy Hea Provce 4544 Cha cluu6697@sa.co
More informationSolution 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 informationOrbital Euclidean stability of the solutions of impulsive equations on the impulsive moments
Pure ad Appled Mahemacs Joural 25 4(: -8 Publshed ole Jauary 23 25 (hp://wwwscecepublshggroupcom/j/pamj do: 648/jpamj254 ISSN: 2326-979 (Pr ISSN: 2326-982 (Ole Orbal ucldea sably of he soluos of mpulsve
More informationPricing of CDO s Based on the Multivariate Wang Transform*
Prcg of DO s Based o he Mulvarae Wag Trasform* ASTIN 2009 olloquum @ Helsk 02 Jue 2009 Masaak Kma Tokyo Meropola versy/ Kyoo versy Emal: kma@mu.ac.p hp://www.comp.mu.ac.p/kmam * Jo Work wh Sh-ch Moomya
More informationEfficient 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 informationOn subsets of the hypercube with prescribed Hamming distances
O subses of he hypercube wh prescrbed Hammg dsaces Hao Huag Oleksy Klurma Cosm Pohoaa Absrac A celebraed heorem of Klema exremal combaorcs saes ha a colleco of bary vecors {0, 1} wh dameer d has cardaly
More informationVARIATIONAL ITERATION METHOD FOR DELAY DIFFERENTIAL-ALGEBRAIC EQUATIONS. Hunan , China,
Mahemacal ad Compuaoal Applcaos Vol. 5 No. 5 pp. 834-839. Assocao for Scefc Research VARIATIONAL ITERATION METHOD FOR DELAY DIFFERENTIAL-ALGEBRAIC EQUATIONS Hoglag Lu Aguo Xao Yogxag Zhao School of Mahemacs
More informationCyclically 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 informationAvailable online Journal of Scientific and Engineering Research, 2014, 1(1): Research Article
Avalable ole wwwjsaercom Joural o Scec ad Egeerg Research, 0, ():0-9 Research Arcle ISSN: 39-630 CODEN(USA): JSERBR NEW INFORMATION INEUALITIES ON DIFFERENCE OF GENERALIZED DIVERGENCES AND ITS APPLICATION
More informationOptimal Control and Hamiltonian System
Pure ad Appled Maheacs Joural 206; 5(3: 77-8 hp://www.scecepublshggroup.co//pa do: 0.648/.pa.2060503.3 ISSN: 2326-9790 (Pr; ISSN: 2326-982 (Ole Opal Corol ad Haloa Syse Esoh Shedrack Massawe Depare of
More informationComparison 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 informationChapter 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 informationCompetitive 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 informationFourth Order Runge-Kutta Method Based On Geometric Mean for Hybrid Fuzzy Initial Value Problems
IOSR Joural of Mahemacs (IOSR-JM) e-issn: 2278-5728, p-issn: 29-765X. Volume, Issue 2 Ver. II (Mar. - Apr. 27), PP 4-5 www.osrjourals.org Fourh Order Ruge-Kua Mehod Based O Geomerc Mea for Hybrd Fuzzy
More informationSynchronization of Complex Network System with Time-Varying Delay Via Periodically Intermittent Control
Sychrozao of Complex ework Sysem wh me-varyg Delay Va Perodcally Ierme Corol JIAG Ya Deparme of Elecrcal ad Iformao Egeerg Hua Elecrcal College of echology Xaga 4, Cha Absrac he sychrozao corol problem
More informationMATH 507a ASSIGNMENT 4 SOLUTIONS FALL 2018 Prof. Alexander. g (x) dx = g(b) g(0) = g(b),
MATH 57a ASSIGNMENT 4 SOLUTIONS FALL 28 Prof. Alexader (2.3.8)(a) Le g(x) = x/( + x) for x. The g (x) = /( + x) 2 is decreasig, so for a, b, g(a + b) g(a) = a+b a g (x) dx b so g(a + b) g(a) + g(b). Sice
More informationApplication of the stochastic self-training procedure for the modelling of extreme floods
The Exremes of he Exremes: Exraordary Floods (Proceedgs of a symposum held a Reyjav, Icelad, July 000). IAHS Publ. o. 7, 00. 37 Applcao of he sochasc self-rag procedure for he modellg of exreme floods
