Pairs of Random Variables
|
|
- Μελέτη Γεωργιάδης
- 6 χρόνια πριν
- Προβολές:
Transcript
1 Pairs of Random Variabls Rading: Chaptr 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., 4.9.3, 4.9., 4..3, 4..8, 4.., 4.., Joint Cumulativ Distri. Function For r.v. s X and Y, th joint CDF is F X,Y (,y = P[X, Y y] Proprtis of joint CDF F X,Y (,y F X,Y (, = F X (, F X,Y (,y = F Y (y F X,Y (,- = F X,Y (-, = F X,Y (, = F X,Y (,y F X,Y (,y if and y y G. Qu ENEE 34 Enginring Probability
2 Joint Probability Mass Function For two discrt r.v. s X and Y, th joint PMF is P X,Y (,y = P[X=, Y=y] Eampl 4.*: Flip twic a biasd coin, whr had coms out with.9. X: th numbr of hads; Y: th numbr of hads bfor th first tail. Draw th tr diagram. Find th joint PMF in function form, points in th X-Y plan, 3 matri form. G. Qu ENEE 34 Enginring Probability 3 Evnt Probability For two discrt r.v. s X and Y and any st B in th X-Y plan, vnt {(,y S X,y S y, (,y B} happns with P[B] = (,y B P X,Y (,y Eampls: G. Qu ENEE 34 Enginring Probability 4
3 Marginal Prob. Mass Function For two discrt r.v. s X and Y, thir PMFs P X ( and P Y (y ar also calld marginal PMFs: P X (= y Sy P X,Y (,y, P Y (y= S P X,Y (,y for any S X, vnt {X= }={(,y =,y S y } Eampl 4.3: P X,Y (,y X= X= X= P Y (y Y=..9. Y=.9.9 Y= G. Qu ENEE 34 Enginring Probability 5 P X (. Evnt Probability and Joint PDF For two continuous r.v. s X and Y and any st A in th X-Y plan, vnt {(,y S X,y S y, (,y A} happns with P[A] = A f X,Y (,yddy f X,Y (,y=( / yf X,Y (,y is th joint PDF. f X,Y (,y y X, Y (, y = f X, Y F ( u, v dvdu P[ <X, y <Y y ] = F X,Y (,y - F X,Y (,y - F X,Y (,y + F X,Y (,y G. Qu ENEE 34 Enginring Probability 6 3
4 Eampls Random variabls X and Y hav joint PDF f X,Y (,y = c if y and o.w. (E. 4.4* What is c? (E. 4.5 (E. 4.6* What is joint CDF? What is P[A] = P[ X+Y ]? G. Qu ENEE 34 Enginring Probability 7 Marginal Prob. Dnsity Function f For two continuous r.v. s X and Y with joint PDF f X,Y (,y, th marginal PDFs f X ( and f Y (y ar th PDFs of X and Y, and w hav X ( f X, Y (, y dy fy ( y = f X, Y (, y = d Eampl 4.7: Find th marginal PDFs for th following joint PDF of X and Y: f X, Y 5y / 4 (, y =, othrwis y G. Qu ENEE 34 Enginring Probability 8 4
5 Functions of Two Discrt R.V. s For two discrt r.v. s X and Y, th random variabl W=g(X,Y has PMF P W (w= g(,y=w P X,Y (,y Eampl (Problm 4.6.4: Joint PMF: P X,Y (,y =. for,y, and othrwis. W=min(X,Y P W (w=? G. Qu ENEE 34 Enginring Probability 9 Functions of Two Continuous R.V. s For two continuous r.v. s X and Y, th random variabl W=g(X,Y has CDF F W (w=p[w w] = g(,y w f X,Y (,yddy Eampl 4.9: Joint PDF: f X,Y (,y = /5 for 5, y 3 and othrwis. W=ma(X,Y f W (w=? G. Qu ENEE 34 Enginring Probability 5
