Transparency and liquidity in securities markets*

Μέγεθος: px
Εμφάνιση ξεκινά από τη σελίδα:

Download "Transparency and liquidity in securities markets*"

Transcript

1 Trasarcy ad lqudty scurts marts Taash U Isttut for Motary ad Ecoomc Studs Ba of Jaa (E-mal: taashu@bojorj Abstract Ths ar rods a framwor whch dals wth arous tys of trasarcy cocr th comosto of ordr flow Us ths framwor w study th rlatosh btw trasarcy ad rc olatlty as a masur of lqudty W dr codtos udr whch cras trasarcy rducs rc olatlty dmostrat that crasd trasarcy dos ot always mly lss olatlty Th ws xrssd hr ar thos of th author Thy do ot rflct thos of th Ba of Jaa

2 Itroducto Mart trasarcy s dfd as th ablty of mart artcats to obsr th formato o th trad rocss by O Hara (995 Trasarcy has may dmsos bcaus a mart has may ds of artcats ad may tys of formato I ths ar w focus o a scal ty of trasarcy that cocrs th comosto of ordr flow scally lqudty-motatd ordr flow W should ot that t stll has multl dmsos Madhaa (996 cosdrd a mart whch all tradrs obsr th tr lqudty-motatd ordr flow It s trasart o ss Röll (99 cosdrd a mart whch bror-dalrs trad basd o rat formato rard ordr flow by thr lqudty-motatd customrs It s also trasart aothr ss It should b otd that wh w cosdr trasarcy cocr th comosto of ordr flow w must ay attto to how much of what art of ad by whom th ordr flow s obsrd Ths faturs dstctos ha rarly b thortcally dscussd th ltratur Extd th modls of Kyl (989 Röll (99 ad Madhaa (996 ths ar rods a modl whch a art of ordr flow s dsclosd to th ublc art of t s obsrd by a art of tradrs ad art of t s ot obsrd by ayo Us th modl w study th rlatosh btw trasarcy ad rc olatlty as a masur of lqudty Mor rcsly w stat th otmal ll of trasarcy that mmss rc olatlty W cosdr two tys of trasarcy O s trasarcy for ublc formato Ths cocrs th stuato whch all tradrs commoly obsr th sam ordr flow whch s smlar to that Madhaa (996 Th othr s trasarcy for rat formato Ths cocrs th stuato whch dffrt tradrs obsr dffrt ad ddt ordr flow whch s smlar to that Röll (99 Accord to th ma rsults w ow th follow I th cas of trasarcy for ublc formato wh th arac of ordr flow s lar ouh ad th mart s ot trasart cras trasarcy rducs rc olatlty Too much trasarcy howr crass rc olatlty I th cas of trasarcy for rat formato wh th arac of ordr flow s lar ouh cras trasarcy rducs rc olatlty ad th most trasart marts joy th last rc olatlty Ths ar s orasd as follows Scto troducs th modl Scto shows th xstc of lar symmtrc qulbra of th modl whch w coctrat o Scto 4 studs trasarcy for ublc formato Scto 5 studs trasarcy for rat formato Scto 6 cocluds th ar A modl I our modl a sl rsy asst s tradd a aucto mart Th lqudato alu of th asst s dotd by whch s a radom arabl ormally dstrbutd wth ma ad arac Th ralsd alu of s doatd by I th rmadr of th ar a arabl wth a tld dots a radom arabl ad that wthout a tld dots ts ralsd alu Prc olatlty susts th dr of mart trasarcy at last drctly thouh t may ot cssarly b a drct masur of mart lqudty Madhaa (996 assumd that all ordr flow s obsrd by tradrs Our modl oly allows for art of t to b obsrd W ar trstd th amout of ordr flow that mmss rc olatlty

3 Thr ar formd tradrs ach of whom s dxd by Tradr rcs a rat formato sal cocr whch s a radom arabl ε ε ε ar ddtly dtcally ad ormally dstrbutd wth ma ad arac os tradrs ordr arat dotd by s ormally dstrbutd wth ma ad arac W assum that thr ar radom arabls such that whr s ddtly ad ormally dstrbutd wth ma ad arac Thus W also assum that for s a art of whch s ublcly obsrd by ryo For for s a art of whch s oly by tradr artly obsrd s a art of whch s ot obsrd by ayo Thus t ca b trrtd that larr / ad / or smallr / mly mor trasarcy / cocrs trasarcy for ublc formato / cocrs trasarcy for rat formato Th modls of Kyl (989 Madhaa (996 ad Röll (99 ca b cosdrd as scal cass of th abo modl th follow ss Kyl (989 studd marts wth / ad / Madhaa (996 studd marts wth / ad / Röll (99 studd marts wth / / ad / W stat th otmal dr of trasarcy by cosdr trmdat cass ad coduct comarat statcs of rc olatlty wth rsct to or for Aftr rc sals ad tradr crats a dmad schdul X ( ; Th ctor of dmad schduls ar dotd by X ( X X Th mart clar rc s dtrmd by th follow quotato X ( To mhass th ddc o X w dot th mart clar rc ad th quatty tradd by tradr as ( X ad x x ( X rsctly Each formd tradr has xotal utlty wth rs arso coffct Each formd tradr has a o-stochastc tal dowmt whch s ormalsd to ro Thus th utlty fucto of tradr ca b wrtt as u ( π x( π whr π ( x ad x s th quatty tradd

4 Dfto Th Baysa-ash qulbrum of th am s a ctor of strats X such that Eu υ X x X Eu υ X X x X X ( for ay dmad schdul X ad Symmtrc lar qulbra Follow Kyl (989 w focus o a symmtrc lar qulbrum whch lar fucto of ad for W wrt th qulbrum straty ( ; X X s a dtcal X as ( Thorm Thr xsts a symmtrc lar qulbrum f > Th aramtrs ( ar dtrmd by th follow quatos: ( ϕ ( whr ( ϕ ( ( ϕ ϕ ( ( ( ϕ ( ( ϕ ( ( ( ( (4 (5 Th roof whch s basd uo th tchqu dlod by Kyl (989 s rodd th adx I th rmadr of ths ar w assum that Ths mls that formd tradrs ha a ror dstrbuto for that s last format Ths assumto smlfs our aalyss du to th follow lmma I Baysa statstcs t s oft sustd to us th last format rors

5 Lmma If th W cosdr th rlatosh btw trasarcy ad a arac of th mart clar rc codtoal o whch s by th follow lmma Lmma If th ( [( ( ] V 4 Trasarcy for ublc sals Ths scto studs th cas whr / ad / For s (almost always qual to ad coys o formato I othr words ry tradr rcs a ublc sal but has o rat formato cocr Thorm Suos s lar For > ad ( ( I addto thr xsts thr xsts uqu ( [ such that Th roof s rodd th adx ( Suos > f such that > I ths cas cras trasarcy > ad Icras mor tha ( ( rods th otmal ll of th trasarcy ot that ( f rducs rc olatlty as far as howr crass rc olatlty Thus < Suos I ths cas cras trasarcy always crass th rc olatlty 5 Trasarcy for rat sals Ths scto xams th cass whr / ad / s (almost always qual to ad coys o formato I othr words tradr ows hs rat formato but dos ot ha ay ublc formato cocr 4

6 Thorm Suos s lar For > ad ( / ( I addto thr xst thr xsts uqu ( [ ] ( < / ad < f < Th roof s rodd th adx such that / such that ( < ad f f / Suos I ths cas cras trasarcy always rducs rc olatlty Suos < < I ths cas cras trasarcy rducs rc olatlty as far as ( Icras mor tha ( rods th otmal ll of trasarcy ot that ( howr crass rc olatlty Thus < / Suos I ths cas cras trasarcy always crass rc olatlty 6 Coclud rmars Ths ar has rodd a framwor whch ca dal wth arous tys of trasarcy cocr th comosto of ordr flow Us ths framwor w study th rlatosh btw trasarcy ad rc olatlty W dr som codtos udr whch cras trasarcy rducs rc olatlty dmostrat that crasd trasarcy dos ot always mly lss rc olatlty Possbl tocs for futur rsarch would b to obta that mmss rc olatlty wthout ay rstrctos as thos our thorms ad to study th rlatosh btw ad othr masurs of mart lqudty or wlfar of tradrs 5

