NORMAL VARIANCE-MEAN MIXTURES (III) OPTION PRICING THROUGH STATE-PRICE DEFLATORS

Size: px
Start display at page:

Download "NORMAL VARIANCE-MEAN MIXTURES (III) OPTION PRICING THROUGH STATE-PRICE DEFLATORS"

Transcription

1 Joural of Mahmaial i: Adva ad Appliaio Volum 5 4 Pag 3-4 NORMAL VARIANCE-MEAN MIURE (III) OPION PRICIN HROUH AE-PRICE DEFLAOR WERNER HÜRLIMANN Wolr Klur Fiaial rvi fldra 69 CH-88 Zürih izrlad -mail: rr.hurlima@olrlur.om Ara A alraiv o h Bla-hol-Vai dflaor i propod. I i ad o a impl mulivaria poial ormal varia-ma miur Lév pro. Clod-form aalial igral formula for priig h Europa gomri a opio ih a dflad ormal varia-ma pri pro ar oaid. Appliaio o h varia-gamma h ormal ivr auia ad h ormal mprd al pro ar iludd. A dd Bla-hol formula ha a io aou h orrlaio ruur of h mar i alo drivd.. Iroduio h pr oriuio follo up h auhor ivigaio aroud h ormal varia-ma (NVM) miur modl irodud i Bardorff-Nil al. [6]. I i moivad h arh for o- auia mulivaria opio priig modl a a alraiv o h Mahmai u Claifiaio: 6P5 6P 9B4 9B5 9. Kord ad phra: ormal varia-ma miur Lév pro varia-gamma ormal ivr auia ormal mprd al a-pri dflaor mar pri of ri gomri a opio dd Bla-hol formula. Rivd Dmr iifi Adva Pulihr

2 4 WERNER HÜRLIMANN mulivaria Bla-hol modl. Our mphai i o rul ha a formulad for a poil larg la of NVM miur modl i h piri of Korholm [3]; Bigham ad Kil [7]; ad p ad a [5] amog ohr. h op of a-pri dflaor or ohai diou faor hih ha irodud Duffi [7] pp. 3 ad 97 i a ovi igrdi of gral fiaial priig rul. I oai iformaio aou h valuaio of pam i diffr a a diffr poi i im. h a-pri dflaor i a aural io of h oio of a pri ha r irodud arlir ad udid Arro [ 3 4 5]; Dru [4]; Ngihi [44]; ad Ro [48] a milo i h hior of a priig ( Dimo ad Muavia [6]). hough gral framor for drivig a-pri dflaor i (.g. Milr ad Pro [4]; Jala al. [3]; Mu [43]) hr ar o ma papr hih propo plii prio for hm ad hir orrpodig diriuio fuio. A hor aou of h o follo. io rall h mulivaria NVM miur modl i i impl Lév pro form. I graliz h oruio of h mulivaria poial varia-gamma pro oidrd i Co ad aov []; Luiao ad hou [35]; ad fir udid i Hürlima [4 5]. horial ad aiial uifiaio for i u ar rifl miod. I io 3 driv h mulivaria NVM dflaor a alraiv o h mulivaria Bla-hol-Vai (BV) dflaor irodud i Hürlima [] ( alo Hürlima [3 8]). Aalial igral formula for priig h Europa gomri a all opio ihi h mulivaria NVM mar ih NVM dflaor ar drivd ad diud i io 4. h fial io 5 irodu a mulivaria uordiad a pri modl ho uivaria vrio oiid ih h modl Hur al. []. A a pial a i ilud a dd Bla-hol formula ha a io aou h orrlaio ruur of h mar. h pr ovl approah i gral ad impl. From a aiial prpiv paramr imaio a do ih a mulivaria mom mhod ( Hürlima [7]). Morovr h drivaio of h dd Bla-hol formula ha a lmar proailii flavour.

3 NORMAL VARIANCE-MEAN MIURE (III) 5. A lio of Mulivaria Normal Varia-Ma Miur Pro h uivaria ormal varia-ma (NVM) miur pro i dfid a a drifd Broia moio im hagd a idpd miig pro. Vid from h iiial im i i dfid hr θ W > > < θ < (.) W i a adard Wir pro ad i a idpd uordiaor ha i a iraig poiiv Lév pro. i i a Lév pro h dami of h pro i drmid i diriuio a ui im. h radom varial follo a diriuio ho umula graig fuio (gf) C ( u) l E [ p( u )] i aumd o i ovr om op irval. B oidrig i paralll diffr uordiaor a uifid approah o vral impora NVM miur modl i poil. Our fou i rrid o hr of hm (for furhr poil hoi oul Hürlima [6] io 4). (a) Varia-gamma (V) pro h uordiaor ~ ( Γ ) i a gamma pro ih ui ma ra ad varia ra. h radom varial ~ V( θ ) follo a hr paramr V diriuio ih gf: C ( u) l{ ( θu u )} > < θ <. C (.) hi formula i oaid from h gf ( u) l( u) of h gamma radom varial odiioig uig ha ~ N( θ ) i ormall diriud. h irm of h pro follo a V < diriuio aml ~ V( θ ).

4 6 WERNER HÜRLIMANN () Normal ivr auia (NI) pro h gf of h ivr auia uordiaor i ( u) C { u } >. h radom varial ~ NI( θ ) follo a hr paramr NI diriuio ih gf: C ( u) { ( θu u )} > < θ <. h irm of h pro ar NI( θ ) diriud. () Normal mprd al (N) pro (.3) h uordiaor follo a laial mprd al pro ih α α gf ( u) α { ( u ) } > α ( ). h radom C varial ~ N ( θ α ) follo a four paramr N diriuio ih gf: C α α α α ( u) α { ( ( θu u )) } > α ( ) < θ <. (.4) α h irm of h pro ar N( θ α ) diriud. h pial a α i h NI pro. imilarl o ho mulivaria vrio miod i Hürlima [4] for h V pro diffr mulivaria modl for ah NVM miur pro a dfid. For implii oidr hr ol mulivaria Lév pro ih NVM ompo of h p hr h E θ W (.5) W ar orrlad adard Wir pro uh ha () i ( [ dw dw ) ] d. Dpi all i horomig h u of h modl i

5 NORMAL VARIANCE-MEAN MIURE (III) 7 (.5) i uifid horiall looig a h varia of i NVM margi a ui im aml Var [ ( ) ] Var[ ] θ. (.6) Eah varia dompo io a idiorai ompo ha i ariud o h Broia moio ad a ogou ompo Var[ ] θ ha i du o h im hag of h Broia moio. h paramr θ govr h pour of h margi o h gloal mar urai maurd h ommo varia Var[ ]. imilarl o o ha h ad uroi ar alo affd h igl margial ig ad h ommo uordiad paramr() ( Hürlima [7] horm.). O h ohr had h aiial mom mhod dvlopd i Hürlima [7] ad i uful appliaio o ral-orld daa uifi i u i praial or. For h rao i i lgiima o fou fir o h modl (.5). O o ha h gf of h NVM miur modl i giv C ( u) C ( θ u u u) θ ( θ θ ) ( ii ) u ( u u ). (.7) h oi gf of h oidrd mulivaria NVM pro a prd i lod-form. Propoiio.. h oi gf of h mulivaria NVM miur () ( pro ( ) ) ih paramr θ ( θ θ ) ( ) ad uordiaor i drmid a follo: Mulivaria V pro i i gf h mulivaria V radom vor ~ V( θ ) ha oi C ( u) l{ ( θ u u u)} u ( u u ). (.8)

6 8 WERNER HÜRLIMANN Mulivaria NI pro gf h mulivaria NI radom vor ~ NI( θ ) ha oi C ( u) { ( θ u u u)} u ( u u ). (.9) Mulivaria N pro α h mulivaria N radom vor ~ N( θ α ) ha oi gf C α α α ( u) α { ( ( θ u u u)) } u ( u u ). (.) Proof. hi follo from (.7) ad h plii form for h gf of h uordiaor. 3. Uifid a-pri Dflaor Rpraio for h Epoial NVM Miur Pro I h folloig implif ad graliz h produr i Hürlima [4] io 4. Coidr h folloig la of a priig modl. iv h urr pri of ri a a iiial im hir fuur pri a im > ar drid poial NVM miur pro ( ( ) p µ ω ) (3.) hr µ rpr h ma logarihmi ra of rur of h -h ( ri a pr im ui ad h radom vor ( ) ) follo a mulivaria NVM miur pro. Uig h dfiig rlaiohip [ ( ) ] E p( µ ) a ui im o ha ω C ( )() < hr o aum ha h gf of

7 NORMAL VARIANCE-MEAN MIURE (III) 9 i ovr om op irval hih oai o. uppo ha h mulivaria NVM dflaor of dimio ha h am form a h pri pro i (3.). For om paramr α ad vor ( ) (oh o drmid) o for i (a Ehr raformd maur) D p ( α ) >. (3.) A impl gf alulaio ho ha h a-pri dflaor marigal odiio E r [ ] [ ( ) ] D E D > (3.3) ar quival ih h m of quaio i h uo α ω (u ha i a Lév pro h C ( u) C ( u)): ( ( r α C µ ω α ) C ) ( ( ) ( ) ) δ. (3.4) Irig h fir quaio io h od o ild h ar rlaiohip ( ( µ r ω C ) ) C ( ). (3.5) i h m (3.4) ha dgr of frdom h uo ho ariraril a ω a ω µ r C (3.6) hih i irprd a h (im-idpd) NVM mar pri of h -h ri a. Wih h mad rriio o h gf hi valu i ala fii. Ird io (3.5) ho ha h paramr vor i drmid h quaio C ( ( ) ) C ( ). (3.7)