6 Epctd Valus of Drivd R.V. s For two r.v. s X and Y, th drivd random variabl W=g(X,Y has pctd valu: E[W] = g(,yp X,Y (,y E[W] = g(,yf X,Y (,yddy E[X+Y] = E[X] +E[Y] Eampl: X and Y discrt X and Y continuous Joint PDF: f X,Y (,y = /5 for 5, y 3 and othrwis. W=ma(X,Y E[W] = ma(,yf X,Y (,yddy = 4/5 E[W] = wf W (wdw = 4/5 G. Qu ENEE 34 Enginring Probability Covarianc of Two R.V. s Covarianc: Cov[X,Y] = E[(X- X (Y- Y ] Also known as σ XY X and Y ar uncorrlatd if Cov[X,Y]=. Corrlation Cofficint: X,Y =Cov[X,Y]/σ X σ Y Whn Y=X, Cov[X,Y]=Var[X], X,Y =. If Y=aX+b, X,Y =- if a<; if a=; if a>. Thorm 4.7: - X,Y G. Qu ENEE 34 Enginring Probability 6
7 Covarianc and Corrlation Corrlation: r X,Y =E[XY] X and Y ar orthogonal if r X,Y =. Whn Y=X, r X,Y =E[X ], th nd momnt Proprtis: Cov[X,Y] = r X,Y - X Y If X and Y ar orthogonal, Cov[X,Y]=- X Y Var[X+Y]=Var[X]+Var[Y]+Cov[X,Y] G. Qu ENEE 34 Enginring Probability 3 Conditioning on an Evnt: Discrt PMF: P X ( = P[X=] Conditional PMF: P X B ( = P[X= B] P X B ( = P X (/P[B] for B; othrwis. E[X B]= P X B ( E[W B]= g( P X B ( for W=g(X Joint PMF: P X,Y (,y = P[X=,Y=y] Conditional joint PMF: P X,Y B (,y = P[(X=,Y=y B] P X,Y B (,y = P X,Y (,y/p[b] for (,y B; othrwis. E[W B]= g(,y P X,Y B (,y for W=g(X,Y G. Qu ENEE 34 Enginring Probability 4 7
8 Conditioning on Evnt {Y=y} Joint PMF: P X,Y (,y = P[X=,Y=y] Conditional joint PMF: P X,Y B (,y = P[(X=,Y=y B] P X,Y B (,y = P X,Y (,y/p[b] for (,y B; othrwis. E[W B]= g(,y P X,Y B (,y for W=g(X,Y Whn B={Y=y} P[(X=,Y=y B] = P[X= B] = P[X= Y=y] P X Y ( y P X,Y (,y = P X Y ( yp Y (y = P Y X (y P X ( P[X= Y=y]=P[X=,Y=y]/P[Y=y] E[g(X,Y Y=y]= g(,y P X Y ( y G. Qu ENEE 34 Enginring Probability 5 Eampls R.V. s X and Y hav joint PMF P X,Y (,y=.5/ for Y X 4; and othrwis. B: X+Y 4. W=X+Y. (E. 4.3 find P X,Y B (,y (E. 4.5 find Var[W B] (E. 4.7 find P Y X (y (E. 4.8 find E[Y X=] G. Qu ENEE 34 Enginring Probability 6 8
9 Conditioning on an Evnt: Continuous CDF: F X ( = P[X ] PDF: f X ( = (d/d F X ( Conditional PDF: f X B ( = f X (/P[B] for B; othrwis. E[X B] = f X B (d E[W B] = g( f X B (d for W=g(X Joint PDF: f X,Y (,y =( / y F X,Y (,y Conditional joint PDF: f X,Y B (,y = f X,Y (,y/p[b] for (,y B with P[B]>; othrwis. E[W B]= g(,y f X,Y B (,yddy for W=g(X,Y G. Qu ENEE 34 Enginring Probability 7 Conditioning on Evnt {Y=y} Joint PDF: f X,Y (,y =( / y F X,Y (,y Conditional joint PDF: f X,Y B (,y = f X,Y (,y/p[b] for (,y B with P[B]>; othrwis. E[W B]= g(,y f X,Y B (,yddy Whn B={Y=y} for W=g(X,Y P[B] = P[Y=y] =, so cannot us th abov formula f X Y ( y f X,Y (,y/f Y (y, f X Y ( y f X,Y (,y/f X ( for f Y (y > and f X ( >. E[g(X,Y Y=y]= g(,y f X Y ( yd G. Qu ENEE 34 Enginring Probability 8 9