7 Adx Proof of Thorm Lt χ b th qulbrum straty whr χ for For th mart clar rc w wrt x χ ( ; Th x Sol for λ x (6 whr ( λ λ ( ot that x s th otmal amout of trad codtoal o ad Thus x maxmss E [ u (( x ] Howr du to (6 ( x uquly dtrms Also ( x uquly dtrms Ths mls that x s th otmal amout of trad codtoal o ad Thus x maxmss E u ( x [ u ( x ] Wh u s xotal utlty wth rs arso coffct t s ow that maxms E s qualt to maxms [( x ] V ( [ x ] E Rwrt (7 E [ ] ( x λx x V[ ] x (7 (8 Th frst ordr codto for maxmsato of (8 wth rsct to x s [ ] E λx x whr / V [ ] ( < λ Th scod ordr codto s (9 6

8 λ x ad E [ ] E[ ] [ ] λ x x Bcaus E Sol ths for x x E [ ] [ E ] λ (9 s rwrtt as ( ot that all th radom arabls o ar jotly ormal Thus s a costat ad E [ ] s lar wth rsct to o Ths mls that χ ( ; s fact a lar fucto Th xt st s to dr I ordr to do so w calculat E [ ] ad / V [ ] χ ; for assum that ( Th mart clar codto s Thus ( ( ( ( ( [( ] ( ( ( [( ] ( Lt µ b th lft had sd of ths quato ot th follow: ; µ ( s statstcally qualt to ( ( s ddt of ad of ( µ Ths mls that E [ ] E [ ] ( E [( µ ] ( µ µ s ormally dstrbutd wth ma ctor ( 7

9 8 ad coarac matrx V µ whr Ths drctly rod E ad Th rsult of calculato s: ϕ ( whr ϕ ad E µ ϕ ϕ ϕ ϕ ( Plu ( ad ( to ( w ha E x ϕ ϕ ϕ ϕ ϕ ϕ

10 9 I a symmtrc qulbrum x Thus w ha ϕ ( ϕ (4 ϕ (5 ϕ (6 Lt Du to ( ad (6 ϕ Thus f (7 ( f has a soluto bcaus ( < f ad ( > f If ( f has multl solutos lt b th larst o satsfy > f Th bcaus f f < Du to ( ad (4 f (8

11 f has a uqu soluto ( bcaus ( f < > f ad > f for ay > Du to Browr s fxd ot thorm th ma has a fxd ot Ths fxd ot rods th alus of ad whch th dtrms by (4 Du to ( ad (5 (9 Ths ad th alus of ad dtrm Proof of Lmma Plu ϕ ϕ to ( ad (4 w ca show that th rht had sd of ( ad (4 ar th sam Proof of Lmma Du to th mart clar codto ad Thus V Proof of Thorm Wh ad

12 ot that s dtrmd oly by f whch s ddt of Rwrt ths: It s strahtforward to s that > for ay W also ow that < Ths lads to / Thus th s for s th oost of th s for W aluat stad of Hr w troduc w arabls ω ad t such that ω ad t Th ths lads to th calculato rsult: B A / whr A ( [ ω t t 4 ( ] 4 4 ω ω ω t t t

13 ad B ( ( ( ( ω ( 6 ω B > bcaus ω < Thus th s for Cosdr ξ ( ω t Bcaus Α 4 ( ( t ( tω ( t ( t < ξ ξ s th sam as that of Α 4 4 ( tω ( t t ω ( ω ( t 6( ( 4( ( ( 4 t > ( t ξ ω ( t 4 ξ 4 ω 6 ( t > 4 ( < Thr xsts uqu ω ( t ( O such that ξ ( ω t > f ( ω ( t ξ ( ω t < f ω ( ω ( t Lt t ( ω Th w ow th follow: If t t ω ( t ω ( ω ( ( ω > ( ( t ω < t ( ω t or < ( ω ( > or ω ( If t ( Wh ad thus ω ξ ( ω t ( ω η( ω ω ω t th t th tas th larst alu ad t t ( ω ω ad I ths cas

14 whr η ( ω 4 ( ( ω ( 4 ω 4 ω ω Bcaus ( 4 < η ( 8( ( ( 4 > η ( ( ( > η ( ( ( ( 8 ( 4 < η thr xsts ω such that η ( ω > ad ξ( ω t ( ω > for ay ( ω η( ω < ad ξ( ω t ( ω < for ay ω ( ω Ths mls that ( ω ω ad t Proof of Thorm Wh ( ( ( ( [ ] Sol for Th ( ( ( ( ( < bcaus > ad < Ths mls that th s for bcaus s th oost of th s for / W aluat stad of

15 ot that [( ] [( ] / Ths lads to th calculato rsult: [( ] A / B whr t tω ( 4t 9 ( 4tω ω Α ω ad ( ω ( ω ( ω B B > Th s of Cosdr ξ ( ωt Α s th sam as that of Α ω ω ( ω t ( ω t t 4 tω 6 tω 4 9 4t ω 6 t ω ω ξ Bcaus ( ω t ξ t < ξ t > > thr xsts uqu ( t ( ω ω t < ξ ( ω t > f ω ( ω ( t ad ξ f ( ω ( t ot that ( ω ( t t ξ( ω ( t t ω ( t ξ( ω ( t t dξ dt Thus ω t ω t ( t ξ ( ω ( t t ξ( ω ( t t t A sml calculato shows that / ω ξ ( ω ( t t t t ω ξ < ad ( ω ( t t ω ω such that > Thus ω t ( t > 4

16 Th w ow th follow: If t t ( ω ( t If t > t ( ω ( t ad th < or < ( ω ( t t th ( ( ω ( ( ω ( < t ( ω ( ( ( ω ( / < If t > t ( ω ( t ad t ( ω ( ( ω ( th ( ot that / ( Thus W ca calculat that ω ( Thrfor ( ω ( ω ( 4 ad ( ( ω 4 Rwrt ths shows that ( ( ω 4 t ω t t Wh ( ω ω tas ts larst alu ad t t ( ω ( ω t ( ω ω ξ η ( ω( ( ω I ths cas 5

17 whr η ( ω ( 8 4 ω ( 4 ω ( 6 9 ω ( 5 6 ω 4 ( ω 5 6 η Ths mls that thr xsts A sml calculato shows that ( < η( > η( ω > ω such that η ( ω > ad ξ( ω t ( ω > for ay ω ( ω ad ( ω < ξ( ω t ( ω < for ay ω ( ω Ths mls that t ( ω 4( Wh / ω tas ts smallst alu ad t t ( ω I ths cas ( ω ω ω ( ω t ( ω η ( ω ξ whr η 4 ( ω 6 ( 8 9 ω ( 4 ω ( 5 ω ω η ad " A sml calculato shows that ( < η( > η( ω < ω such that η ( ω > ad ξ( ω t ( ω > for ay ω ( ω ad ( ω < ( ω ( ω < ω ω Ths mls that ( ω ξ t for ay η Ths mls that thr xsts t η ad 6

18 Rfrcs Kyl A (989: Iformd Sculato wth Imrfct comtto Rw of Ecoomc Studs Madhaa A (996: Scurty Prcs ad Trasarcy Joural of Facal Itrmdato O Hara M (995: Mart Mcrostructur Thory Cambrd MA: Blacwll Röll A (99: Dual-Caacty Trad ad th Qualty of th Mart Joural of Facal Itrmdato 5-4 7

Some Geometric Properties of a Class of Univalent. Functions with Negative Coefficients Defined by. Hadamard Product with Fractional Calculus I

Some Geometric Properties of a Class of Univalent. Functions with Negative Coefficients Defined by. Hadamard Product with Fractional Calculus I Itrtol Mthtcl Foru Vol 6 0 o 64 379-388 So otrc Proprts o Clss o Uvlt Fuctos wth Ntv Cocts Dd y Hdrd Product wth Frctol Clculus I Huss Jr Adul Huss Dprtt o Mthtcs d Coputr pplctos Coll o Sccs Uvrsty o