8 WERNER HÜRLIMANN W ar rad o ho h folloig uifid NVM dflaor rpraio: horm 3. (Mulivaria NVM dflaor). iv i a ri-fr a ih oa rur r ad ri a ih ral-orld pri (3.) hr o aum ha h gf of i ovr om op irval hih oai o. h h mulivaria NVM dflaor of h poial NVM miur pro i drmid D p( α ( ) ) α r C ( ) θ ( γ ) γ. (3.8) Morovr i h uivaria a o ha γ. Proof. h fir quaio i (3.4) ild α. i ar Lév pro o ha C ( u) C ( u) C C ( u) h (.7) i quival ih h quaio C ( u) C ( θ u u u). I follo ha h odiio (3.7) ar quival ih h quaio: θ θ. A raighforard alulaio ho ha h lar i quival ih h ad odiio for hr i a o ha γ (mp um). 4. Priig omri Ba Opio for h Epoial NVM Miur Pro I h liraur o diiguih o p of a opio. h arihmi a opio i dfid o h ighd arihmi avrag of a pri uh ha

9 NORMAL VARIANCE-MEAN MIURE (III) (4.) hr h igh ( ) a gaiv ad i hi iuaio i ilud prad opio. h gomri a opio i dfid o h ighd gomri avrag of a pri ( [ ) ] >. (4.) i diriuio fuio of ighd um of orrlad a pri a uuall o ri i plii lod form h priig of arihmi a opio i rahr hallgig. Diffr ad mol approima mhod o pri hm hav dvlopd o far ma auhor iludig urull ad Wama [5]; Milv ad Por [4]; Krl al. [34]; Carmoa ad Durrlma []; Borova al. [8]; Wu al. [53]; Varamaa ad Aladr [5]; Aladr ad Varamaa []; ad Brigo al. [9]. h priig of h gomri a opio i mor raighforard. o illura h uful i opio priig of h mulivaria NVM dflaor rri h aio o h priig of gomri a opio. W oai a gral aalial NVM priig formula hih a vid a a gralizaio of h Bla-hol formula. Morovr plii aalial formula ar diplad for h poial V ad NI pri pro. I pariular a implr alraiv o h NI lod-form formula Wu al. [53] i drivd. Coidr a Europa gomri a all opio ih mauri da ad ri pri K i h mulivaria NVM mar ih ri a ha follo h pri pro (3.) ad i u o h NVM dflaor (3.8). I pri a iiial im i giv C E[ D ( K ) ]. (4.3)

10 WERNER HÜRLIMANN A raighforard alulaio hih a io aou h ormalizig odiio r µ ω ho ha { } p C D { }. p r C K K D (4.4) I h folloig uppo ha h di fuio f of h miig radom varial i. hrough odiioig o a rri (4.3) a d f C C C ih [( { ( )} W E C θ p p{ ( )}) ]. W K r θ (4.5) Eah of h o odiioal orrlad ormall diriud um i (4.5) i ormall diriud ad hir oi diriuio i ivaria ormal. hrfor h diriuio of h odiioal radom oupl ( ) i drmid h odiioal ma [ ] m m E θ [ ] m m E θ (4.6) h odiioal varia

11 NORMAL VARIANCE-MEAN MIURE (III) 3 [ ] Var i i i i i [ ] Var i i i i (4.7) ad h odiioal ovaria [ ] Cov. i i i i i (4.8) No l Φ do h adard ormal diriuio Φ Φ i urvival fuio ad Φ ϕ i di. h ivaria adard ormal di i dfid ad dod. p ; π ϕ From (4.5) ad h dfiiio (4.6)-(4.8) o oai. ; dd K C m r m ϕ (4.9) h prio i h ra of (4.9) i o-gaiv providd ih. l m m r K i ( ) ; ϕ ϕ ϕ a paraio of h doul igral ild d J C ϕ ih h ir igral

12 WERNER HÜRLIMANN 4 { } m r m K J ( ). d ϕ (4.) A raighforard appliaio of Lmma A i h Appdi ild ( ) J m Φ ( ). Φ K m r o implif oaio rri h argum ihi h ormal diriuio fuio a ih a ( ) l m m K r a. l m m K r Furhrmor o ha. ϕ ϕ ϕ ϕ No uig i h Lmma A of h Appdi o oai. K a C m r m Φ Φ Bad o h aov prio for h offii ad a o oai furhr

13 NORMAL VARIANCE-MEAN MIURE (III) 5 C ( m ) Φ r ( l( K ) m Ω m Ω Ω ) r ( m ) K Φ r ( l( K ) m Ω m Ω Ω ) Ω Ω Ω Ω. (4.) ummarizig hav ho h folloig mai rul: horm 4. (omri a all opio formula i h mulivaria NVM mar). iv i h mulivaria poial NVM miur pro (3.) u o h NVM dflaor (3.8). h i h aov oaio o ha C C E[ D ( K ) ] r { Ψ ( a d ) K Ψ ( a )} d (4.) Ψ a ( a d ) Φ( d ) f d a m m m ( ) Ω Ω r l( K ) d. (4.3) Ω A pariular ia for hih h formula (4.) implifi oidral i h pial a d ha i h ri pri i qual o K. I hi iuaio (4.) rri a r C r C E[ D ( ) ] a { Φ( ) a Φ( )} f d. (4.4)

14 6 WERNER HÜRLIMANN A hi poi a impora oio ih h adard o-arirag framor of mahmaial fia mu miod (.g. Wührih al. [54] uio.5; ad Wührih ad Mrz [55] Chapr ). B h fudamal horm of a priig h aumpio of o-arirag (a form of ffii mar hpohi) i quival ih h i of a quival marigal maur for dflad pri pro. I ompl mar h quival marigal maur i uiqu prf rpliaio of oig laim hold ad raighforard priig appli. I iompl mar a oomi modl i rquird o did upo hih quival marigal maur i appropria. No l P do h ral-orld maur ad P do a quival marigal maur. h o a ihr or udr P hr h pri pro ar dflad ih a a-pri dflaor. Alraivl o a or udr P diouig h pri pro ih h a aou umrair. Worig ih fiaial irum ol o of or udr P. Bu if addiioall iura liailii ar oidrd o or udr P ( Wührih al. [54] Rmar.3). A r o-rivial ampl i priig of h guarad maimum iflaio dah fi (MIDB) opio (Hürlima []; Equaio (5.4) i Hürlima [3]). horm 4. dmora h praiaili of h a-pri dflaor approah for poial NVM pri pro a applid o h Europa gomri a all opio. I a hr h di fuio of h miig radom varial i o h formula (4.)-(4.4) ild a lod-form aalial priig m for h gomri a opio. h odiio udr hih h mulivaria NVM mar i ompl ad arirag-fr ha i hr i a uiqu quival marigal maur ad pri ar uiqul dfid (hhr udr P or udr P ih a-pri dflaor) rmai o foud. hi i a o-rivial prolm ha ha ald o far oll for h mulivaria Bla-hol modl ( Dha al. [5]).

15 NORMAL VARIANCE-MEAN MIURE (III) 7 Eampl 4.. Mulivaria poial V a priig modl. h uordiaor ~ Γ( γ γ) γ i gamma diriud ih di γ γ f γ ( γ) Γ( γ). (4.5) Maig h hag of varial z ( a) h Ψ -fuio i (4.3) i of h form ad i Koz al. [33] p. 96 (Europa ri-ural all opio pri for a poial V pri pro) hih i h origial lod form formula Mada al. [37] horm ad Appdi ( alo Mada [36] uio 6.3. Equaio (4)). h advaag of h alraiv priig formula (4.) i h impl paramr ruur (4.3). Alo h pial a (4.4) implifi oidral. A imilar aalial priig m for h Margra opio ha oidrd prvioul i Hürlima [4 5]. I pariular for a Erlag uordiaor ih igr paramr γ formula of h p (4.4) rdu o fii um prio ( Hürlima [4] Appdi ). A a rul driv a lod-form prio for h Ψ -fuio a a um of a iompl a fuio ad a igrad Madoald fuio. I i muh implr ha h origial formula i rm of h Madoald fuio (modifid Bl fuio of h od id) ad h dgra hpr-gomri fuio of o varial. I h folloig l a ( γ) ~ Γ( γ ) a adardizd gamma γ radom varial ih di fa ( γ ) Γ( γ) B( γ ) a a ih diriuio fuio γ F ( ) d (4.6) B γ B( γ )

16 8 WERNER HÜRLIMANN ad V ( γ α ) a varia-gamma ih di (.g. Hürlima [9] Equaio (A4.)) f V γ γ ( γ α ) ( α) πγ( γ) α p α K ( ( α ) ) γ. (4.7) horm 4. (Clod-form V Ψ -fuio rpraio). h ormal gamma mid igral Ψ( a γ) Φ( a ) f d aifi h folloig proailii rpraio: Ψ( a γ) { g( ) F a B ( )} γ a λ g ( a) f ( ) d. (4.8) V γ g a g a Proof. hi i ho i h Appdi. I pariular h V formula (4.4) a rri i rm of h iompl a fuio. Eampl 4.. Mulivaria poial NI a priig modl. h uordiaor ~ I( ) i ivr auia diriud ih di f p{ π 3 ( ) }. (4.9) Priig of h gomri a opio udr a quival marigal maur ha prvioul oidrd i Wu al. [53]. Hovr hir fa Fourir raform priig formula i mor ompl ha h lodform prio (4.) lo.

17 NORMAL VARIANCE-MEAN MIURE (III) 9 I h folloig l NRI ( α δ) a mmri NVM miur radom varial ih riproal ivr auia miig di αδ f NRI ( α δ) α K ( α δ ). (4.) π I i oaid from h gralizd hproli diriuio a NRI ( α δ) H( α δ). h di of h ormal ivr auia NI ( α δ) H( α δ) i dod δ α K ( α δ ) fni α δ αδ (4.) π δ horm 4.3 (Clod-form NI Ψ -fuio rpraio). h ormal gamma mid igral Ψ( a γ) Φ( a ) f d aifi h folloig proailii rpraio: ( a Ψ γ) g( ) F NRI ( ) g ( a) F ( ) ( a ). (4.) NI g a A io of h proailii rpraio (4.8) ad (4.) o arirar gralizd ivr auia miig dii i foud i Hürlima [3]. Eampl 4.3. Mulivaria poial N a priig modl. h laial mprd al uordiaor ha o a raal di fuio u a rlaivl impl hararii fuio. I hi iuaio o approima h igral (4.3)-(4.4) u of h fa Fourir raform (FF) of f (.g. Hürlima [7] Appdi ).