10 Eampl: Problm 4.9. Random variabls X and Y hav joint PDF f X,Y (,y =.5 if - y and o.w. What is marginal PDF f Y (y? f Y (y = f X,Y (,yd = (y+/ What is conditional PDF f X Y ( y? f X Y ( y=f X,Y (,y/f Y (y=/(y+ What is E[X Y=y]? E[X Y=y]= f X Y ( yd = (y-/ if - y if - y G. Qu ENEE 34 Enginring Probability 9 Conditional Varianc and Eampl For drivd r.v. W=g(X,Y and vnt B with P[B] >, Var[W B] = E[(W- W B B] = E[W B]- ( W B Eampls: Joint PDF: f X,Y (,y = /5 for 5, y 3 and othrwis. W=XY. B: X+Y 4. (E. 4.4 find f X,Y B (,y (E. 4.6 find E[W B] and Var[W B] G. Qu ENEE 34 Enginring Probability
11 Itratd Epctation E[X Y=y] = f X Y ( yd is a function of Y, dnot it as E[X Y]. In Problm 4.9., E[X Y](y = E[X Y=y] = (y-/ Itratd pctation: E[E[X Y]]=E[X] Proof: E[E[X Y]] = E[X Y=y] f Y (y dy = ( f X Y ( yd f Y (ydy = f X Y ( y f Y (y d dy = ( f X Y ( y f Y (y dy d = f X (d In gnral, E[E[g(X Y]]=E[g(X] G. Qu ENEE 34 Enginring Probability Indpndnt Random Variabls Rcall: two vnts ar indpndnt iff P[AB] = P[A]P[B]. Also P[A B]=P[A] Two r.v. s X and Y ar indpndnt iff P X,Y (,y = P X (P Y (y f X,Y (,y = f X (f Y (y discrt Whn X and Y ar indpndnt, continuous P X Y ( y= P X,Y (,y/p Y (y=p X (, P Y X (y = P Y (y f X Y ( y= f X,Y (,y/f Y (y=f X (, f Y X (y = f Y (y G. Qu ENEE 34 Enginring Probability
12 Proprtis of Indpndnt R.V. s E[g(Xh(Y] = E[g(X]E[h(Y] r X,Y =E[XY]=E[X]E[Y] Cov[X.Y] = X,Y = indpndnt uncorrlatd but not vic vrsa (s. 4.5 Var[X+Y] = Var[] + Var[Y] E[X Y=y] = E[X] for all y S Y E[Y X=] = E[Y] for all X S G. Qu ENEE 34 Enginring Probability 3 Quiz 4. R.V. s X and Y hav th following joint PMF, ar thy indpndnt? P X,Y (,y X= X= X= Y=..9 Y=.9 Y=.8 P X (..8.8 P Y (y..9.8 R.V. s X and Y ar indpndnt and idntically distributd with PDF as follows. What is th joint PDF? f X ( = What is th CDF of Z=ma(X,Y? othrwis G. Qu ENEE 34 Enginring Probability 4
13 3 G. Qu ENEE 34 Enginring Probability 5 Bivariat Gaussian R.V. s Rcall Gaussian (,σ X: E[X]=, Var[X]=σ Bivariat Gaussian Random Variabls X and Y hav PDF f X,Y (,y with 5 paramtrs,, σ >, σ > and -<< dfind as: ( ( σ πσ = X f ( ( ( ( ( ( ( σ σ σ πσ σ πσ y y G. Qu ENEE 34 Enginring Probability 6 Bivariat Gaussian PDF: impact of = : circular symmtric < : ridg ovr lin =-y > : ridg ovr lin =y : masurs th pak of th ridg at (=, y= ( ( ( ( ( ( ( σ σ σ πσ σ πσ y y
14 Bivariat Gaussian PDF is a PDF πσ ( σ ( f X,Y (,y f X,Y (,y= πσ ( y σ ( Dfin: ( = +σ (- /σ, σ= σ (- / ( ( y σ σ ( f X,Y (,y can b rwrittn as th product of PDFs of Gaussian(,σ and Gaussian((,σ: ( ( y ( σ σ πσ πσ G. Qu ENEE 34 Enginring Probability 7 R.V. s X and Y in Bivariat Gaussian For bivariat Gaussian R.V. s X and Y, X is Gaussian (,σ Y is Gaussian (,σ. X,Y = Y X is Gaussian (,σ. X Y is also Gaussian. X,Y = iff X and Y ar indpndnt. G. Qu ENEE 34 Enginring Probability 8 4
Solutions to Exercise Sheet 5
Solutions to Eercise Sheet 5 jacques@ucsd.edu. Let X and Y be random variables with joint pdf f(, y) = 3y( + y) where and y. Determine each of the following probabilities. Solutions. a. P (X ). b. P (X