Διαβάστε περισσότερα

UNIT 13: TRIGONOMETRIC SERIES

UNIT 13: TRIGONOMETRIC SERIES UNIT : TRIGONOMETRIC SERIES UNIT STUCTURE. Larg Objctvs. Itroducto. Grgory s Srs.. Gral Thorm o Grgory s Srs. Summato of Trgoomtrc Srs.. CS Mthod.. Srs Basd o Gomtrc or Arthmtco-Gomtrc Srs.. Sum of a Srs

Διαβάστε περισσότερα

General theorems of Optical Imaging systems

General theorems of Optical Imaging systems Gnral thorms of Optcal Imagng sstms Tratonal Optcal Imagng Topcs Imagng qualt harp: mags a pont sourc to a pont Dstorton fr: mags a shap to a smlar shap tgmatc Imagng Imags a pont sourc to a nfntl sharp

Διαβάστε περισσότερα

A NEW FORM OF MULTIVARIATE GENERALIZED DOUBLE EXPONENTIAL FAMILY OF DISTRIBUTIONS OF KIND-2

A NEW FORM OF MULTIVARIATE GENERALIZED DOUBLE EXPONENTIAL FAMILY OF DISTRIBUTIONS OF KIND-2 Journal of Rlablty and Statstcal Studs; ISSN (Prnt: 0974-804, (Onln: 9-5666 Vol. 0, Issu (07: 79-0 A NEW FORM OF MULTIVARIATE GENERALIZED DOUBLE EXPONENTIAL FAMILY OF DISTRIBUTIONS OF KIND- G.S. Davd Sam

Διαβάστε περισσότερα

i i (3) Derive the fixed-point iteration algorithm and apply it to the data of Example 1.

i i (3) Derive the fixed-point iteration algorithm and apply it to the data of Example 1. Howor#3 urvval Aalyss Na: Huag Xw 黃昕蔚 Quso: uppos ha daa ( follow h odl ( ( > ad <

Διαβάστε περισσότερα

CS 1675 Introduction to Machine Learning Lecture 7. Density estimation. Milos Hauskrecht 5329 Sennott Square

CS 1675 Introduction to Machine Learning Lecture 7. Density estimation. Milos Hauskrecht 5329 Sennott Square CS 675 Itroducto to Mache Learg Lecture 7 esty estmato Mlos Hausrecht mlos@cs.tt.edu 539 Seott Square ata: esty estmato {.. } a vector of attrbute values Objectve: estmate the model of the uderlyg robablty

Διαβάστε περισσότερα

ΝΕΑ ΑΠΟΤΕΛΕΣΜΑΤΑ ΣΧΕΤΙΚΑ ΜΕ ΤΗΝ ΥΠΑΡΞΗ ΕΚΤΙΜΗΤΩΝ ΜΕΓΙΣΤΗΣ ΠΙΘΑΝΟΦΑΝΕΙΑΣ ΓΙΑ ΤΗΝ 3-ΠΑΡΑΜΕΤΡΙΚΗ ΓΑΜΜΑ ΚΑΤΑΝΟΜΗ

ΝΕΑ ΑΠΟΤΕΛΕΣΜΑΤΑ ΣΧΕΤΙΚΑ ΜΕ ΤΗΝ ΥΠΑΡΞΗ ΕΚΤΙΜΗΤΩΝ ΜΕΓΙΣΤΗΣ ΠΙΘΑΝΟΦΑΝΕΙΑΣ ΓΙΑ ΤΗΝ 3-ΠΑΡΑΜΕΤΡΙΚΗ ΓΑΜΜΑ ΚΑΤΑΝΟΜΗ Ελληνικό Στατιστικό Ινστιτούτο Πρακτικά ου Πανελληνίου Συνεδρίου Στατιστικής 008, σελ 9-98 ΝΕΑ ΑΠΟΤΕΛΕΣΜΑΤΑ ΣΧΕΤΙΚΑ ΜΕ ΤΗΝ ΥΠΑΡΞΗ ΕΚΤΙΜΗΤΩΝ ΜΕΓΙΣΤΗΣ ΠΙΘΑΝΟΦΑΝΕΙΑΣ ΓΙΑ ΤΗΝ 3-ΠΑΡΑΜΕΤΡΙΚΗ ΓΑΜΜΑ ΚΑΤΑΝΟΜΗ Γεώργιος

Διαβάστε περισσότερα

Relative Valuation. Relative Valuation. Relative Valuation. Υπολογισµός αξίας επιχείρησης µε βάση τρέχουσες αποτιµήσεις οµοειδών εταιρειών

Relative Valuation. Relative Valuation. Relative Valuation. Υπολογισµός αξίας επιχείρησης µε βάση τρέχουσες αποτιµήσεις οµοειδών εταιρειών Rlativ Valuatio Αρτίκης Γ. Παναγιώτης Rlativ Valuatio Rlativ Valuatio Υπολογισµός αξίας επιχείρησης µε βάση τρέχουσες αποτιµήσεις οµοειδών εταιρειών Ø Επιλογή οµοειδών επιχειρήσεων σε όρους κινδύνου, ανάπτυξης

Διαβάστε περισσότερα

Examples of Cost and Production Functions

Examples of Cost and Production Functions Dvso of the Humates ad Socal Sceces Examples of Cost ad Producto Fuctos KC Border October 200 v 20605::004 These otes sho ho you ca use the frst order codtos for cost mmzato to actually solve for cost

Διαβάστε περισσότερα

Homework #6. A circular cylinder of radius R rotates about the long axis with angular velocity

Homework #6. A circular cylinder of radius R rotates about the long axis with angular velocity Homwork #6 1. (Kittl 5.1) Cntrifug. A circular cylindr of radius R rotats about th long axis with angular vlocity ω. Th cylindr contains an idal gas of atoms of mass m at tmpratur. Find an xprssion for

Διαβάστε περισσότερα

One and two particle density matrices for single determinant HF wavefunctions. (1) = φ 2. )β(1) ( ) ) + β(1)β * β. (1)ρ RHF

One and two particle density matrices for single determinant HF wavefunctions. (1) = φ 2. )β(1) ( ) ) + β(1)β * β. (1)ρ RHF One and two partcle densty matrces for sngle determnant HF wavefunctons One partcle densty matrx Gven the Hartree-Fock wavefuncton ψ (,,3,!, = Âϕ (ϕ (ϕ (3!ϕ ( 3 The electronc energy s ψ H ψ = ϕ ( f ( ϕ

Διαβάστε περισσότερα

Exam Statistics 6 th September 2017 Solution

Exam Statistics 6 th September 2017 Solution Exam Statstcs 6 th September 17 Soluto Maura Mezzett Exercse 1 Let (X 1,..., X be a raom sample of... raom varables. Let f θ (x be the esty fucto. Let ˆθ be the MLE of θ, θ be the true parameter, L(θ be

Διαβάστε περισσότερα

α A G C T 國立交通大學生物資訊及系統生物研究所林勇欣老師

α A G C T 國立交通大學生物資訊及系統生物研究所林勇欣老師 A G C T Juks and Cantor s (969) on-aramtr modl A T C G A G 0 0 0-3 C T A() A( t ) ( 3 ) ( ) A() A() ( 3 ) ( ) A( A( A( A( t ) A( 3 A( t ) ( ) A( A( Juks and Cantor s (969) on-aramtr modl A( A( t ) A( d

Διαβάστε περισσότερα

1. For each of the following power series, find the interval of convergence and the radius of convergence:

1. For each of the following power series, find the interval of convergence and the radius of convergence: Math 6 Practice Problems Solutios Power Series ad Taylor Series 1. For each of the followig power series, fid the iterval of covergece ad the radius of covergece: (a ( 1 x Notice that = ( 1 +1 ( x +1.