18 3 WERNER HÜRLIMANN 5. A impl Mulivaria uordiad A Pri Modl h pial hoi of h mulivaria NVM dflaor (3.8) impli ha C ( ) α r θ ( γ ). I follo ha h NVM dflaor dgra o h ri-fr diou faor D. I hi mulivaria uordiad mar h ri a follo h pri pro (ir (3.6) io (3.)) ( ( ) p r γ W ) (5.) hr h [ () i ( dw ) dw ] i r W ar orrlad adard Wir pro uh ha E ad i a idpd uordiaor. h priig of h Europa gomri a all opio ih mauri da ad ri pri K implifi omha. horm 5. (omri a all opio formula i h mulivaria uordiad mar). iv i h mulivaria uordiad a pri modl (5.) u o h ri-fr diou faor D. h o ha r C r E[ D ( K ) ] Ψ ( m d) K ( m ) (5.) Ψ d Ψ ( ) m ( m d) Φ d ( m ) ( ) f d Ψ d m ( m d) Φ f d (5.3)

19 NORMAL VARIANCE-MEAN MIURE (III) 3 l. r K d m i i i i Proof. hi rul i a pial ia of horm 4. ad a imilarl drivd i a implr a (o-dimioal igraio ol). h pializaio o a gomri a ih a igl ri a i iruiv. Corollar 5. (Europa a-pri dflad all opio pri for a igl uordiad a). iv i h mulivaria uordiad a pri modl (5.) u o h ri-fr a-pri dflaor. r D h o ha [ ( () ) ] () ( { } ) ( { } ) d K d K D E C r Ψ Ψ (5.4) ( { } ) ( ) d f d d Φ Ψ ( { } ) ( ) d f d d Φ Ψ. l r K d (5.5) Proof. o ha m ad h rul follo immdial hrough irio io h formula (5.). Corollar 5. i rlad o om rmaral prviou rul. uppo ha i h igl a i uorrlad ih h

20 3 WERNER HÜRLIMANN ohr ri a h. h o rovr h opio formula Hur al. [] for h uivaria uordiad a pri modl ( alo Rahv ad Mii [47]; ad Rahv al. [46] uio 7.6 ih orrio of h mipri i h o pri modl hovr). If ih proaili o (Bla-hol-Mro modl) h (5.4) ild a dd Bla-hol formula ha a io aou h orrlaio ruur of h mar. Bid h mulivaria poial V NI ad N pro h propod mulivaria uordiaor mar oai a rih la of fail modl. If i h al pro propod Madlro [39 4] ad Fama [8] o ha a logal modl. If i h CIR pro Co al. [3] o oai a mulivaria vrio of h modl Ho [9] ( Rahv al. [46] uio 7.6). h CMY ad Mir pifiaio Carr al. [] rp. hou ad ugl [49]; ad Pima ad Yor [45] ar alo uordiad modl a ho Mada ad Yor [38]. om diffr ih h approah Hur al. [] a od. Whil h auhor or udr a quival marigal maur P i h uivaria a ol or i h mulivaria a udr P uig a a-pri dflaor. Hur al. argu i io 5 ha a quival marigal maur i o pd o i ul o aum ha µ r. I hi a hir (quival marigal maur) mar pri of ri rad (Equaio (5.)) ω ( θ ). (5.6) h aumpio µ r orrpod o h hoi µ ω r i h dflaor approah. B viru of (3.6) h (a-pri dflaor) mar pri of ri qual () C ( θ ) C ( ( )). ω µ r C (5.7)

21 NORMAL VARIANCE-MEAN MIURE (III) 33 Udr h mad aumpio oh mar pri of ri ar qual ad vaih if ad ol if o ha ad i hi iuaio h formula (5.4) oiid ih h Hur-Pla-Rahv formula. Our mulivaria uordiad a pri modl i a gui io au i ovr a ih o-vaihig mar pri of ri for hih µ r. For hi h odiio i fiaial mar i rahr h rul ha h pio. hrfor h approah i of primordial impora. i h quival marigal approah ad h a-pri dflaor approah ar quival (omm afr horm 4.) hr i o d o dipla h maur P i h mulivaria a. Rfr [] C. Aladr ad A. Varamaa Aalial approimaio for muli-a opio priig Mahmaial Fia (4) () [] K. Arro A Eio of h Bai horm of Claial Wlfar Eoomi I: Prodig of h d Brl mpoium o Mahmaial aii ad Proaili pp Uivri of Califoria Pr 95. [3] K. Arro L rol d valur ourièr pour la répariio la millur d riqu Eoomria 4 (953) 4-47 (Eglih ralaio Arro (964)). [4] K. Arro h rol of urii i h opimal alloaio of ri-arig Rvi of Eoomi udi 3 (964) [5] K. Arro Ea i h hor of Ri-Barig Norh-Hollad 97. [6] O. E. Bardorff-Nil J. K ad M. or Normal varia-ma miur ad z diriuio I. ai. Rvi 5 (98) [7] N. H. Bigham ad R. Kil mi-paramri modllig i fia: horial foudaio Quaiaiv Fia () -. [8]. Borova F. J. Prmaa ad H. V. D. Wid A lod form approah o h valuaio ad hdgig of a ad prad opio h Joural of Drivaiv 4(4) (7) 8-4. [9] D. Brigo F. Rapiarda ad A. ridi h arirag-fr mulivaria miur dami modl: Coi igl-a ad id volaili mil ariv:3.7v [q-fi.pr] (3).

22 34 WERNER HÜRLIMANN [] R. Carmoa ad V. Durrlma ralizig h Bla-hol formula o mulivaria oig laim h Joural of Compuaioal Fia 9() (6) [] P. Carr H. ma D. B. Mada ad M. Yor h fi ruur of a rur: A mpirial ivigaio J. of Bui 75() () [] R. Co ad P. aov Fiaial Modllig ih Jump Pro CRC Fiaial Mahmai ri Chapma ad Hall 4. [3] J. C. Co J. E. Igroll ad. A. Ro A hor of h rm ruur of ir ra Eoomria 53 (985) [4]. Dru Valuaio quilirium ad Paro opimum Prodig of h Naioal Aadm of i 4 (954) [5] J. Dha A. Kuuh ad D. Lidr h mulivaria Bla & hol mar: Codiio for ompl ad o-arirag hor Proa. Mah. ai. 88 (3) -4. [6] E. Dimo ad M. Muavia hr uri of a priig J. of Baig ad Fia 3 (999) [7] D. Duffi Dami A Priig hor Prio Uivri Pr N Jr 99. [8] E. Fama Madlro ad h al Paria hpohi J. of Bui 36(4) (963) [9]. L. Ho A lod form oluio for opio ih ohai volaili ih appliaio o od ad urr opio Rvi of Fiaial udi 6 (993) []. R. Hur E. Pla ad.. Rahv Opio priig for a log-al a pri modl Mahmaial ad Compur Modllig 9 (999) 5-9. [] W. Hürlima A io of h Bla-hol ad Margra formula o a mulipl ri oom Applid Mahmai (4) (a) [] W. Hürlima Aalial priig of a iura mddd opio: Alraiv formula ad auia approimaio J. of Iformai ad Mah. i 3() () [3] W. Hürlima h algra of opio priig: hor ad appliaio I. J. of Algra ad aii () () [4] W. Hürlima Margra formula for a impl ivaria poial variagamma pri pro (I): hor I. J. iifi Iovaiv Mah. Rarh () (3a) -6. [5] W. Hürlima Margra formula for a impl ivaria poial variagamma pri pro (II): aiial imaio ad appliaio I. J. iifi Iovaiv Mah. Rarh () (3)

23 NORMAL VARIANCE-MEAN MIURE (III) 35 [6] W. Hürlima Normal varia-ma miur (I): A iquali ad uroi Adva i Iqualii ad Appliaio (i pr) 4 (3). URL:hp://i.org/id.php/aia/aril/vi/7 [7] W. Hürlima Normal varia-ma miur (II): A mulivaria mom mhod J. of Mahmaial ad Compuaioal i (i pr) (3d). [8] W. Hürlima Opio priig i h mulivaria Bla-hol mar ih Vai ir ra Mahmaial Fia Lr () (3) -8. [9] W. Hürlima Porfolio raig ffii (I): Normal varia gamma rur Iraioal J. of Mah. Arhiv 4(5) (3f) 9-8. [3] W. Hürlima Proailii rpraio of a ormal gralizd ivr auia igral: Appliaio o opio priig J. of Adva i Mahmai 4() (3g) [3] M. Jala M. Yor ad M. Ch Mahmaial Mhod for Fiaial Mar prigr Fia prigr-vrlag Lodo Limid 9. [3] L. Korholm h miparamri ormal varia-ma miur modl adiavia Joural of aii 7 () -67. [33]. Koz. J. Kozuoi ad K. Podgori h Lapla Diriuio ad ralizaio: A Rvii ih Appliaio Birhäur Boo. [34] M. Krl J. d Ko R. Kor ad.-k. Ma A aali of priig mhod for a opio Willmo Magazi (4) [35] E. Luiao ad W. hou A mulivaria ump-driv fiaial a modl Quaiaiv Fia 6(5) (6) [36] D. Mada Purl Dioiuou A Priig Pro I: E. Jouii J. Cviai ad M. Muila (Ed.) Opio Priig Ir Ra ad Ri Maagm 5-53 Camridg Uivri Pr Camridg. [37] D. Mada P. Carr ad E. Chag h varia gamma pro ad opio priig Europa Fia Rvi (998) [38] D. Mada ad M. Yor Rprig h CMY ad Mir Lév pro a im hagd Broia moio h Joural of Compuaioal Fia () (8) [39] B. Madlro h variaio of rai pulaiv pri J. of Bui 36 (963) [4] B. Madlro h variaio of om ohr pulaiv pri J. of Bui 4 (967) [4] M. A. Milv ad. E. Por A lod-form approimaio for valuig a opio h Joural of Drivaiv 5(4) (998) [4] K. R. Milr ad. A. Pro Priig ra of rur guara i a Hah- Jarro-Moro framor Iura: Mahmai ad Eoomi 5(3) (999)