Διαβάστε περισσότεραSolution 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 περισσότεραChapter 5, 6 Multiple Random Variables ENCS Probability and Stochastic Processes
Chapter 5, 6 Multiple Random Variables ENCS6161 - Probability and Stochastic Processes Concordia University ENCS6161 p.1/47 Vector Random Variables A vector r.v. X is a function X : S R n, where S is the
Διαβάστε περισσότεραSOLUTIONS TO MATH38181 EXTREME VALUES AND FINANCIAL RISK EXAM
SOLUTIONS TO MATH38181 EXTREME VALUES AND FINANCIAL RISK EXAM Solutions to Question 1 a) The cumulative distribution function of T conditional on N n is Pr T t N n) Pr max X 1,..., X N ) t N n) Pr max
Διαβάστε περισσότεραSOLUTIONS TO MATH38181 EXTREME VALUES AND FINANCIAL RISK EXAM
SOLUTIONS TO MATH38181 EXTREME VALUES AND FINANCIAL RISK EXAM Solutions to Question 1 a) The cumulative distribution function of T conditional on N n is Pr (T t N n) Pr (max (X 1,..., X N ) t N n) Pr (max
Διαβάστε περισσότεραStatistics 104: Quantitative Methods for Economics Formula and Theorem Review
Harvard College Statistics 104: Quantitative Methods for Economics Formula and Theorem Review Tommy MacWilliam, 13 tmacwilliam@college.harvard.edu March 10, 2011 Contents 1 Introduction to Data 5 1.1 Sample
Διαβάστε περισσότεραHomework #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 περισσότερα3.4 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 περισσότεραHomework 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 περισσότεραAn Inventory of Continuous Distributions
Appendi A An Inventory of Continuous Distributions A.1 Introduction The incomplete gamma function is given by Also, define Γ(α; ) = 1 with = G(α; ) = Z 0 Z 0 Z t α 1 e t dt, α > 0, >0 t α 1 e t dt, α >
Διαβάστε περισσότεραSCHOOL OF MATHEMATICAL SCIENCES G11LMA Linear Mathematics Examination Solutions
SCHOOL OF MATHEMATICAL SCIENCES GLMA Linear Mathematics 00- Examination Solutions. (a) i. ( + 5i)( i) = (6 + 5) + (5 )i = + i. Real part is, imaginary part is. (b) ii. + 5i i ( + 5i)( + i) = ( i)( + i)
Διαβάστε περισσότεραFourier 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 περισσότεραCalculus 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 περισσότερα2 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 περισσότεραOther 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 περισσότεραFinite 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 περισσότεραΣΤΟΧΑΣΤΙΚΑ ΣΥΣΤΗΜΑΤΑ & ΕΠΙΚΟΙΝΩΝΙΕΣ 1o Τμήμα (Α - Κ): Αμφιθέατρο 4, Νέα Κτίρια ΣΗΜΜΥ Θεωρία Πιθανοτήτων & Στοχαστικές Ανελίξεις - 2
ΣΤΟΧΑΣΤΙΚΑ ΣΥΣΤΗΜΑΤΑ & ΕΠΙΚΟΙΝΩΝΙΕΣ 1o Τμήμα (Α - Κ): Αμφιθέατρο 4, Νέα Κτίρια ΣΗΜΜΥ Θεωρία Πιθανοτήτων & Στοχαστικές Ανελίξεις - 5.4: Στατιστικοί Μέσοι Όροι 5.5 Στοχαστικές Ανελίξεις (Stochastic Processes)