Διαβάστε περισσότερα

Multi-dimensional Central Limit Theorem

Multi-dimensional Central Limit Theorem Mult-dmensonal Central Lmt heorem Outlne () () () t as () + () + + () () () Consder a sequence of ndependent random proceses t, t, dentcal to some ( t). Assume t 0. Defne the sum process t t t t () t ();

Διαβάστε περισσότερα

Multi-dimensional Central Limit Theorem

Multi-dimensional Central Limit Theorem Mult-dmensonal Central Lmt heorem Outlne () () () t as () + () + + () () () Consder a sequence of ndependent random proceses t, t, dentcal to some ( t). Assume t 0. Defne the sum process t t t t () t tme

Διαβάστε περισσότερα

A study on generalized absolute summability factors for a triangular matrix

A study on generalized absolute summability factors for a triangular matrix Proceedigs of the Estoia Acadey of Scieces, 20, 60, 2, 5 20 doi: 0.376/proc.20.2.06 Available olie at www.eap.ee/proceedigs A study o geeralized absolute suability factors for a triagular atrix Ere Savaş

Διαβάστε περισσότερα

Π Ο Λ Ι Τ Ι Κ Α Κ Α Ι Σ Τ Ρ Α Τ Ι Ω Τ Ι Κ Α Γ Ε Γ Ο Ν Ο Τ Α

Π Ο Λ Ι Τ Ι Κ Α Κ Α Ι Σ Τ Ρ Α Τ Ι Ω Τ Ι Κ Α Γ Ε Γ Ο Ν Ο Τ Α Α Ρ Χ Α Ι Α Ι Σ Τ Ο Ρ Ι Α Π Ο Λ Ι Τ Ι Κ Α Κ Α Ι Σ Τ Ρ Α Τ Ι Ω Τ Ι Κ Α Γ Ε Γ Ο Ν Ο Τ Α Σ η µ ε ί ω σ η : σ υ ν ά δ ε λ φ ο ι, ν α µ ο υ σ υ γ χ ω ρ ή σ ε τ ε τ ο γ ρ ή γ ο ρ ο κ α ι α τ η µ έ λ η τ ο ύ

Διαβάστε περισσότερα

EGR 544 Communication Theory

EGR 544 Communication Theory EGR 544 Commucato hory 8. Spctral charactrstcs of Dgtally Modulats Sgals Z. Alyazcoglu Elctrcal ad Computr Egrg Dpartmt Cal Poly Pomoa Spctral charactrstcs of Dgtally Modulats Sgals Spctral charactrstcs

Διαβάστε περισσότερα

r r t r r t t r t P s r t r P s r s r r rs tr t r r t s ss r P s s t r t t tr r r t t r t r r t t s r t rr t Ü rs t 3 r r r 3 rträ 3 röÿ r t

r r t r r t t r t P s r t r P s r s r r rs tr t r r t s ss r P s s t r t t tr r r t t r t r r t t s r t rr t Ü rs t 3 r r r 3 rträ 3 röÿ r t r t t r t ts r3 s r r t r r t t r t P s r t r P s r s r P s r 1 s r rs tr t r r t s ss r P s s t r t t tr r 2s s r t t r t r r t t s r t rr t Ü rs t 3 r t r 3 s3 Ü rs t 3 r r r 3 rträ 3 röÿ r t r r r rs

Διαβάστε περισσότερα

Pairs of Random Variables

Pairs of Random Variables Pairs of Random Variabls Rading: Chaptr 4. 4. Homwork: (do at last 5 out of th following problms 4..4, 4..6, 4.., 4.3.4, 4.3.5, 4.4., 4.4.4, 4.5.3, 4.6.3, 4.6.7, 4.6., 4.7.9, 4.7., 4.8.3, 4.8.7, 4.9.,

Διαβάστε περισσότερα

Πανεπιστήµιο Κρήτης - Τµήµα Επιστήµης Υπολογιστών. ΗΥ-570: Στατιστική Επεξεργασία Σήµατος. ιδάσκων : Α. Μουχτάρης. εύτερη Σειρά Ασκήσεων.

Πανεπιστήµιο Κρήτης - Τµήµα Επιστήµης Υπολογιστών. ΗΥ-570: Στατιστική Επεξεργασία Σήµατος. ιδάσκων : Α. Μουχτάρης. εύτερη Σειρά Ασκήσεων. Πανεπιστήµιο Κρήτης - Τµήµα Επιστήµης Υπολογιστών ΗΥ-570: Στατιστική Επεξεργασία Σήµατος 2015 ιδάσκων : Α. Μουχτάρης εύτερη Σειρά Ασκήσεων Λύσεις Ασκηση 1. 1. Consder the gven expresson for R 1/2 : R 1/2

Διαβάστε περισσότερα

ss rt çã r s t Pr r Pós r çã ê t çã st t t ê s 1 t s r s r s r s r q s t r r t çã r str ê t çã r t r r r t r s

ss rt çã r s t Pr r Pós r çã ê t çã st t t ê s 1 t s r s r s r s r q s t r r t çã r str ê t çã r t r r r t r s P P P P ss rt çã r s t Pr r Pós r çã ê t çã st t t ê s 1 t s r s r s r s r q s t r r t çã r str ê t çã r t r r r t r s r t r 3 2 r r r 3 t r ér t r s s r t s r s r s ér t r r t t q s t s sã s s s ér t

Διαβάστε περισσότερα

PARTIAL SUMS OF CERTAIN CLASSES OF MEROMORPHIC FUNCTIONS

PARTIAL SUMS OF CERTAIN CLASSES OF MEROMORPHIC FUNCTIONS 5 Proc Pist Acd Sci Nilh 43: A 5-6 Al-ih 006 PATIAL SUMS OF CETAIN CLASSES OF MEOMOPHIC FUNCTIONS Nilh A Al-ih Girls Collg o Eductio iydh Sudi Arbi civd Jury 006 cctd Fbrury 006 Couictd by Pro r M Iqbl

Διαβάστε περισσότερα

C.S. 430 Assignment 6, Sample Solutions

C.S. 430 Assignment 6, Sample Solutions C.S. 430 Assignment 6, Sample Solutions Paul Liu November 15, 2007 Note that these are sample solutions only; in many cases there were many acceptable answers. 1 Reynolds Problem 10.1 1.1 Normal-order

Διαβάστε περισσότερα

rs r r â t át r st tíst Ó P ã t r r r â

rs r r â t át r st tíst Ó P ã t r r r â rs r r â t át r st tíst P Ó P ã t r r r â ã t r r P Ó P r sã rs r s t à r çã rs r st tíst r q s t r r t çã r r st tíst r t r ú r s r ú r â rs r r â t át r çã rs r st tíst 1 r r 1 ss rt q çã st tr sã

Διαβάστε περισσότερα

P P Ó P. r r t r r r s 1. r r ó t t ó rr r rr r rí st s t s. Pr s t P r s rr. r t r s s s é 3 ñ

P P Ó P. r r t r r r s 1. r r ó t t ó rr r rr r rí st s t s. Pr s t P r s rr. r t r s s s é 3 ñ P P Ó P r r t r r r s 1 r r ó t t ó rr r rr r rí st s t s Pr s t P r s rr r t r s s s é 3 ñ í sé 3 ñ 3 é1 r P P Ó P str r r r t é t r r r s 1 t r P r s rr 1 1 s t r r ó s r s st rr t s r t s rr s r q s

Διαβάστε περισσότερα

Homework for 1/27 Due 2/5

Homework for 1/27 Due 2/5 Name: ID: Homework for /7 Due /5. [ 8-3] I Example D of Sectio 8.4, the pdf of the populatio distributio is + αx x f(x α) =, α, otherwise ad the method of momets estimate was foud to be ˆα = 3X (where

Διαβάστε περισσότερα

Chapter 3 Prior Information

Chapter 3 Prior Information Chatr Pror Iorato Subjtv Dtrato o th Pror Dst Svral usul aroah a b us to tr th ror st Th ar th hstogra aroah th rlatv llhoo aroah athg a gv utoal or 4 CDF trato () Th hstogra aroah Dv th aratr sa to trvals

Διαβάστε περισσότερα

Fractional Colorings and Zykov Products of graphs

Fractional Colorings and Zykov Products of graphs Fractional Colorings and Zykov Products of graphs Who? Nichole Schimanski When? July 27, 2011 Graphs A graph, G, consists of a vertex set, V (G), and an edge set, E(G). V (G) is any finite set E(G) is

Διαβάστε περισσότερα

SUPERPOSITION, MEASUREMENT, NORMALIZATION, EXPECTATION VALUES. Reading: QM course packet Ch 5 up to 5.6

SUPERPOSITION, MEASUREMENT, NORMALIZATION, EXPECTATION VALUES. Reading: QM course packet Ch 5 up to 5.6 SUPERPOSITION, MEASUREMENT, NORMALIZATION, EXPECTATION VALUES Readig: QM course packet Ch 5 up to 5. 1 ϕ (x) = E = π m( a) =1,,3,4,5 for xa (x) = πx si L L * = πx L si L.5 ϕ' -.5 z 1 (x) = L si

Διαβάστε περισσότερα

Discrete Fourier Transform { } ( ) sin( ) Discrete Sine Transformation. n, n= 0,1,2,, when the function is odd, f (x) = f ( x) L L L N N.