24 36 WERNER HÜRLIMANN [43] C. Mu Fiaial A Priig hor Oford Uivri Pr Oford 3. [44]. Ngihi Wlfar oomi ad i of a quilirium for a ompiiv oom Mrooomria (96) [45] J. Pima ad M. Yor Ifiil diviil la aoiad ih hproli fuio Caadia Joural of Mahmai 55() (3) [46].. Rahv Y.. Kim M. L. Biahi ad F. J. Faozzi Fiaial Modl ih Lév Pro ad Volaili Clurig J. Wil & o I. Hoo N Jr. [47].. Rahv ad. Mii al Paria Modl i Fia J. Wil & o NY. [48]. A. Ro A impl approah o h valuaio of ri ram J. of Bui 5 (978) [49] W. hou ad J. L. ugl Lév pro polomial ad marigal Commuiaio i aii-ohai Modl 4 (998) [5] A. p ad E. a d ormal varia-ma modl for a priig ad h mhod of mom Iraioal aiial Rvi 74() (6) 9-6. [5]. M. urull ad L. M. Wama A qui algorihm for priig Europa avrag opio h Joural of Fiaial ad Quaiaiv Aali 6(3) (99) [5] A. Varamaa ad C. Aladr Clod form approimaio for prad opio Applid Mahmaial Fia ifir () -6. [53] Y.-C. Wu.-L. Liao ad.-d. hu Clod-form valuaio of a opio uig a mulivaria ormal ivr auia modl Iura: Mah. Eoom. 44() (9) 95-. [54] M. V. Wührih H. Bühlma ad H. Furrr Mar-Coi Auarial Valuaio EAA Lur No Vol. (d Rvid ad Elargd Ediio) prigr- Vrlag. [55] M. V. Wührih ad M. Mrz Fiaial Modlig Auarial Valuaio ad olv i Iura prigr Fia 3. Appdi: Igral Idii of Normal p ad Proailii Ψ -Fuio Rpraio g h ruial idii ud i h drivaio of horm 4. ar ad ad provd paral.

25 NORMAL VARIANCE-MEAN MIURE (III) 37 Lmma A. For a ral umr µ ad σ > o ha h idi σ µ σ ( ) µ ϕ µ σ d Φ σ. σ (A.) Proof. Coidr fir h a µ σ. From h rlaio ϕ ϕ( ) o g ϕ d ϕ() d Φ ( ). Uig hi o oai a hag of varial σ ϕ( ( µ ) σ) d µ σ ϕ ( µ ) σ µ σ () µ d Φ σ. σ Lmma A. For a ral umr a µ ad σ > o ha h idi a σ ( ) a µ Φ a ϕ µ σ d Φ. (A.) σ Proof. Coidr h fuio F ( z) Φ( z ) ϕ d ( z) Φ( a z) ϕ d. ad F ( z) ϕ( z ) ϕ d ϕ( z ) a O o ha F ( ) Φ ϕ d from hih i follo ha a F ( a) F F ( z) dz Φ. O h ohr had o ha a a z () F ( a) Φ ad a ( z) z ( a z) d ϕ ϕ ( z ) 3

26 38 WERNER HÜRLIMANN a a ϕ h a ( ) a ( z) dz. Φ I follo z ha ( ) a µ σ Φ a ϕ µ σ d µ σ Φ. σ a Proof of horm 4.. A rpad appliaio of h mhod ud i Lmma A ill do. I h pial a a J ( z) Ψ( z γ) λ Φ( z ) fa d z. O ha J J( ) J J ( z) dz. A alulaio ho ha J ( z ) ϕ( z ) f ( ) d a λ ( ) ( γ z ) d πγ( γ) B( γ ) ( z ) γ hr h la prio follo oig ha h igrad i rlad o a gamma di. Wih h hag of varial z u o ha du J( ) B( γ ) ( u ) γ. A furhr hag of varial u ( ) ho ha du γ ( u ) ( ) γ ( ) d. Wih (4.6) hi impli (4.8) for a. h formula for a < follo oig ha Φ ( ) Φ( ) Φ( ) h

27 NORMAL VARIANCE-MEAN MIURE (III) 39 J( ) J( ). I gral if a I z Φ( z ) fa( λ) ( ) d z. O ha I J( ) I( a) J( ) I ( z) dz ad I ( z) ϕ( z ) fa( λ)d a z πγ( γ) ( γ ) ( ) z d. h igrad i rlad o a gralizd ivr auia di of h form f I ( α p) p ( α ) K ( α ) p p ( α ) p γ α ( ) z. ( ) o ha I ( z) π z γ z K Γ( γ) γ γ ( z) γ α A ompario ih (4.7) ho ha I ( z) fv ( z) α α. h formula (4.8) for a follo. If a < o o ha Φ( a ) Φ( a ) Φ( a ) h Ψ( a γ) Ψ( a γ).

28 4 WERNER HÜRLIMANN Proof of horm 4.3. If a J( z) Ψ( z γ) Φ( z ) d z. O ha J( ) J ( z) dz. A alulaio ho ha f J z ϕ z f d p( {( z ) }) d. π h igrad i rlad o a harmoi la H ( α ) I( α ) ih di ( α ) f ( ) H α α z K( α ) uh ha J ( z) K( z ). π Comparig ih (4.) ho (4.) for a. h formula for a < follo from h rlaio J( ) J( ). If a h o a ha I( a) J( ) I ( z)dz ih I( z) Φ( z ) f d z. hrough alulaio o oai I ( z) ϕ( z ) f d z p( {( ) ( z ) }) d π

29 NORMAL VARIANCE-MEAN MIURE (III) 4 ho igrad i rlad o h di of a gralizd ivr auia p I α ih paramr. z p α I follo ha. z z K z I z π Comparig ih (4.) ho ha z f z I NI ad (4.) for a i ho. h formula for < a i oaid from h rlaiohip. γ Ψ γ Ψ a a

Boyce/DiPrima 9 th ed, Ch 7.9: Nonhomogeneous Linear Systems

Boyce/DiPrima 9 th ed, Ch 7.9: Nonhomogeneous Linear Systems BoDiPrima 9 h d Ch 7.9: Nohomogou Liar Sm Elmar Diffrial Equaio ad Boudar Valu Prolm 9 h diio William E. Bo ad Rihard C. DiPrima 9 Joh Wil & So I. Th gral hor of a ohomogou m of quaio g g aralll ha of

More information

Market Conditions under Frictions and without Dynamic Spanning

Market Conditions under Frictions and without Dynamic Spanning Mar Codiio udr Friio ad wihou Dyai Spaig Jui Kppo Hlii Uivriy of hology Sy Aalyi aboraory O Bo FIN-5 HU Filad hp://wwwhufi/ui/syaalyi ISBN 95--3948-5 Hlii Uivriy of hology ISSN 78-3 Sy Aalyi aboraory iblla

More information

Numerical Simulation for the 2-D Heat Equation with Derivative Boundary Conditions

Numerical Simulation for the 2-D Heat Equation with Derivative Boundary Conditions IOSR Joural of Applid Chmisr IOSR-JAC -ISSN: 78-576.Volum 9 Issu 8 Vr. I Aug. 6 PP 4-8 www.iosrjourals.org Numrical Simulaio for h - Ha Equaio wih rivaiv Boudar Codiios Ima. I. Gorial parm of Mahmaics

More information

What Is the Difference between Gamma and Gaussian Distributions?

What Is the Difference between Gamma and Gaussian Distributions? Applid Mahmaics,,, 85-89 hp://ddoiorg/6/am Publishd Oli Fbruary (hp://wwwscirporg/joural/am) Wha Is h Diffrc bw Gamma ad Gaussia Disribuios? iao-li Hu chool of Elcrical Egirig ad Compur cic, Uivrsiy of

More information

Infinite Continued Fraction (CF) representations. of the exponential integral function, Bessel functions and Lommel polynomials

Infinite Continued Fraction (CF) representations. of the exponential integral function, Bessel functions and Lommel polynomials Ifii Coiu Fraio CF rraio of h oial igral fuio l fuio a Lol olyoial Coiu Fraio CF rraio a orhogoal olyoial I hi io w rall h rlaio bw ifi rurry rlaio of olyoial orroig orhogoaliy a aroria ifii oiu fraio

More information

Practice papers A and B, produced by Edexcel in 2009, with mark schemes. Practice Paper A. 5 cosh x 2 sinh x = 11,

Practice papers A and B, produced by Edexcel in 2009, with mark schemes. Practice Paper A. 5 cosh x 2 sinh x = 11, Prai paprs A ad B, produd by Edl i 9, wih mark shms Prai Papr A. Fid h valus of for whih 5 osh sih =, givig your aswrs as aural logarihms. (Toal 6 marks) k. A = k, whr k is a ral osa. 9 (a) Fid valus of

More information

BMM3553 Mechanical Vibrations

BMM3553 Mechanical Vibrations BMM3553 Mhaial Vibraio Chapr 3: Damp Vibraio of Sigl Dgr of From Sym (Par ) by Ch Ku Ey Nizwa Bi Ch Ku Hui Fauly of Mhaial Egirig mail: y@ump.u.my Chapr Dripio Ep Ouom Su will b abl o: Drmi h aural frquy

More information

1973 AP Calculus BC: Section I

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

More information

x, x, e are not periodic. Properties of periodic function: 1. For any integer n,

x, x, e are not periodic. Properties of periodic function: 1. For any integer n, Chpr Fourir Sri, Igrl, d Tror. Fourir Sri A uio i lld priodi i hr i o poiiv ur p uh h p, p i lld priod o R i,, r priodi uio.,, r o priodi. Propri o priodi uio:. For y igr, p. I d g hv priod p, h h g lo

More information

UNIT I FOURIER SERIES T

UNIT I FOURIER SERIES T UNIT I FOURIER SERIES PROBLEM : Th urig mom T o h crkh o m gi i giv or ri o vu o h crk g dgr 6 9 5 8 T 5 897 785 599 66 Epd T i ri o i. Souio: L T = i + i + i +, Sic h ir d vu o T r rpd gc o T T i T i

More information

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

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

More information

1a.- Solution: 1a.- (5 points) Plot ONLY three full periods of the square wave MUST include the principal region.