Διαβάστε περισσότεραST5224: 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 περισσότεραSection 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 περισσότεραAdditional Results for the Pareto/NBD Model
Additional Results for the Pareto/NBD Model Peter S. Fader www.petefader.com Bruce G. S. Hardie www.brucehardie.com January 24 Abstract This note derives expressions for i) the raw moments of the posterior
Διαβάστε περισσότεραCHAPTER 5. p(x,y) x
CHAPTER 5 Sction 5.. a. P(X, Y p(,. b. P(X and Y p(, + p(, + p(, + p(,.4 c. At last on hos is in us at both islands. P(X and Y p(, + p(, + p(, + p(,.7 d. B summing row probabilitis, p (.6,.4,.5 for,,,
Διαβάστε περισσότερα1. A fully continuous 20-payment years, 30-year term life insurance of 2000 is issued to (35). You are given n A 1
Chapter 7: Exercises 1. A fully continuous 20-payment years, 30-year term life insurance of 2000 is issued to (35). You are given n A 1 35+n:30 n a 35+n:20 n 0 0.068727 11.395336 10 0.097101 7.351745 25
Διαβάστε περισσότεραTheorem 8 Let φ be the most powerful size α test of H
Testing composite hypotheses Θ = Θ 0 Θ c 0 H 0 : θ Θ 0 H 1 : θ Θ c 0 Definition 16 A test φ is a uniformly most powerful (UMP) level α test for H 0 vs. H 1 if φ has level α and for any other level α test
Διαβάστε περισσότεραAreas and Lengths in Polar Coordinates
Kiryl Tsishchanka Areas and Lengths in Polar Coordinates In this section we develop the formula for the area of a region whose boundary is given by a polar equation. We need to use the formula for the
Διαβάστε περισσότεραGeneral 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 περισσότεραProbability and Random Processes (Part II)
Probability and Random Processes (Part II) 1. If the variance σ x of d(n) = x(n) x(n 1) is one-tenth the variance σ x of a stationary zero-mean discrete-time signal x(n), then the normalized autocorrelation
Διαβάστε περισσότεραΣΤΟΧΑΣΤΙΚΑ ΣΥΣΤΗΜΑΤΑ & ΕΠΙΚΟΙΝΩΝΙΕΣ 1o Τμήμα (Α - Κ): Αμφιθέατρο 3, Νέα Κτίρια ΣΗΜΜΥ Θεωρία Πιθανοτήτων & Στοχαστικές Ανελίξεις - 1
ΣΤΟΧΑΣΤΙΚΑ ΣΥΣΤΗΜΑΤΑ & ΕΠΙΚΟΙΝΩΝΙΕΣ 1o Τμήμα (Α - Κ): Αμφιθέατρο 3, Νέα Κτίρια ΣΗΜΜΥ Θεωρία Πιθανοτήτων & Στοχαστικές Ανελίξεις - 1 5.1: Εισαγωγή 5.2: Πιθανότητες 5.3: Τυχαίες Μεταβλητές καθ. Βασίλης Μάγκλαρης
Διαβάστε περισσότεραα 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 περισσότεραOrdinal 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 περισσότεραExample Sheet 3 Solutions
Example Sheet 3 Solutions. i Regular Sturm-Liouville. ii Singular Sturm-Liouville mixed boundary conditions. iii Not Sturm-Liouville ODE is not in Sturm-Liouville form. iv Regular Sturm-Liouville note
Διαβάστε περισσότεραCHAPTER 101 FOURIER SERIES FOR PERIODIC FUNCTIONS OF PERIOD
CHAPTER FOURIER SERIES FOR PERIODIC FUNCTIONS OF PERIOD EXERCISE 36 Page 66. Determine the Fourier series for the periodic function: f(x), when x +, when x which is periodic outside this rge of period.