Discrete Fourier Transform { } ( ) sin( ) Discrete Sine Transformation. n, n= 0,1,2,, when the function is odd, f (x) = f ( x) L L L N N. Dscrete Fourer Trasform Refereces:. umercal Aalyss of Spectral Methods: Theory ad Applcatos, Davd Gottleb ad S.A. Orszag, Soc. for Idust. App. Math. 977.. umercal smulato of compressble flows wth smple

Διαβάστε περισσότερα

4.6 Autoregressive Moving Average Model ARMA(1,1)

4.6 Autoregressive Moving Average Model ARMA(1,1) 84 CHAPTER 4. STATIONARY TS MODELS 4.6 Autoregressive Moving Average Model ARMA(,) This section is an introduction to a wide class of models ARMA(p,q) which we will consider in more detail later in this

Διαβάστε περισσότερα

Chapter 1 Fundamentals in Elasticity

Chapter 1 Fundamentals in Elasticity D. of o. NU Fs s ν ss L. Pof. H L ://s.s.. D. of o. NU. Po Dfo ν Ps s - Do o - M os - o oos : o o w Uows o: - ss - - Ds W ows s o qos o so s os. w ows o fo s o oos s os of o os. W w o s s ss: - ss - -

Διαβάστε περισσότερα

Ordinal Arithmetic: Addition, Multiplication, Exponentiation and Limit

Ordinal Arithmetic: Addition, Multiplication, Exponentiation and Limit Ordinal Arithmetic: Addition, Multiplication, Exponentiation and Limit Ting Zhang Stanford May 11, 2001 Stanford, 5/11/2001 1 Outline Ordinal Classification Ordinal Addition Ordinal Multiplication Ordinal

Διαβάστε περισσότερα

Matrices and Determinants

Matrices and Determinants Matrices and Determinants SUBJECTIVE PROBLEMS: Q 1. For what value of k do the following system of equations possess a non-trivial (i.e., not all zero) solution over the set of rationals Q? x + ky + 3z

Διαβάστε περισσότερα

2 Composition. Invertible Mappings

2 Composition. Invertible Mappings Arkansas Tech University MATH 4033: Elementary Modern Algebra Dr. Marcel B. Finan Composition. Invertible Mappings In this section we discuss two procedures for creating new mappings from old ones, namely,

Διαβάστε περισσότερα

Solution Series 9. i=1 x i and i=1 x i.

Solution Series 9. i=1 x i and i=1 x i. Lecturer: Prof. Dr. Mete SONER Coordinator: Yilin WANG Solution Series 9 Q1. Let α, β >, the p.d.f. of a beta distribution with parameters α and β is { Γ(α+β) Γ(α)Γ(β) f(x α, β) xα 1 (1 x) β 1 for < x

Διαβάστε περισσότερα

The one-dimensional periodic Schrödinger equation

The one-dimensional periodic Schrödinger equation The one-dmensonal perodc Schrödnger equaon Jordan Bell jordan.bell@gmal.com Deparmen of Mahemacs, Unversy of Torono Aprl 23, 26 Translaons and convoluon For y, le τ y f(x f(x y. To say ha f : C s unformly

Διαβάστε περισσότερα

A Class of Orthohomological Triangles

A Class of Orthohomological Triangles A Class of Orthohomologcal Trangles Prof. Claudu Coandă Natonal College Carol I Craova Romana. Prof. Florentn Smarandache Unversty of New Mexco Gallup USA Prof. Ion Pătraşcu Natonal College Fraţ Buzeşt

Διαβάστε περισσότερα

On Inclusion Relation of Absolute Summability

On Inclusion Relation of Absolute Summability It. J. Cotemp. Math. Scieces, Vol. 5, 2010, o. 53, 2641-2646 O Iclusio Relatio of Absolute Summability Aradhaa Dutt Jauhari A/66 Suresh Sharma Nagar Bareilly UP) Idia-243006 aditya jauhari@rediffmail.com

Διαβάστε περισσότερα

Finite Field Problems: Solutions

Finite Field Problems: Solutions Finite Field Problems: Solutions 1. Let f = x 2 +1 Z 11 [x] and let F = Z 11 [x]/(f), a field. Let Solution: F =11 2 = 121, so F = 121 1 = 120. The possible orders are the divisors of 120. Solution: The

Διαβάστε περισσότερα

Estimators when the Correlation Coefficient. is Negative

Estimators when the Correlation Coefficient. is Negative It J Cotemp Math Sceces, Vol 5, 00, o 3, 45-50 Estmators whe the Correlato Coeffcet s Negatve Sad Al Al-Hadhram College of Appled Sceces, Nzwa, Oma abur97@ahoocouk Abstract Rato estmators for the mea of

Διαβάστε περισσότερα

Potential Dividers. 46 minutes. 46 marks. Page 1 of 11

Potential Dividers. 46 minutes. 46 marks. Page 1 of 11 Potential Dividers 46 minutes 46 marks Page 1 of 11 Q1. In the circuit shown in the figure below, the battery, of negligible internal resistance, has an emf of 30 V. The pd across the lamp is 6.0 V and

Διαβάστε περισσότερα

Nowhere-zero flows Let be a digraph, Abelian group. A Γ-circulation in is a mapping : such that, where, and : tail in X, head in

Nowhere-zero flows Let be a digraph, Abelian group. A Γ-circulation in is a mapping : such that, where, and : tail in X, head in Nowhere-zero flows Let be a digraph, Abelian group. A Γ-circulation in is a mapping : such that, where, and : tail in X, head in : tail in X, head in A nowhere-zero Γ-flow is a Γ-circulation such that

Διαβάστε περισσότερα

Section 8.3 Trigonometric Equations

Section 8.3 Trigonometric Equations 99 Section 8. Trigonometric Equations Objective 1: Solve Equations Involving One Trigonometric Function. In this section and the next, we will exple how to solving equations involving trigonometric functions.

Διαβάστε περισσότερα

Uniform Convergence of Fourier Series Michael Taylor

Uniform Convergence of Fourier Series Michael Taylor Uniform Convergence of Fourier Series Michael Taylor Given f L 1 T 1 ), we consider the partial sums of the Fourier series of f: N 1) S N fθ) = ˆfk)e ikθ. k= N A calculation gives the Dirichlet formula

Διαβάστε περισσότερα

Α Ρ Ι Θ Μ Ο Σ : 6.913

Α Ρ Ι Θ Μ Ο Σ : 6.913 Α Ρ Ι Θ Μ Ο Σ : 6.913 ΠΡΑΞΗ ΚΑΤΑΘΕΣΗΣ ΟΡΩΝ ΔΙΑΓΩΝΙΣΜΟΥ Σ τ η ν Π ά τ ρ α σ ή μ ε ρ α σ τ ι ς δ ε κ α τ έ σ σ ε ρ ι ς ( 1 4 ) τ ο υ μ ή ν α Ο κ τ ω β ρ ί ο υ, η μ έ ρ α Τ ε τ ά ρ τ η, τ ο υ έ τ ο υ ς δ

Διαβάστε περισσότερα

5 Haar, R. Haar,. Antonads 994, Dogaru & Carn Kerkyacharan & Pcard 996. : Haar. Haar, y r x f rt xβ r + ε r x β r + mr k β r k ψ kx + ε r x, r,.. x [,

5 Haar, R. Haar,. Antonads 994, Dogaru & Carn Kerkyacharan & Pcard 996. : Haar. Haar, y r x f rt xβ r + ε r x β r + mr k β r k ψ kx + ε r x, r,.. x [, 4 Chnese Journal of Appled Probablty and Statstcs Vol.6 No. Apr. Haar,, 6,, 34 E-,,, 34 Haar.., D-, A- Q-,. :, Haar,. : O.6..,..,.. Herzberg & Traves 994, Oyet & Wens, Oyet Tan & Herzberg 6, 7. Haar Haar.,