1a.- Solution: 1a.- (5 points) Plot ONLY three full periods of the square wave MUST include the principal region. INEL495 SIGNALS AND SYSEMS FINAL EXAM: Ma 9, 8 Pro. Doigo Rodrígz SOLUIONS Probl O: Copl Epoial Forir Sri A priodi ri ar wav l ad a daal priod al o o od. i providd wi a a 5% d a.- 5 poi: Plo r ll priod

More information

15. Numerical Methods

15. Numerical Methods S K Modal' 5. Numrical Mhod. Th quaio + 4 4 i o b olvd uig h Nwo-Rapho mhod. If i ak a h iiial approimaio of h oluio, h h approimaio uig hi mhod will b [EC: GATE-7].(a (a (b 4 Nwo-Rapho iraio chm i f(

More information

Trigonometric Formula

Trigonometric Formula MhScop g of 9 FORMULAE SHEET If h lik blow r o-fucioig ihr Sv hi fil o your hrd driv (o h rm lf of h br bov hi pg for viwig off li or ju coll dow h pg. [] Trigoomry formul. [] Tbl of uful rigoomric vlu.

More information

It is quickly verified that the dynamic response of this system is entirely governed by τ or equivalently the pole s = 1.

It is quickly verified that the dynamic response of this system is entirely governed by τ or equivalently the pole s = 1. Tim Domai Prforma I orr o aalyz h im omai rforma of ym, w will xami h hararii of h ouu of h ym wh a ariular iu i ali Th iu w will hoo i a ui iu, ha i u ( < Th Lala raform of hi iu i U ( Thi iu i l bau

More information

2 Dirac delta function, modeling of impulse processes. 3 Sine integral function. Exponential integral function

2 Dirac delta function, modeling of impulse processes. 3 Sine integral function. Exponential integral function Chapr VII Spcial Fucios Ocobr 7, 7 479 CHAPTER VII SPECIAL FUNCTIONS Cos: Havisid sp fucio, filr fucio Dirac dla fucio, modlig of impuls procsss 3 Si igral fucio 4 Error fucio 5 Gamma fucio E Epoial igral

More information

Dark Solitons in Gravitational Wave and Pulsar Plasma Interaction

Dark Solitons in Gravitational Wave and Pulsar Plasma Interaction J Pl Fuio R SRIS ol 8 9 Dr Solio i Griiol W d Pulr Pl Irio U MOFIZ Dr of Mi d Nurl Si RC Uiri 66 Moli D- ld Rid: 4 uu 8 / d: 6 Jur 9 olir roio of riiol w rdiulr o urro i fild ird i lro-oiro ulr l i oidrd

More information

3.2. Derivation of Laplace Transforms of Simple Functions

3.2. Derivation of Laplace Transforms of Simple Functions 3. aplac Tarform 3. PE TRNSFORM wid rag of girig ym ar modld mahmaically by uig diffrial quaio. I gral, h diffrial quaio of h ordr ym i wri: d y( a d d d y( dy( a a y( f( (3. d Which i alo ow a a liar

More information

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

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

More information

New Results Involving a Class of Generalized Hurwitz- Lerch Zeta Functions and Their Applications

New Results Involving a Class of Generalized Hurwitz- Lerch Zeta Functions and Their Applications Turkih Joural of Aalyi ad Nur Thory 3 Vol No 6-35 Availal oli a hp://pucipuco/a///7 Scic ad Educaio Pulihig DOI:69/a---7 Nw Rul Ivolvig a Cla of Gralid Hurwi- Lrch Za Fucio ad Thir Applicaio H M Srivaava

More information

Advanced Engineering Mathematics, K.A. Stroud, Dexter J. Booth Engineering Mathematics, H.K. Dass Higher Engineering Mathematics, Dr. B.S.

Advanced Engineering Mathematics, K.A. Stroud, Dexter J. Booth Engineering Mathematics, H.K. Dass Higher Engineering Mathematics, Dr. B.S. Rfrc: (i) (ii) (iii) Advcd Egirig Mhmic, K.A. Sroud, Dxr J. Booh Egirig Mhmic, H.K. D Highr Egirig Mhmic, Dr. B.S. Grwl Th mhod of m Thi coi of h followig xm wih h giv coribuio o h ol. () Mid-rm xm : 3%

More information

Poisson Arrival Process

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

More information

Analysis of Non-Sinusoidal Waveforms Part 2 Laplace Transform

Analysis of Non-Sinusoidal Waveforms Part 2 Laplace Transform Aalyi o No-Siuoidal Wavorm Par Laplac raorm I h arlir cio, w lar ha h Fourir Sri may b wri i complx orm a ( ) C jω whr h Fourir coici C i giv by o o jωo C ( ) d o I h ymmrical orm, h Fourir ri i wri wih

More information

Improved estimation of population variance using information on auxiliary attribute in simple random sampling. Rajesh Singh and Sachin Malik

Improved estimation of population variance using information on auxiliary attribute in simple random sampling. Rajesh Singh and Sachin Malik Imrovd imaio of oulaio variac uig iformaio o auxiliar ariu i iml radom amlig Rajh igh ad achi alik Darm of aiic, Baara Hidu Uivri Varaai-5, Idia (righa@gmail.com, achikurava999@gmail.com) Arac igh ad Kumar

More information

Note 6 Frequency Response

Note 6 Frequency Response No 6 Frqucy Rpo Dparm of Mchaical Egirig, Uivriy Of Sakachwa, 57 Campu Driv, Sakaoo, S S7N 59, Caada Dparm of Mchaical Egirig, Uivriy Of Sakachwa, 57 Campu Driv, Sakaoo, S S7N 59, Caada. alyical Exprio

More information

Fiscal Policies in a Stochastic Model with Hyperbolic Discounting

Fiscal Policies in a Stochastic Model with Hyperbolic Discounting Fisal Poliis i a Soasi Modl i Hprboli Disouig Liuag Gog * ad Hg-fu Zou ** Absra I is papr sud ffs of fisal poliis o oom i a soasi modl i prboli disouig ra i spifi assumpios o produio olog prfrs ad soasi

More information

Response of LTI Systems to Complex Exponentials

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

More information

Continous system: differential equations

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

More information

ECEN620: Network Theory Broadband Circuit Design Fall 2014

ECEN620: Network Theory Broadband Circuit Design Fall 2014 ECE60: work Thory Broadbad Circui Dig Fall 04 Lcur 6: PLL Trai Bhavior Sam Palrmo Aalog & Mixd-Sigal Cr Txa A&M Uivriy Aoucm, Agda, & Rfrc HW i du oday by 5PM PLL Trackig Rpo Pha Dcor Modl PLL Hold Rag

More information

Poisson Arrival Process

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

More information

LINEAR 2 nd ORDER DIFFERENTIAL EQUATIONS WITH CONSTANT COEFFICIENTS

LINEAR 2 nd ORDER DIFFERENTIAL EQUATIONS WITH CONSTANT COEFFICIENTS Diol Bgyoko (0) I.INTRODUCTION LINEAR d ORDER DIFFERENTIAL EQUATIONS WITH CONSTANT COEFFICIENTS I. Dfiiio All suh diffril quios s i h sdrd or oil form: y + y + y Q( x) dy d y wih y d y d dx dx whr,, d

More information

The Development of Suitable and Well-founded Numerical Methods to Solve Systems of Integro- Differential Equations,

The Development of Suitable and Well-founded Numerical Methods to Solve Systems of Integro- Differential Equations, Shiraz Uivrsiy of Tchology From h SlcdWorks of Habibolla Laifizadh Th Dvlopm of Suiabl ad Wll-foudd Numrical Mhods o Solv Sysms of Igro- Diffrial Equaios, Habibolla Laifizadh, Shiraz Uivrsiy of Tchology

More information

Naive Parameter Estimation Technique of Equity Return Models Based on Short and Long Memory Processes

Naive Parameter Estimation Technique of Equity Return Models Based on Short and Long Memory Processes 6 pp.-6 004 Naiv Paramr Eimaio Tchiqu of Equiy Rur Mol Ba o Shor a Log Mmory Proc Koichi Miyazai Abrac Th aalyi o variac of Japa quiy rur from h poi of obrvaio irval i qui fw hough i i impora i maagig

More information

Fourier Series: main points

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

More information

AE57/AC51/AT57 SIGNALS AND SYSTEMS DECEMBER 2012

AE57/AC51/AT57 SIGNALS AND SYSTEMS DECEMBER 2012 AE7/AC/A7 SIGNALS AND SYSEMS DECEMBER Q. Drmi powr d rgy of h followig igl j i ii =A co iii = Solio: i E P I I l jw l I d jw d d Powr i fii, i i powr igl ii =A cow E P I co w d / co l I I l d wd d Powr

More information

P a g e 5 1 of R e p o r t P B 4 / 0 9

P a g e 5 1 of R e p o r t P B 4 / 0 9 P a g e 5 1 of R e p o r t P B 4 / 0 9 J A R T a l s o c o n c l u d e d t h a t a l t h o u g h t h e i n t e n t o f N e l s o n s r e h a b i l i t a t i o n p l a n i s t o e n h a n c e c o n n e

More information

DIFFERENTIAL EQUATIONS MTH401

DIFFERENTIAL EQUATIONS MTH401 DIFFERENTIAL EQUATIONS MTH Virual Uivrsi of Pakisa Kowldg bod h boudaris Tabl of Cos Iroduio... Fudamals.... Elms of h Thor.... Spifi Eampls of ODE s.... Th ordr of a quaio.... Ordiar Diffrial Equaio....5

More information

1.7 Vector Calculus 2 - Integration

1.7 Vector Calculus 2 - Integration cio.7.7 cor alculus - Igraio.7. Ordiary Igrals o a cor A vcor ca b igrad i h ordiary way o roduc aohr vcor or aml 5 5 d 6.7. Li Igrals Discussd hr is h oio o a dii igral ivolvig a vcor ucio ha gras a scalar.