Διαβάστε περισσότεραAreas and Lengths in Polar Coordinates
Kiryl Tsishchanka Areas and Lengths in Polar Coordinates In this section we develop the formula for the area of a region whose boundary is given by a polar equation. We need to use the formula for the
Διαβάστε περισσότεραSection 7.6 Double and Half Angle Formulas
09 Section 7. Double and Half Angle Fmulas To derive the double-angles fmulas, we will use the sum of two angles fmulas that we developed in the last section. We will let α θ and β θ: cos(θ) cos(θ + θ)
Διαβάστε περισσότεραderivation 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 περισσότεραFractional 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 περισσότεραChapter 6: Systems of Linear Differential. be continuous functions on the interval
Chapter 6: Systems of Linear Differential Equations Let a (t), a 2 (t),..., a nn (t), b (t), b 2 (t),..., b n (t) be continuous functions on the interval I. The system of n first-order differential equations
Διαβάστε περισσότεραω ω ω ω ω ω+2 ω ω+2 + ω ω ω ω+2 + ω ω+1 ω ω+2 2 ω ω ω ω ω ω ω ω+1 ω ω2 ω ω2 + ω ω ω2 + ω ω ω ω2 + ω ω+1 ω ω2 + ω ω+1 + ω ω ω ω2 + ω
0 1 2 3 4 5 6 ω ω + 1 ω + 2 ω + 3 ω + 4 ω2 ω2 + 1 ω2 + 2 ω2 + 3 ω3 ω3 + 1 ω3 + 2 ω4 ω4 + 1 ω5 ω 2 ω 2 + 1 ω 2 + 2 ω 2 + ω ω 2 + ω + 1 ω 2 + ω2 ω 2 2 ω 2 2 + 1 ω 2 2 + ω ω 2 3 ω 3 ω 3 + 1 ω 3 + ω ω 3 +
Διαβάστε περισσότεραC.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 περισσότεραMean-Variance Analysis
Mean-Variance Analysis Jan Schneider McCombs School of Business University of Texas at Austin Jan Schneider Mean-Variance Analysis Beta Representation of the Risk Premium risk premium E t [Rt t+τ ] R1
Διαβάστε περισσότεραIf we restrict the domain of y = sin x to [ π, π ], the restrict function. y = sin x, π 2 x π 2
Chapter 3. Analytic Trigonometry 3.1 The inverse sine, cosine, and tangent functions 1. Review: Inverse function (1) f 1 (f(x)) = x for every x in the domain of f and f(f 1 (x)) = x for every x in the
Διαβάστε περισσότερα= λ 1 1 e. = λ 1 =12. has the properties e 1. e 3,V(Y
Stat 50 Homework Solutions Spring 005. (a λ λ λ 44 (b trace( λ + λ + λ 0 (c V (e x e e λ e e λ e (λ e by definition, the eigenvector e has the properties e λ e and e e. (d λ e e + λ e e + λ e e 8 6 4 4
Διαβάστε περισσότεραTMA4115 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 περισσότεραSCITECH Volume 13, Issue 2 RESEARCH ORGANISATION Published online: March 29, 2018
Journal of rogressive Research in Mathematics(JRM) ISSN: 2395-028 SCITECH Volume 3, Issue 2 RESEARCH ORGANISATION ublished online: March 29, 208 Journal of rogressive Research in Mathematics www.scitecresearch.com/journals
Διαβάστε περισσότεραThe 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 περισσότεραIf we restrict the domain of y = sin x to [ π 2, π 2
Chapter 3. Analytic Trigonometry 3.1 The inverse sine, cosine, and tangent functions 1. Review: Inverse function (1) f 1 (f(x)) = x for every x in the domain of f and f(f 1 (x)) = x for every x in the
Διαβάστε περισσότεραBayesian statistics. DS GA 1002 Probability and Statistics for Data Science.