Διαβάστε περισσότερα

MATH 38061/MATH48061/MATH68061: MULTIVARIATE STATISTICS Solutions to Problems on Matrix Algebra

MATH 38061/MATH48061/MATH68061: MULTIVARIATE STATISTICS Solutions to Problems on Matrix Algebra MATH 38061/MATH48061/MATH68061: MULTIVARIATE STATISTICS Solutios to Poblems o Matix Algeba 1 Let A be a squae diagoal matix takig the fom a 11 0 0 0 a 22 0 A 0 0 a pp The ad So, log det A t log A t log

Διαβάστε περισσότερα

( y) Partial Differential Equations

( y) Partial Differential Equations Partial Dierential Equations Linear P.D.Es. contains no owers roducts o the deendent variables / an o its derivatives can occasionall be solved. Consider eamle ( ) a (sometimes written as a ) we can integrate

Διαβάστε περισσότερα

ΗΥ537: Έλεγχος Πόρων και Επίδοση σε Ευρυζωνικά Δίκτυα,

ΗΥ537: Έλεγχος Πόρων και Επίδοση σε Ευρυζωνικά Δίκτυα, ΗΥ537: Έλεγχος Πόρων και Επίδοση σε Ευρυζωνικά Δίκτυα Βασίλειος Σύρης Τμήμα Επιστήμης Υπολογιστών Πανεπιστήμιο Κρήτης Εαρινό εξάμηνο 2008 Economcs Contents The contet The basc model user utlty, rces and

Διαβάστε περισσότερα

Every set of first-order formulas is equivalent to an independent set

Every set of first-order formulas is equivalent to an independent set Every set of first-order formulas is equivalent to an independent set May 6, 2008 Abstract A set of first-order formulas, whatever the cardinality of the set of symbols, is equivalent to an independent

Διαβάστε περισσότερα

derivation of the Laplacian from rectangular to spherical coordinates

derivation of the Laplacian from rectangular to spherical coordinates derivation of the Laplacian from rectangular to spherical coordinates swapnizzle 03-03- :5:43 We begin by recognizing the familiar conversion from rectangular to spherical coordinates (note that φ is used

Διαβάστε περισσότερα

Fourier Series. MATH 211, Calculus II. J. Robert Buchanan. Spring Department of Mathematics

Fourier Series. MATH 211, Calculus II. J. Robert Buchanan. Spring Department of Mathematics Fourier Series MATH 211, Calculus II J. Robert Buchanan Department of Mathematics Spring 2018 Introduction Not all functions can be represented by Taylor series. f (k) (c) A Taylor series f (x) = (x c)

Διαβάστε περισσότερα

α & β spatial orbitals in

α & β spatial orbitals in The atrx Hartree-Fock equatons The most common method of solvng the Hartree-Fock equatons f the spatal btals s to expand them n terms of known functons, { χ µ } µ= consder the spn-unrestrcted case. We

Διαβάστε περισσότερα

Other Test Constructions: Likelihood Ratio & Bayes Tests

Other Test Constructions: Likelihood Ratio & Bayes Tests Other Test Constructions: Likelihood Ratio & Bayes Tests Side-Note: So far we have seen a few approaches for creating tests such as Neyman-Pearson Lemma ( most powerful tests of H 0 : θ = θ 0 vs H 1 :

Διαβάστε περισσότερα

Self and Mutual Inductances for Fundamental Harmonic in Synchronous Machine with Round Rotor (Cont.) Double Layer Lap Winding on Stator

Self and Mutual Inductances for Fundamental Harmonic in Synchronous Machine with Round Rotor (Cont.) Double Layer Lap Winding on Stator Sel nd Mutul Inductnces or Fundmentl Hrmonc n Synchronous Mchne wth Round Rotor (Cont.) Double yer p Wndng on Sttor Round Rotor Feld Wndng (1) d xs s r n even r Dene S r s the number o rotor slots. Dene

Διαβάστε περισσότερα

Phys460.nb Solution for the t-dependent Schrodinger s equation How did we find the solution? (not required)

Phys460.nb Solution for the t-dependent Schrodinger s equation How did we find the solution? (not required) Phys460.nb 81 ψ n (t) is still the (same) eigenstate of H But for tdependent H. The answer is NO. 5.5.5. Solution for the tdependent Schrodinger s equation If we assume that at time t 0, the electron starts

Διαβάστε περισσότερα

ss rt çã r s t à rs r ç s rt s 1 ê s Pr r Pós r çã ís r t çã tít st r t

ss rt çã r s t à rs r ç s rt s 1 ê s Pr r Pós r çã ís r t çã tít st r t ss rt çã r s t à rs r ç s rt s 1 ê s Pr r Pós r çã ís r t çã tít st r t FichaCatalografica :: Fichacatalografica https://www3.dti.ufv.br/bbt/ficha/cadastrarficha/visua... Ficha catalográfica preparada

Διαβάστε περισσότερα

Errata (Includes critical corrections only for the 1 st & 2 nd reprint)

Errata (Includes critical corrections only for the 1 st & 2 nd reprint) Wedesday, May 5, 3 Erraa (Icludes criical correcios oly for he s & d repri) Advaced Egieerig Mahemaics, 7e Peer V O eil ISB: 978474 Page # Descripio 38 ie 4: chage "w v a v " "w v a v " 46 ie : chage "y

Διαβάστε περισσότερα

Variance of Trait in an Inbred Population. Variance of Trait in an Inbred Population

Variance of Trait in an Inbred Population. Variance of Trait in an Inbred Population Varance of Trat n an Inbred Populaton Varance of Trat n an Inbred Populaton Varance of Trat n an Inbred Populaton Revew of Mean Trat Value n Inbred Populatons We showed n the last lecture that for a populaton

Διαβάστε περισσότερα

Statistical Inference I Locally most powerful tests

Statistical Inference I Locally most powerful tests Statistical Inference I Locally most powerful tests Shirsendu Mukherjee Department of Statistics, Asutosh College, Kolkata, India. shirsendu st@yahoo.co.in So far we have treated the testing of one-sided

Διαβάστε περισσότερα

Original Lambda Lube-Free Roller Chain

Original Lambda Lube-Free Roller Chain ambda (ub-fr) llr Ca Orgal ambda ub-fr llr Ca ambda a rass prduvy ad savs my. du maa m. Elma prdu ama. du dwm. g lf ad lw maa ambda as us spal l-mprgad busgs prvd lubra ad prlg war lf. mb Tmpraur: 10 C

Διαβάστε περισσότερα

Qualitative Analysis of SIR Epidemic Models in two Competing Species

Qualitative Analysis of SIR Epidemic Models in two Competing Species ntrnatnal Jurnal f Basc & Appld cncs JBA-J Vl: : 87 Qualtatv Analyss f pdmc Mdls n tw Cmptng pcs Abstract Almst all mathmatcal mdls f nfctus dsass dpnd n subdvdng th ppulatn nt a st f dstnctv classs dpndnt

Διαβάστε περισσότερα

Galatia SIL Keyboard Information

Galatia SIL Keyboard Information Galatia SIL Keyboard Information Keyboard ssignments The main purpose of the keyboards is to provide a wide range of keying options, so many characters can be entered in multiple ways. If you are typing

Διαβάστε περισσότερα

ST5224: Advanced Statistical Theory II

ST5224: Advanced Statistical Theory II ST5224: Advanced Statistical Theory II 2014/2015: Semester II Tutorial 7 1. Let X be a sample from a population P and consider testing hypotheses H 0 : P = P 0 versus H 1 : P = P 1, where P j is a known

Διαβάστε περισσότερα

ECE 222b Applied Electromagnetics Notes Set 3b

ECE 222b Applied Electromagnetics Notes Set 3b C b Appl lcomancs Nos S 3b Insuco: Pof. Val Loman Dpamn of lccal an Compu nnn Unvs of Calfona San Do Rflcon an Tansmsson. Nomal ncnc T R T R Fs fn h manc fls: 3 Rflcon an Tansmsson T R T R T R T R R T

Διαβάστε περισσότερα

2. Α ν ά λ υ σ η Π ε ρ ι ο χ ή ς. 3. Α π α ι τ ή σ ε ι ς Ε ρ γ ο δ ό τ η. 4. Τ υ π ο λ ο γ ί α κ τ ι ρ ί ω ν. 5. Π ρ ό τ α σ η. 6.