More information

MAT3700. Tutorial Letter 201/2/2016. Mathematics III (Engineering) Semester 2. Department of Mathematical sciences MAT3700/201/2/2016

MAT3700. Tutorial Letter 201/2/2016. Mathematics III (Engineering) Semester 2. Department of Mathematical sciences MAT3700/201/2/2016 MAT3700/0//06 Tuorial Lr 0//06 Mahmaics III (Egirig) MAT3700 Smsr Dparm of Mahmaical scics This uorial lr coais soluios ad aswrs o assigms. BARCODE CONTENTS Pag SOLUTIONS ASSIGNMENT... 3 SOLUTIONS ASSIGNMENT...

More information

82A Engineering Mathematics

82A Engineering Mathematics Class Nos 5: Sod Ordr Diffrial Eqaio No Homoos 8A Eiri Mahmais Sod Ordr Liar Diffrial Eqaios Homoos & No Homoos v q Homoos No-homoos q ar iv oios fios o h o irval I Sod Ordr Liar Diffrial Eqaios Homoos

More information

IJRET: International Journal of Research in Engineering and Technology eissn: pissn:

IJRET: International Journal of Research in Engineering and Technology eissn: pissn: IJRE: Iiol Joul o Rh i Eii d holo I: 39-63 I: 3-738 VRIE OF IME O RERUIME FOR ILE RDE MOWER EM WI DIFFERE EO FOR EXI D WO E OF DEIIO VI WO REOLD IVOLVI WO OMOE. Rvihd. iiv i oo i Mhi R Eii oll RM ROU ih

More information

Chapter 7 INTEGRAL EQUATIONS

Chapter 7 INTEGRAL EQUATIONS hapr 7 INTERAL EQUATIONS hapr 7 INTERAL EUATIONS hapr 7 Igral Eqaios 7. Normd Vcor Spacs. Eclidia vcor spac. Vcor spac o coios cios ( ). Vcor Spac L ( ) 4. ach-baowsi iqali 5. iowsi iqali 7. Liar Opraors

More information

1. Introduction and notations.

1. Introduction and notations. Alyi Ar om plii orml or q o ory mr Rol Gro Lyé olyl Roièr, r i lir ill, B 5 837 Tolo Fr Emil : rolgro@orgr W y hr q o ory mr, o ll h o ory polyomil o gi rm om orhogol or h mr Th mi rl i orml mig plii h

More information

A FAMILY OF GOODNESS-OF-FIT TESTS FOR THE CAUCHY DISTRIBUTION RODZINA TESTÓW ZGODNOŚCI Z ROZKŁADEM CAUCHY EGO

A FAMILY OF GOODNESS-OF-FIT TESTS FOR THE CAUCHY DISTRIBUTION RODZINA TESTÓW ZGODNOŚCI Z ROZKŁADEM CAUCHY EGO JAN PUDEŁKO A FAMILY OF GOODNESS-OF-FIT TESTS FO THE CAUCHY DISTIBUTION ODZINA TESTÓW ZGODNOŚCI Z OZKŁADEM CAUCHY EGO Abrac A w family of good-of-fi for h Cauchy diribuio i propod i h papr. Evry mmbr of

More information

Fourier Techniques Chapters 2 & 3, Part I

Fourier Techniques Chapters 2 & 3, Part I Fourir chiqus Chaprs & 3, Par I Dr. Yu Q. Shi Dp o Elcrical & Compur Egirig Nw Jrsy Isiu o chology Email: shi@i.du usd or h cours: , 4 h Ediio, Lahi ad Dog, Oord

More information

(1) (2) sin. nx Derivation of the Euler Formulas Preliminary Orthogonality of trigonometric system

(1) (2) sin. nx Derivation of the Euler Formulas Preliminary Orthogonality of trigonometric system orir Sri Priodi io A io i lld priodi io o priod p i p p > p: ir I boh d r io o priod p h b i lo io o priod p orir Sri Priod io o priod b rprd i rm o rioomri ri o b i I h ri ovr i i lld orir ri o hr b r

More information

( A) ( B) ( C) ( D) ( E)

( A) ( B) ( C) ( D) ( E) d Smsr Fial Exam Worksh x 5x.( NC)If f ( ) d + 7, h 4 f ( ) d is 9x + x 5 6 ( B) ( C) 0 7 ( E) divrg +. (NC) Th ifii sris ak has h parial sum S ( ) for. k Wha is h sum of h sris a? ( B) 0 ( C) ( E) divrgs

More information

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

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

More information

Web-appendix 1: macro to calculate the range of ( ρ, for which R is positive definite

Web-appendix 1: macro to calculate the range of ( ρ, for which R is positive definite Wb-basd Supplmary Marials for Sampl siz cosidraios for GEE aalyss of hr-lvl clusr radomizd rials by Sv Trsra, Big Lu, oh S. Prissr, Tho va Achrbrg, ad Gorg F. Borm Wb-appdix : macro o calcula h rag of

More information

DEFLECTIONS OF THIN PLATES: INFLUENCE OF THE SLOPE OF THE PLATE IN THE APLICATION OF LINEAR AND NONLINEAR THEORIES

DEFLECTIONS OF THIN PLATES: INFLUENCE OF THE SLOPE OF THE PLATE IN THE APLICATION OF LINEAR AND NONLINEAR THEORIES Procdigs of COBEM 5 Coprigh 5 b BCM 8h Iraioal Cogrss of Mchaical Egirig Novmbr 6-, 5, Ouro Pro, MG DEFLECIONS OF HIN PLES: INFLUENCE OF HE SLOPE OF HE PLE IN HE PLICION OF LINER ND NONLINER HEORIES C..

More information

INTERNAL MEMORANDUM No. 117 THE SEDIMENT DIGESTER. Gary Parker February, 2004

INTERNAL MEMORANDUM No. 117 THE SEDIMENT DIGESTER. Gary Parker February, 2004 T. ANTONY FALL LAORATORY UNIVERITY OF MINNEOTA INTERNAL MEMORANDUM No. 7 TE EDIMENT DIGETER Gary Parkr Fruary, 4 TE EDIMENT DIGETER INTRODUCTION Th marial low wa wri i Novmr,. I rr a am o quaify ro orv

More information

, R we have. x x. ) 1 x. R and is a positive bounded. det. International Journal of Basic & Applied Sciences IJBAS-IJENS Vol:10 No:06 11

, R we have. x x. ) 1 x. R and is a positive bounded. det. International Journal of Basic & Applied Sciences IJBAS-IJENS Vol:10 No:06 11 raioal Joral of asic & ppli Scics JS-JENS Vol: No:6 So Dirichl ors a Pso Diffrial Opraors wih Coiioall Epoial Cov cio aa. M. Kail Dpar of Mahaics; acl of Scic; Ki laziz Uivrsi Jah Sai raia Eail: fkail@ka..sa

More information

CS 688 Pattern Recognition. Linear Models for Classification

CS 688 Pattern Recognition. Linear Models for Classification //6 S 688 Pr Rcogiio Lir Modls for lssificio Ø Probbilisic griv modls Ø Probbilisic discrimiiv modls Probbilisic Griv Modls Ø W o ur o robbilisic roch o clssificio Ø W ll s ho modls ih lir dcisio boudris

More information

THE ROYAL STATISTICAL SOCIETY 2016 EXAMINATIONS SOLUTIONS GRADUATE DIPLOMA MODULE 1

THE ROYAL STATISTICAL SOCIETY 2016 EXAMINATIONS SOLUTIONS GRADUATE DIPLOMA MODULE 1 TH ROAL TATITICAL OCIT 6 AINATION OLTION GRADAT DILOA ODL T oci i providig olio o ai cadida prparig or aiaio i 7. T olio ar idd a larig aid ad old o b a "odl awr". r o olio old alwa b awar a i a ca r ar

More information

Review Topics from Chapter 3&4. Fourier Series Fourier Transform Linear Time Invariant (LTI) Systems Energy-Type Signals Power-Type Signals

Review Topics from Chapter 3&4. Fourier Series Fourier Transform Linear Time Invariant (LTI) Systems Energy-Type Signals Power-Type Signals Rviw opics from Chapr 3&4 Fourir Sris Fourir rasform Liar im Ivaria (LI) Sysms Ergy-yp Sigals Powr-yp Sigals Fourir Sris Rprsaio for Priodic Sigals Dfiiio: L h sigal () b a priodic sigal wih priod. ()

More information

I M P O R T A N T S A F E T Y I N S T R U C T I O N S W h e n u s i n g t h i s e l e c t r o n i c d e v i c e, b a s i c p r e c a u t i o n s s h o

I M P O R T A N T S A F E T Y I N S T R U C T I O N S W h e n u s i n g t h i s e l e c t r o n i c d e v i c e, b a s i c p r e c a u t i o n s s h o I M P O R T A N T S A F E T Y I N S T R U C T I O N S W h e n u s i n g t h i s e l e c t r o n i c d e v i c e, b a s i c p r e c a u t i o n s s h o u l d a l w a y s b e t a k e n, i n c l u d f o l

More information

The Exile Began. Family Journal Page. God Called Jeremiah Jeremiah 1. Preschool. below. Tell. them too. Kids. Ke Passage: Ezekiel 37:27

The Exile Began. Family Journal Page. God Called Jeremiah Jeremiah 1. Preschool. below. Tell. them too. Kids. Ke Passage: Ezekiel 37:27 Faily Jo Pag Th Exil Bg io hy u c prof b jo ou Shar ab ou job ab ar h o ay u Yo ra u ar u r a i A h ) ar par ( grp hav h y y b jo i crib blo Tll ri ir r a r gro up Allo big u r a i Rvi h b of ha u ha a