Bayesian statistics DS GA 1002 Probability and Statistics for Data Science http://www.cims.nyu.edu/~cfgranda/pages/dsga1002_fall17 Carlos Fernandez-Granda Frequentist vs Bayesian statistics In frequentist
Διαβάστε περισσότεραAquinas College. Edexcel Mathematical formulae and statistics tables DO NOT WRITE ON THIS BOOKLET
Aquinas College Edexcel Mathematical formulae and statistics tables DO NOT WRITE ON THIS BOOKLET Pearson Edexcel Level 3 Advanced Subsidiary and Advanced GCE in Mathematics and Further Mathematics Mathematical
Διαβάστε περισσότερα12xy(1 x)dx = 12y. = 12 y. = 12 y( ) = 12 y 1 6 = 2y. x 6x(1 x)dx = 6. dx = 6 3 x4
Πανεπιστήµιο Κρήτης - Τµήµα Επιστήµης Υπολογιστών ΗΥ-7: Πιθανότητες-Χειµερινό Εξάµηνο 5 ιδάσκων: Π. Τσακαλίδης Λύσεις 6ης Σειρά Ασκήσεων Ασκηση. α) Η περιθωριακή σ.π.π. της f X,Y για την τ.µ X γίνεται:
Διαβάστε περισσότεραLecture 34 Bootstrap confidence intervals
Lecture 34 Bootstrap confidence intervals Confidence Intervals θ: an unknown parameter of interest We want to find limits θ and θ such that Gt = P nˆθ θ t If G 1 1 α is known, then P θ θ = P θ θ = 1 α
Διαβάστε περισσότεραQuadratic Expressions
Quadratic Expressions. The standard form of a quadratic equation is ax + bx + c = 0 where a, b, c R and a 0. The roots of ax + bx + c = 0 are b ± b a 4ac. 3. For the equation ax +bx+c = 0, sum of the roots
Διαβάστε περισσότεραCHAPTER 12: PERIMETER, AREA, CIRCUMFERENCE, AND 12.1 INTRODUCTION TO GEOMETRIC 12.2 PERIMETER: SQUARES, RECTANGLES,
CHAPTER : PERIMETER, AREA, CIRCUMFERENCE, AND SIGNED FRACTIONS. INTRODUCTION TO GEOMETRIC MEASUREMENTS p. -3. PERIMETER: SQUARES, RECTANGLES, TRIANGLES p. 4-5.3 AREA: SQUARES, RECTANGLES, TRIANGLES p.
Διαβάστε περισσότεραΘΕΩΡΙΑ ΠΙΘΑΝΟΤΗΤΩΝ Ι (ΝΠΣ) ΠΙΘΑΝΟΤΗΤΕΣ Ι (ΠΠΣ) Φεβρουάριος 2010
ΘΕΩΡΙΑ ΠΙΘΑΝΟΤΗΤΩΝ Ι (ΝΠΣ) ΠΙΘΑΝΟΤΗΤΕΣ Ι (ΠΠΣ) Φεβρουάριος 1 Επώνυμο... Όνομα... A.E.M.... Εξάμηνο... Θέμα 1 Θέμα Θέμα 3 Θέμα 4 Θέμα 5 Θέμα 5* Βαθμός ΝΠΣ ΠΠΣ / / / / / /1 / / / / / / /1 ΘΕΜΑ 1: Στο ράφι
Διαβάστε περισσότερα4. Απαγορεύεται η χρήση υπολογιστή χειρός. Απαγορεύεται η χρήση κινητού, και ως υπολογιστή χειρός.
ΟΙΚΟΝΟΜΙΚΟ ΠΑΝΕΠΙΣΤΗΜΙΟ ΑΘΗΝΩΝ, ΤΜΗΜΑ ΠΛΗΡΟΦΟΡΙΚΗΣ ΠΙΘΑΝΟΤΗΤΕΣ, ΙΩΑΝΝΗΣ ΚΟΝΤΟΓΙΑΝΝΗΣ, ΣΤΑΥΡΟΣ ΤΟΥΜΠΗΣ ΤΕΛΙΚΗ ΕΞΕΤΑΣΗ, ΙΟΥΝΙΟΣ 207 ΟΝΟΜΑ ΦΟΙΤΗΤΗ:.............................. Οδηγίες. Συμπληρώστε το όνομά
Διαβάστε περισσότεραConcrete Mathematics Exercises from 30 September 2016
Concrete Mathematics Exercises from 30 September 2016 Silvio Capobianco Exercise 1.7 Let H(n) = J(n + 1) J(n). Equation (1.8) tells us that H(2n) = 2, and H(2n+1) = J(2n+2) J(2n+1) = (2J(n+1) 1) (2J(n)+1)
Διαβάστε περισσότεραΕΠΙΧΕΙΡΗΣΙΑΚΗ ΑΛΛΗΛΟΓΡΑΦΙΑ ΚΑΙ ΕΠΙΚΟΙΝΩΝΙΑ ΣΤΗΝ ΑΓΓΛΙΚΗ ΓΛΩΣΣΑ
Ανοικτά Ακαδημαϊκά Μαθήματα στο ΤΕΙ Ιονίων Νήσων ΕΠΙΧΕΙΡΗΣΙΑΚΗ ΑΛΛΗΛΟΓΡΑΦΙΑ ΚΑΙ ΕΠΙΚΟΙΝΩΝΙΑ ΣΤΗΝ ΑΓΓΛΙΚΗ ΓΛΩΣΣΑ Ενότητα 1: Elements of Syntactic Structure Το περιεχόμενο του μαθήματος διατίθεται με άδεια
Διαβάστε περισσότεραHOMEWORK#1. t E(x) = 1 λ = (b) Find the median lifetime of a randomly selected light bulb. Answer:
HOMEWORK# 52258 李亞晟 Eercise 2. The lifetime of light bulbs follows an eponential distribution with a hazard rate of. failures per hour of use (a) Find the mean lifetime of a randomly selected light bulb.