2. Α ν ά λ υ σ η Π ε ρ ι ο χ ή ς. 3. Α π α ι τ ή σ ε ι ς Ε ρ γ ο δ ό τ η. 4. Τ υ π ο λ ο γ ί α κ τ ι ρ ί ω ν. 5. Π ρ ό τ α σ η. 6. Π Ε Ρ Ι Ε Χ Ο Μ Ε Ν Α 1. Ε ι σ α γ ω γ ή 2. Α ν ά λ υ σ η Π ε ρ ι ο χ ή ς 3. Α π α ι τ ή σ ε ι ς Ε ρ γ ο δ ό τ η 4. Τ υ π ο λ ο γ ί α κ τ ι ρ ί ω ν 5. Π ρ ό τ α σ η 6. Τ ο γ ρ α φ ε ί ο 1. Ε ι σ α γ ω

Διαβάστε περισσότερα

the total number of electrons passing through the lamp.

the total number of electrons passing through the lamp. 1. A 12 V 36 W lamp is lit to normal brightness using a 12 V car battery of negligible internal resistance. The lamp is switched on for one hour (3600 s). For the time of 1 hour, calculate (i) the energy

Διαβάστε περισσότερα

LAPLACE TRANSFORM TABLE

LAPLACE TRANSFORM TABLE LAPLACE TRANSFORM TABLE Th Laplac afom of am mpl fuco a gv h Tabl. Fuco U mpul U Sp U Ramp Expoal Rpad Roo S Co Polyomal Dampd Dampd co f δ u -a -a co,,... -a -a co F / / /a /a / /!/ /a a/a Thom : Shf

Διαβάστε περισσότερα

( )( ) ( )( ) 2. Chapter 3 Exercise Solutions EX3.1. Transistor biased in the saturation region

( )( ) ( )( ) 2. Chapter 3 Exercise Solutions EX3.1. Transistor biased in the saturation region Chapter 3 Exercise Solutios EX3. TN, 3, S 4.5 S 4.5 > S ( sat TN 3 Trasistor biased i the saturatio regio TN 0.8 3 0. / K K K ma (a, S 4.5 Saturatio regio: 0. 0. ma (b 3, S Nosaturatio regio: ( 0. ( 3

Διαβάστε περισσότερα

Markov Processes and Applications

Markov Processes and Applications Markov rocesses ad Applcatos Dscrete-Tme Markov Chas Cotuous-Tme Markov Chas Applcatos Queug theory erformace aalyss ΠΜΣ524: Μοντελοποίηση και Ανάλυση Απόδοσης Δικτύων (Ι. Σταυρακάκης - ΕΚΠΑ) Dscrete-Tme

Διαβάστε περισσότερα

Markov Processes and Applications

Markov Processes and Applications Markov Processes ad Alcatos Dscrete-Tme Markov Chas Cotuous-Tme Markov Chas Alcatos Queug theory Performace aalyss Dscrete-Tme Markov Chas Books - Itroducto to Stochastc Processes (Erha Clar), Cha. 5,

Διαβάστε περισσότερα

( )( ) ( ) ( )( ) ( )( ) β = Chapter 5 Exercise Problems EX α So 49 β 199 EX EX EX5.4 EX5.5. (a)

( )( ) ( ) ( )( ) ( )( ) β = Chapter 5 Exercise Problems EX α So 49 β 199 EX EX EX5.4 EX5.5. (a) hapter 5 xercise Problems X5. α β α 0.980 For α 0.980, β 49 0.980 0.995 For α 0.995, β 99 0.995 So 49 β 99 X5. O 00 O or n 3 O 40.5 β 0 X5.3 6.5 μ A 00 β ( 0)( 6.5 μa) 8 ma 5 ( 8)( 4 ) or.88 P on + 0.0065

Διαβάστε περισσότερα

Homework 8 Model Solution Section

Homework 8 Model Solution Section MATH 004 Homework Solution Homework 8 Model Solution Section 14.5 14.6. 14.5. Use the Chain Rule to find dz where z cosx + 4y), x 5t 4, y 1 t. dz dx + dy y sinx + 4y)0t + 4) sinx + 4y) 1t ) 0t + 4t ) sinx

Διαβάστε περισσότερα

P t s st t t t t2 t s st t t rt t t tt s t t ä ör tt r t r 2ö r t ts t t t t t t st t t t s r s s s t är ä t t t 2ö r t ts rt t t 2 r äärä t r s Pr r

P t s st t t t t2 t s st t t rt t t tt s t t ä ör tt r t r 2ö r t ts t t t t t t st t t t s r s s s t är ä t t t 2ö r t ts rt t t 2 r äärä t r s Pr r r s s s t t P t s st t t t t2 t s st t t rt t t tt s t t ä ör tt r t r 2ö r t ts t t t t t t st t t t s r s s s t är ä t t t 2ö r t ts rt t t 2 r äärä t r s Pr r t t s st ä r t str t st t tt2 t s s t st

Διαβάστε περισσότερα

Lecture 2. Soundness and completeness of propositional logic

Lecture 2. Soundness and completeness of propositional logic Lecture 2 Soundness and completeness of propositional logic February 9, 2004 1 Overview Review of natural deduction. Soundness and completeness. Semantics of propositional formulas. Soundness proof. Completeness

Διαβάστε περισσότερα

Last Lecture. Biostatistics Statistical Inference Lecture 19 Likelihood Ratio Test. Example of Hypothesis Testing.

Last Lecture. Biostatistics Statistical Inference Lecture 19 Likelihood Ratio Test. Example of Hypothesis Testing. Last Lecture Biostatistics 602 - Statistical Iferece Lecture 19 Likelihood Ratio Test Hyu Mi Kag March 26th, 2013 Describe the followig cocepts i your ow words Hypothesis Null Hypothesis Alterative Hypothesis

Διαβάστε περισσότερα

The Simply Typed Lambda Calculus

The Simply Typed Lambda Calculus Type Inference Instead of writing type annotations, can we use an algorithm to infer what the type annotations should be? That depends on the type system. For simple type systems the answer is yes, and

Διαβάστε περισσότερα

Homework 3 Solutions

Homework 3 Solutions Homework 3 Solutions Igor Yanovsky (Math 151A TA) Problem 1: Compute the absolute error and relative error in approximations of p by p. (Use calculator!) a) p π, p 22/7; b) p π, p 3.141. Solution: For

Διαβάστε περισσότερα

w o = R 1 p. (1) R = p =. = 1

w o = R 1 p. (1) R = p =. = 1 Πανεπιστήµιο Κρήτης - Τµήµα Επιστήµης Υπολογιστών ΗΥ-570: Στατιστική Επεξεργασία Σήµατος 205 ιδάσκων : Α. Μουχτάρης Τριτη Σειρά Ασκήσεων Λύσεις Ασκηση 3. 5.2 (a) From the Wiener-Hopf equation we have:

Διαβάστε περισσότερα

Ψηφιακή Επεξεργασία Εικόνας

Ψηφιακή Επεξεργασία Εικόνας ΠΑΝΕΠΙΣΤΗΜΙΟ ΙΩΑΝΝΙΝΩΝ ΑΝΟΙΚΤΑ ΑΚΑΔΗΜΑΪΚΑ ΜΑΘΗΜΑΤΑ Ψηφιακή Επεξεργασία Εικόνας Φιλτράρισμα στο πεδίο των συχνοτήτων Διδάσκων : Αναπληρωτής Καθηγητής Νίκου Χριστόφορος Άδειες Χρήσης Το παρόν εκπαιδευτικό

Διαβάστε περισσότερα

F19MC2 Solutions 9 Complex Analysis

F19MC2 Solutions 9 Complex Analysis F9MC Solutions 9 Complex Analysis. (i) Let f(z) = eaz +z. Then f is ifferentiable except at z = ±i an so by Cauchy s Resiue Theorem e az z = πi[res(f,i)+res(f, i)]. +z C(,) Since + has zeros of orer at