More information

Mathematical Preliminaries for Transforms, Subbands, and Wavelets

Mathematical Preliminaries for Transforms, Subbands, and Wavelets Mahmaical Prlimiaris for rasforms, Subbads, ad Wavls C.M. Liu Prcpual Sigal Procssig Lab Collg of Compur Scic Naioal Chiao-ug Uivrsiy hp://www.csi.cu.du.w/~cmliu/courss/comprssio/ Offic: EC538 (03)5731877

More information

Modeling of the CML FD noise-to-jitter conversion as an LPTV process

Modeling of the CML FD noise-to-jitter conversion as an LPTV process Modlig of h CML FD ois-o-ir covrsio as a LPV procss Marko Alksic. Rvisio hisory Vrsio Da Comms. //4 Firs vrsio mrgd wo docums. Cyclosaioary Nois ad Applicaio o CML Frqucy Dividr Jir/Phas Nois Aalysis fil

More information

EEE 304 Test 1 NAME: solutions

EEE 304 Test 1 NAME: solutions EEE 4 NAME: olio Probl : For h oio i ih rafr fio. Fi h rgio of ovrg of orrpoig o:.. a abl... a aal.. Cop h i p rpo x, aig ha i i aal. / / / / } { } {R, }; {R, } {. } {R : }, R { :. L L L L Y L Y x L Caali

More information

ANOVA- Analyisis of Variance

ANOVA- Analyisis of Variance ANOVA- Aalii of Variac CS 700 Comparig altrativ Comparig two altrativ u cofidc itrval Comparig mor tha two altrativ ANOVA Aali of Variac Comparig Mor Tha Two Altrativ Naïv approach Compar cofidc itrval

More information

EEE 304 Test 1 NAME:

EEE 304 Test 1 NAME: EEE 0 NME: For h oio i ih rafr fio 0.. Fi h rgio of ovrg of orrpoig o:.. a abl... a aal.. Cop h i p rpo x aig ha i i abl..: { 0. R }. : { 0. R } : 0. { aal / / 0. aal 0. aal { 0. R } {0 R } Probl : For

More information

Controllability and Observability of Matrix Differential Algebraic Equations

Controllability and Observability of Matrix Differential Algebraic Equations NERNAONAL JOURNAL OF CRCUS, SYSEMS AND SGNAL PROCESSNG Corollabiliy ad Obsrvabiliy of Marix Diffrial Algbrai Equaios Ya Wu Absra Corollabiliy ad obsrvabiliy of a lass of marix Diffrial Algbrai Equaio (DAEs)

More information

Boyce/DiPrima 9 th ed, Ch 7.8: Repeated Eigenvalues

Boyce/DiPrima 9 th ed, Ch 7.8: Repeated Eigenvalues Boy/DiPrima 9 h d Ch 7.8: Rpad Eignvalus Elmnary Diffrnial Equaions and Boundary Valu Problms 9 h diion by William E. Boy and Rihard C. DiPrima 9 by John Wily & Sons In. W onsidr again a homognous sysm

More information

REPETITION before the exam PART 2, Transform Methods. Laplace transforms: τ dτ. L1. Derive the formulas : L2. Find the Laplace transform F(s) if.

REPETITION before the exam PART 2, Transform Methods. Laplace transforms: τ dτ. L1. Derive the formulas : L2. Find the Laplace transform F(s) if. Tranform Mhod and Calculu of Svral Variabl H7, p Lcurr: Armin Halilovic KTH, Campu Haning E-mail: armin@dkh, wwwdkh/armin REPETITION bfor h am PART, Tranform Mhod Laplac ranform: L Driv h formula : a L[

More information

, then the old equilibrium biomass was greater than the new B e. and we want to determine how long it takes for B(t) to reach the value B e.

, then the old equilibrium biomass was greater than the new B e. and we want to determine how long it takes for B(t) to reach the value B e. SURPLUS PRODUCTION (coiud) Trasiio o a Nw Equilibrium Th followig marials ar adapd from lchr (978), o h Rcommdd Radig lis caus () approachs h w quilibrium valu asympoically, i aks a ifii amou of im o acually

More information

Jonathan Turner Exam 2-10/28/03

Jonathan Turner Exam 2-10/28/03 CS Algorihm n Progrm Prolm Exm Soluion S Soluion Jonhn Turnr Exm //. ( poin) In h Fioni hp ruur, u wn vrx u n i prn v u ing u v i v h lry lo hil in i l m hil o om ohr vrx. Suppo w hng hi, o h ing u i prorm

More information

Boyce/DiPrima/Meade 11 th ed, Ch 4.1: Higher Order Linear ODEs: General Theory

Boyce/DiPrima/Meade 11 th ed, Ch 4.1: Higher Order Linear ODEs: General Theory Bo/DiPima/Mad h d Ch.: High Od Lia ODEs: Gal Tho Elma Diffial Eqaios ad Boda Val Poblms h diio b William E. Bo Rihad C. DiPima ad Dog Mad 7 b Joh Wil & Sos I. A h od ODE has h gal fom d d P P P d d W assm

More information

Exotic Options Pricing under Stochastic Volatility

Exotic Options Pricing under Stochastic Volatility Caada Rarch Chair i Ri Maagm Worig papr 5- Jaary 5 Exoic Opio Pricig dr Sochaic olailiy Nabil AHANI Nabil ahai i a h School of Admiiraiv Sdi Yor Uivriy oroo Caada. Fiacial ppor wa providd by h Caada Rarch

More information

EXERCISE - 01 CHECK YOUR GRASP

EXERCISE - 01 CHECK YOUR GRASP DIFFERENTIAL EQUATION EXERCISE - CHECK YOUR GRASP 7. m hn D() m m, D () m m. hn givn D () m m D D D + m m m m m m + m m m m + ( m ) (m ) (m ) (m + ) m,, Hnc numbr of valus of mn will b. n ( ) + c sinc

More information

A L A BA M A L A W R E V IE W

A L A BA M A L A W R E V IE W A L A BA M A L A W R E V IE W Volume 52 Fall 2000 Number 1 B E F O R E D I S A B I L I T Y C I V I L R I G HT S : C I V I L W A R P E N S I O N S A N D TH E P O L I T I C S O F D I S A B I L I T Y I N

More information

CATAVASII LA NAȘTEREA DOMNULUI DUMNEZEU ȘI MÂNTUITORULUI NOSTRU, IISUS HRISTOS. CÂNTAREA I-A. Ήχος Πα. to os se e e na aș te e e slă ă ă vi i i i i

CATAVASII LA NAȘTEREA DOMNULUI DUMNEZEU ȘI MÂNTUITORULUI NOSTRU, IISUS HRISTOS. CÂNTAREA I-A. Ήχος Πα. to os se e e na aș te e e slă ă ă vi i i i i CATAVASII LA NAȘTEREA DOMNULUI DUMNEZEU ȘI MÂNTUITORULUI NOSTRU, IISUS HRISTOS. CÂNTAREA I-A Ήχος α H ris to os s n ș t slă ă ă vi i i i i ți'l Hris to o os di in c ru u uri, în tâm pi i n ți i'l Hris

More information

CS 541 Algorithms and Programs. Exam 2 Solutions. Jonathan Turner 11/8/01

CS 541 Algorithms and Programs. Exam 2 Solutions. Jonathan Turner 11/8/01 CS 1 Algorim nd Progrm Exm Soluion Jonn Turnr 11/8/01 B n nd oni, u ompl. 1. (10 poin). Conidr vrion of or p prolm wi mulipliiv o. In i form of prolm, lng of p i produ of dg lng, rr n um. Explin ow or

More information

Approximately Inner Two-parameter C0

Approximately Inner Two-parameter C0 urli Jourl of ic d pplid Scic, 5(9: 0-6, 0 ISSN 99-878 pproximly Ir Two-prmr C0 -group of Tor Produc of C -lgr R. zri,. Nikm, M. Hi Dprm of Mmic, Md rc, Ilmic zd Uivriy, P.O.ox 4-975, Md, Ir. rc: I i ppr,

More information

Reliability Analysis of a Bridge and Parallel Series Networks with Critical and Non- Critical Human Errors: A Block Diagram Approach.

Reliability Analysis of a Bridge and Parallel Series Networks with Critical and Non- Critical Human Errors: A Block Diagram Approach. Inrnaional Journal of Compuaional Sin and Mahmais. ISSN 97-3189 Volum 3, Numr 3 11, pp. 351-3 Inrnaional Rsarh Puliaion Hous hp://www.irphous.om Rliailiy Analysis of a Bridg and Paralll Sris Nworks wih

More information

Chapter 11 INTEGRAL EQUATIONS

Chapter 11 INTEGRAL EQUATIONS hapr INTERAL EQUATIONS hapr INTERAL EUATIONS Dcmbr 4, 8 hapr Igral Eqaios. Normd Vcor Spacs. Eclidia vcor spac. Vcor spac o coios cios ( ). Vcor Spac L ( ) 4. achy-byaowsi iqaliy 5. iowsi iqaliy. Liar

More information

Material and geometric nonlinear analysis of reinforced concrete frames. Análise não linear física e geométrica de pórticos de concreto armado

Material and geometric nonlinear analysis of reinforced concrete frames. Análise não linear física e geométrica de pórticos de concreto armado Volu 7, Nubr 5 (Oobr 4) p. 879-94 ISSN 983-495 Marial ad ori oliar aalyi of riford or fra Aáli ão liar fíia oéria d pório d oro arado E. Par Jr a vadro@uf.br G. V. Nouira a ovaviaa@ail.o M. Mirl No a arloirlo@ail.o.