More informationExtremal graph theory II: K t and K t,t
Exremal graph heory II: K ad K, Lecure Graph Theory 06 EPFL Frak de Zeeuw I his lecure, we geeralize he wo mai heorems from he las lecure, from riagles K 3 o complee graphs K, ad from squares K, o complee
More informationRATIO ESTIMATORS USING CHARACTERISTICS OF POISSON DISTRIBUTION WITH APPLICATION TO EARTHQUAKE DATA
The 7 h Ieraoal as of Sascs ad Ecoomcs Prague Sepember 9-0 Absrac RATIO ESTIMATORS USING HARATERISTIS OF POISSON ISTRIBUTION WITH APPLIATION TO EARTHQUAKE ATA Gamze Özel Naural pulaos bolog geecs educao
More informationRELIABILITY AND CREDIT RISK MODELS
Chaper 8 RELIABILITY AND CREDIT RIK MODEL I hs chaper, he reader wll frs fd a shor summary of he basc oos of relably ad he he sem-markov exesos. Afer ha, he classcal problem of cred rsk s also preseed
More informationStability Criterion for BAM Neural Networks of Neutral- Type with Interval Time-Varying Delays
Avalable ole a www.scecedrec.com Proceda Egeerg 5 (0) 86 80 Advaced Corol Egeergad Iformao Scece Sably Crero for BAM Neural Neworks of Neural- ype wh Ierval me-varyg Delays Guoqua Lu a* Smo X. Yag ab a
More informationMath 6710, Fall 2016 Final Exam Solutions
Mah 67, Fall 6 Fial Exam Soluios. Firs, a sude poied ou a suble hig: if P (X i p >, he X + + X (X + + X / ( evaluaes o / wih probabiliy p >. This is roublesome because a radom variable is supposed o be
More informationSolving fuzzy linear programming problems with piecewise linear membership functions by the determination of a crisp maximizing decision
Frs Jo Cogress o Fuzzy ad Iellge Sysems Ferdows Uversy of Mashhad Ira 9-3 Aug 7 Iellge Sysems Scefc Socey of Ira Solvg fuzzy lear programmg problems wh pecewse lear membershp fucos by he deermao of a crsp
More informationAs evident from the full-sample-model, we continue to assume that individual errors are identically and
Maxmum Lkelhood smao Greee Ch.4; App. R scrp modsa, modsb If we feel safe makg assumpos o he sascal dsrbuo of he error erm, Maxmum Lkelhood smao (ML) s a aracve alerave o Leas Squares for lear regresso
More informationCONJECTURAL VARIATION MODELS AND SUPERGAMES WITH PRICE-COMPETITION IN A DIFFERENTIATED PRODUCT OLIGOPOLY
CONJECTURAL VARIATION MODELS AND SUPERGAMES WITH PRICE-COMPETITION IN A DIFFERENTIATED PRODUCT OLIGOPOLY Mchael Pfaffermayr * WIFO-Workg Paper No. 13 revsed verso, Aprl 1999 Absrac: Cojecural varao models
More informationDensity estimation III. Linear regression.
Lecure 6 Mlos Hauskrec mlos@cs.p.eu 539 Seo Square Des esmao III. Lear regresso. Daa: Des esmao D { D D.. D} D a vecor of arbue values Obecve: r o esmae e uerlg rue probabl srbuo over varables X px usg
More informationUnit 10. The Lie Algebra of Vector Fields
U 10. The Le Algebra of Vecor Felds ================================================================================================================================================================ -----------------------------------
More informationA 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 informationOther Topics in Kernel Method Statistical Inference with Reproducing Kernel Hilbert Space
Oher Topcs Kerel Mehod Sascal Iferece wh Reproducg Kerel Hlber Space Kej Fukumzu Isue of Sascal Mahemacs, ROIS Deparme of Sascal Scece, Graduae Uversy for Advaced Sudes Sepember 6, 008 / Sascal Learg Theory
More information