Διαβάστε περισσότερα

Outline. M/M/1 Queue (infinite buffer) M/M/1/N (finite buffer) Networks of M/M/1 Queues M/G/1 Priority Queue

Outline. M/M/1 Queue (infinite buffer) M/M/1/N (finite buffer) Networks of M/M/1 Queues M/G/1 Priority Queue Queueig Aalysis Outlie M/M/ Queue (ifiite buffer M/M//N (fiite buffer M/M// (Erlag s B forula M/M/ (Erlag s C forula Networks of M/M/ Queues M/G/ Priority Queue M/M/ M: Markovia/Meoryless Arrival process

Διαβάστε περισσότερα

Calculus and Differential Equations page 1 of 17 CALCULUS and DIFFERENTIAL EQUATIONS

Calculus and Differential Equations page 1 of 17 CALCULUS and DIFFERENTIAL EQUATIONS alculus and Diffrnial Equaions pag of 7 ALULUS and DIFFERENTIAL EQUATIONS Th following 55 qusions concrn calculus and diffrnial quaions. In his vrsion of h am, h firs choic is always h corrc on. In h acual

Διαβάστε περισσότερα

Instruction Execution Times

Instruction Execution Times 1 C Execution Times InThisAppendix... Introduction DL330 Execution Times DL330P Execution Times DL340 Execution Times C-2 Execution Times Introduction Data Registers This appendix contains several tables

Διαβάστε περισσότερα

16 Electromagnetic induction

16 Electromagnetic induction Chatr : Elctromagntic Induction Elctromagntic induction Hint to Problm for Practic., 0 d φ or dφ 0 0.0 Wb. A cm cm 7 0 m, A 0 cm 0 cm 00 0 m B 0.8 Wb/m, B. Wb/m,, dφ d BA (B.A) BA 0.8 7 0. 00 0 80 0 8

Διαβάστε περισσότερα

3.4 SUM AND DIFFERENCE FORMULAS. NOTE: cos(α+β) cos α + cos β cos(α-β) cos α -cos β

3.4 SUM AND DIFFERENCE FORMULAS. NOTE: cos(α+β) cos α + cos β cos(α-β) cos α -cos β 3.4 SUM AND DIFFERENCE FORMULAS Page Theorem cos(αβ cos α cos β -sin α cos(α-β cos α cos β sin α NOTE: cos(αβ cos α cos β cos(α-β cos α -cos β Proof of cos(α-β cos α cos β sin α Let s use a unit circle

Διαβάστε περισσότερα

Second Order RLC Filters

Second Order RLC Filters ECEN 60 Circuits/Electronics Spring 007-0-07 P. Mathys Second Order RLC Filters RLC Lowpass Filter A passive RLC lowpass filter (LPF) circuit is shown in the following schematic. R L C v O (t) Using phasor

Διαβάστε περισσότερα

INTEGRATION OF THE NORMAL DISTRIBUTION CURVE

INTEGRATION OF THE NORMAL DISTRIBUTION CURVE INTEGRATION OF THE NORMAL DISTRIBUTION CURVE By Tom Irvie Email: tomirvie@aol.com March 3, 999 Itroductio May processes have a ormal probability distributio. Broadbad radom vibratio is a example. The purpose

Διαβάστε περισσότερα

ΠΤΥΧΙΑΚΗ ΕΡΓΑΣΙΑ ΒΑΛΕΝΤΙΝΑ ΠΑΠΑΔΟΠΟΥΛΟΥ Α.Μ.: 09/061. Υπεύθυνος Καθηγητής: Σάββας Μακρίδης

ΠΤΥΧΙΑΚΗ ΕΡΓΑΣΙΑ ΒΑΛΕΝΤΙΝΑ ΠΑΠΑΔΟΠΟΥΛΟΥ Α.Μ.: 09/061. Υπεύθυνος Καθηγητής: Σάββας Μακρίδης Α.Τ.Ε.Ι. ΙΟΝΙΩΝ ΝΗΣΩΝ ΠΑΡΑΡΤΗΜΑ ΑΡΓΟΣΤΟΛΙΟΥ ΤΜΗΜΑ ΔΗΜΟΣΙΩΝ ΣΧΕΣΕΩΝ ΚΑΙ ΕΠΙΚΟΙΝΩΝΙΑΣ ΠΤΥΧΙΑΚΗ ΕΡΓΑΣΙΑ «Η διαμόρφωση επικοινωνιακής στρατηγικής (και των τακτικών ενεργειών) για την ενδυνάμωση της εταιρικής

Διαβάστε περισσότερα

2. Let H 1 and H 2 be Hilbert spaces and let T : H 1 H 2 be a bounded linear operator. Prove that [T (H 1 )] = N (T ). (6p)

2. Let H 1 and H 2 be Hilbert spaces and let T : H 1 H 2 be a bounded linear operator. Prove that [T (H 1 )] = N (T ). (6p) Uppsala Universitet Matematiska Institutionen Andreas Strömbergsson Prov i matematik Funktionalanalys Kurs: F3B, F4Sy, NVP 2005-03-08 Skrivtid: 9 14 Tillåtna hjälpmedel: Manuella skrivdon, Kreyszigs bok

Διαβάστε περισσότερα

Econ 2110: Fall 2008 Suggested Solutions to Problem Set 8 questions or comments to Dan Fetter 1

Econ 2110: Fall 2008 Suggested Solutions to Problem Set 8  questions or comments to Dan Fetter 1 Eon : Fall 8 Suggested Solutions to Problem Set 8 Email questions or omments to Dan Fetter Problem. Let X be a salar with density f(x, θ) (θx + θ) [ x ] with θ. (a) Find the most powerful level α test

Διαβάστε περισσότερα

EE 570: Location and Navigation

EE 570: Location and Navigation EE 570: Locatio ad Navigatio INS Iitializatio Aly El-Osery Electrical Egieerig Departmet, New Mexico Tech Socorro, New Mexico, USA April 25, 2013 Aly El-Osery (NMT) EE 570: Locatio ad Navigatio April 25,

Διαβάστε περισσότερα

TMA4115 Matematikk 3

TMA4115 Matematikk 3 TMA4115 Matematikk 3 Andrew Stacey Norges Teknisk-Naturvitenskapelige Universitet Trondheim Spring 2010 Lecture 12: Mathematics Marvellous Matrices Andrew Stacey Norges Teknisk-Naturvitenskapelige Universitet

Διαβάστε περισσότερα

Η ΕΕ εγκρίνει νέο πρόγραµµα για ασφαλέστερη χρήση του Ίντερνετ και διαθέτει 55 εκατ. ευρώ ώστε να καταστεί ασφαλές για τα παιδιά

Η ΕΕ εγκρίνει νέο πρόγραµµα για ασφαλέστερη χρήση του Ίντερνετ και διαθέτει 55 εκατ. ευρώ ώστε να καταστεί ασφαλές για τα παιδιά IP/8/899 Βρυξέλλες, 9 εκεµβρίου 8 Η ΕΕ εγκρίνει νέο πρόγραµµα για ασφαλέστερη χρήση του Ίντερνετ και διαθέτει εκατ. ευρώ ώστε να καταστεί ασφαλές για τα παιδιά Από την η Ιανουαρίου 9 η ΕΕ θα διαθέτει ένα

Διαβάστε περισσότερα

CYLINDRICAL & SPHERICAL COORDINATES

CYLINDRICAL & SPHERICAL COORDINATES CYLINDRICAL & SPHERICAL COORDINATES Here we eamine two of the more popular alternative -dimensional coordinate sstems to the rectangular coordinate sstem. First recall the basis of the Rectangular Coordinate

Διαβάστε περισσότερα

Inverse trigonometric functions & General Solution of Trigonometric Equations. ------------------ ----------------------------- -----------------

Inverse trigonometric functions & General Solution of Trigonometric Equations. ------------------ ----------------------------- ----------------- Inverse trigonometric functions & General Solution of Trigonometric Equations. 1. Sin ( ) = a) b) c) d) Ans b. Solution : Method 1. Ans a: 17 > 1 a) is rejected. w.k.t Sin ( sin ) = d is rejected. If sin

Διαβάστε περισσότερα