More information

Part B: Transform Methods. Professor E. Ambikairajah UNSW, Australia

Part B: Transform Methods. Professor E. Ambikairajah UNSW, Australia Par B: rasform Mhods Profssor E. Ambikairaah UNSW, Ausralia Chapr : Fourir Rprsaio of Sigal. Fourir Sris. Fourir rasform.3 Ivrs Fourir rasform.4 Propris.4. Frqucy Shif.4. im Shif.4.3 Scalig.4.4 Diffriaio

More information

Option Pricing When Changes of the Underlying Asset Prices Are Restricted

Option Pricing When Changes of the Underlying Asset Prices Are Restricted Journal of Mahmaial Finan 8-33 doi:.436/jmf..4 Publishd Onlin Augus (hp://www.sirp.org/journal/jmf) Opion Priing Whn Changs of h Undrling Ass Pris Ar Rsrid Absra Gorg J. Jiang Guanzhong Pan Li Shi 3 Univrsi

More information

Detection of cracks in concrete and evaluation of freeze-thaw resistance using contrast X-ray

Detection of cracks in concrete and evaluation of freeze-thaw resistance using contrast X-ray Fraur Mhai Cor a Cor Sruur - Am Durabiliy Moiorig a Rriig Cor Sruur- B. H. Oh al. () 2 Kora Cor Iiu Soul ISBN 978-89-578-8-5 Dio rak i or a valuaio frz-ha ria uig ora X-ray M. Taka & K. Ouka Tohoku Gakui

More information

SHAPE DESIGN SENSITIVITY ANALYSIS OF CONTACT PROBLEM WITH FRICTION

SHAPE DESIGN SENSITIVITY ANALYSIS OF CONTACT PROBLEM WITH FRICTION SHAPE DESIGN SENSIIIY ANALYSIS OF ONA PROBLEM WIH FRIION 7 h AIAA/NASA/USAF/ISSMO Symposium o Mulidisipliay Aalysis ad Opimiaio K.K. hoi Nam H. Kim You H. Pak ad J.S. h fo ompu-aidd Dsi Dpam of Mhaial

More information

Right Angle Trigonometry

Right Angle Trigonometry Righ gl Trigoomry I. si Fs d Dfiiios. Righ gl gl msurig 90. Srigh gl gl msurig 80. u gl gl msurig w 0 d 90 4. omplmry gls wo gls whos sum is 90 5. Supplmry gls wo gls whos sum is 80 6. Righ rigl rigl wih

More information

Overview. Splay trees. Balanced binary search trees. Inge Li Gørtz. Self-adjusting BST (Sleator-Tarjan 1983).

Overview. Splay trees. Balanced binary search trees. Inge Li Gørtz. Self-adjusting BST (Sleator-Tarjan 1983). Ovrvw B r rh r: R-k r -3-4 r 00 Ig L Gør Amor Dm rogrmmg Nwork fow Srg mhg Srg g Comuo gomr Irouo o NP-om Rom gorhm B r rh r -3-4 r Aow,, or 3 k r o Prf Evr h from roo o f h m gh mr h E w E R E R rgr h

More information

Elementary Differential Equations and Boundary Value Problems

Elementary Differential Equations and Boundary Value Problems Elmnar Diffrnial Equaions and Boundar Valu Problms Boc. & DiPrima 9 h Ediion Chapr : Firs Ordr Diffrnial Equaions 00600 คณ ตศาสตร ว ศวกรรม สาขาว ชาว ศวกรรมคอมพ วเตอร ป การศ กษา /55 ผศ.ดร.อร ญญา ผศ.ดร.สมศ

More information

Boyce/DiPrima 9 th ed, Ch 2.1: Linear Equations; Method of Integrating Factors

Boyce/DiPrima 9 th ed, Ch 2.1: Linear Equations; Method of Integrating Factors Boc/DiPrima 9 h d, Ch.: Linar Equaions; Mhod of Ingraing Facors Elmnar Diffrnial Equaions and Boundar Valu Problms, 9 h diion, b William E. Boc and Richard C. DiPrima, 009 b John Wil & Sons, Inc. A linar

More information

Mixing time with Coupling

Mixing time with Coupling Mixig im wih Couplig Jihui Li Mig Zhg Saisics Dparm May 7 Goal Iroducio o boudig h mixig im for MCMC wih couplig ad pah couplig Prsig a simpl xampl o illusra h basic ida Noaio M is a Markov chai o fii

More information

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

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

More information

Fading Theory. Stochastic Signal Processing. Contents. Mobile Communication Channel

Fading Theory. Stochastic Signal Processing. Contents. Mobile Communication Channel Fadig Thory I may iruma i i oo omliad o dri all rlio diraio ad arig ro ha drmi h dir Muli-ah Como. ahr i i o rral o dri h roailiy ha a hal aramr aai a rai valu. rmiii v. Sohai rmiii a : y ma. Sohai a :

More information

Mathcad Lecture #4 In-class Worksheet Vectors and Matrices 1 (Basics)

Mathcad Lecture #4 In-class Worksheet Vectors and Matrices 1 (Basics) Mh Lr # In-l Workh Vor n Mri (Bi) h n o hi lr, o hol l o: r mri n or in Mh i mri prorm i mri mh oprion ol m o linr qion ing mri mh. Cring Mri Thr r rl o r mri. Th "Inr Mri" Wino (M) B K Poin Rr o

More information

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

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

More information

S.Y. B.Sc. (IT) : Sem. III. Applied Mathematics. Q.1 Attempt the following (any THREE) [15]

S.Y. B.Sc. (IT) : Sem. III. Applied Mathematics. Q.1 Attempt the following (any THREE) [15] S.Y. B.Sc. (IT) : Sm. III Applid Mahmaics Tim : ½ Hrs.] Prlim Qusion Papr Soluion [Marks : 75 Q. Amp h following (an THREE) 3 6 Q.(a) Rduc h mari o normal form and find is rank whr A 3 3 5 3 3 3 6 Ans.:

More information

MARTIN COUNTY, FLORIDA

MARTIN COUNTY, FLORIDA RA 5 OA. RFFY A A RA RVOAL R F 8+8 O 5+ 5+ 5+ ORI 55 OA. RFFY A A RA RVOAL R 8 F 5+ O 8+8 ROFIL ORIZ: = VR: = 5 ROFIL 5 5 5 5 5+ 5+ 5+ 5+ + 5+ 8+ + + + 8+ 8+ 8+ 8+ + 5+ 8+ 5+ - --A 8-K @.5 -K @.5 -K @.5

More information

Chapter4 Time Domain Analysis of Control System

Chapter4 Time Domain Analysis of Control System Chpr4 im Domi Alyi of Corol Sym Rouh biliy cririo Sdy rror ri rpo of h fir-ordr ym ri rpo of h cod-ordr ym im domi prformc pcificio h rliohip bw h prformc pcificio d ym prmr ri rpo of highr-ordr ym Dfiiio

More information

The Licking County Health Department 675 Price Rd., Newark, OH (740)

The Licking County Health Department 675 Price Rd., Newark, OH (740) T Liki y Drm 675 Pri R. Nrk O 43055 (740) 349-6535.Liki.r @iki.r A R r # W Ar Pbi Amim i Liki y : U P Sri m LD ff i ri fr fbk iify ri f Br i y fr r mmi P. Imrvm R. J b R.S. M.S. M.B.A. Liki y r mmii m

More information

176 5 t h Fl oo r. 337 P o ly me r Ma te ri al s

176 5 t h Fl oo r. 337 P o ly me r Ma te ri al s A g la di ou s F. L. 462 E l ec tr on ic D ev el op me nt A i ng er A.W.S. 371 C. A. M. A l ex an de r 236 A d mi ni st ra ti on R. H. (M rs ) A n dr ew s P. V. 326 O p ti ca l Tr an sm is si on A p ps

More information

Valley Forge Middle School Fencing Project Facilities Committee Meeting February 2016

Valley Forge Middle School Fencing Project Facilities Committee Meeting February 2016 Valley Forge iddle chool Fencing roject Facilities ommittee eeting February 2016 ummer of 2014 Installation of Fencing at all five istrict lementary chools October 2014 Facilities ommittee and

More information

Dec. 3rd Fall 2012 Dec. 31st Dec. 16th UVC International Jan 6th 2013 Dec. 22nd-Jan 6th VDP Cancun News

Dec. 3rd Fall 2012 Dec. 31st Dec. 16th UVC International Jan 6th 2013 Dec. 22nd-Jan 6th VDP Cancun News Fll 2012 C N P D V Lk Exii Aii Or Bifl Rr! Pri Dk W ri k fr r f rr. Ti iq fr ill fr r ri ir. Ii rlxi ill fl f ir rr r - i i ri r l ll! Or k i l rf fr r r i r x, ri ir i ir l. T i r r Cri r i l ill rr i

More information

OH BOY! Story. N a r r a t iv e a n d o bj e c t s th ea t e r Fo r a l l a g e s, fr o m th e a ge of 9

OH BOY! Story. N a r r a t iv e a n d o bj e c t s th ea t e r Fo r a l l a g e s, fr o m th e a ge of 9 OH BOY! O h Boy!, was or igin a lly cr eat ed in F r en ch an d was a m a jor s u cc ess on t h e Fr en ch st a ge f or young au di enc es. It h a s b een s een by ap pr ox i ma t ely 175,000 sp ect at

More information

ECE351: Signals and Systems I. Thinh Nguyen

ECE351: Signals and Systems I. Thinh Nguyen ECE35: Sigals ad Sysms I Thih Nguy FudamalsofSigalsadSysms x Fudamals of Sigals ad Sysms co. Fudamals of Sigals ad Sysms co. x x] Classificaio of sigals Classificaio of sigals co. x] x x] =xt s =x

More information

Algorithms and Data Structures 2011/12 Week 9 Solutions (Tues 15th - Fri 18th Nov)

Algorithms and Data Structures 2011/12 Week 9 Solutions (Tues 15th - Fri 18th Nov) Algorihm and Daa Srucure 2011/ Week Soluion (Tue 15h - Fri 18h No) 1. Queion: e are gien 11/16 / 15/20 8/13 0/ 1/ / 11/1 / / To queion: (a) Find a pair of ube X, Y V uch ha f(x, Y) = f(v X, Y). (b) Find

More information