Διαβάστε περισσότεραSolve the difference equation
Solve the differece equatio Solutio: y + 3 3y + + y 0 give tat y 0 4, y 0 ad y 8. Let Z{y()} F() Taig Z-trasform o both sides i (), we get y + 3 3y + + y 0 () Z y + 3 3y + + y Z 0 Z y + 3 3Z y + + Z y
Διαβάστε περισσότεραMathCity.org Merging man and maths
MathCity.org Merging man and maths Exercise 10. (s) Page Textbook of Algebra and Trigonometry for Class XI Available online @, Version:.0 Question # 1 Find the values of sin, and tan when: 1 π (i) (ii)
Διαβάστε περισσότεραProblem Set 3: Solutions
CMPSCI 69GG Applied Information Theory Fall 006 Problem Set 3: Solutions. [Cover and Thomas 7.] a Define the following notation, C I p xx; Y max X; Y C I p xx; Ỹ max I X; Ỹ We would like to show that C
Διαβάστε περισσότεραHomework 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 περισσότεραSecond Order Partial Differential Equations
Chapter 7 Second Order Partial Differential Equations 7.1 Introduction A second order linear PDE in two independent variables (x, y Ω can be written as A(x, y u x + B(x, y u xy + C(x, y u u u + D(x, y
Διαβάστε περισσότεραSequential Bayesian Search Appendices
Squnial Baysian Sarch Appndics Zhn Wn Branislav Kvon Brian Eriksson Sandilya Bhamidipai A Proof of Thorm Assum ha a h binnin of am, h sysm s blif in h usr s prfrnc is P crainy-quival usr prfrnc durin am
Διαβάστε περισσότεραReminders: linear functions
Reminders: linear functions Let U and V be vector spaces over the same field F. Definition A function f : U V is linear if for every u 1, u 2 U, f (u 1 + u 2 ) = f (u 1 ) + f (u 2 ), and for every u U
Διαβάστε περισσότεραCRASH COURSE IN PRECALCULUS
CRASH COURSE IN PRECALCULUS Shiah-Sen Wang The graphs are prepared by Chien-Lun Lai Based on : Precalculus: Mathematics for Calculus by J. Stuwart, L. Redin & S. Watson, 6th edition, 01, Brooks/Cole Chapter
Διαβάστε περισσότεραb. Use the parametrization from (a) to compute the area of S a as S a ds. Be sure to substitute for ds!
MTH U341 urface Integrals, tokes theorem, the divergence theorem To be turned in Wed., Dec. 1. 1. Let be the sphere of radius a, x 2 + y 2 + z 2 a 2. a. Use spherical coordinates (with ρ a) to parametrize.
Διαβάστε περισσότεραIntroduction to the ML Estimation of ARMA processes
Introduction to the ML Estimation of ARMA processes Eduardo Rossi University of Pavia October 2013 Rossi ARMA Estimation Financial Econometrics - 2013 1 / 1 We consider the AR(p) model: Y t = c + φ 1 Y
Διαβάστε περισσότεραΚΑΤΑΝΟΜΕΣ Ι ΙΑΣΤΑΤΩΝ ΤΥΧΑΙΩΝ ΜΕΤΑΒΛΗΤΩΝ (Συνέχεια)
(Συνέχεια) Χαράλαµπος Α. Χαραλαµπίδης 23 εκεµβρίου 29 5.1. Στο τυχαίο πείραµα της ϱίψης δύο διακεκριµένων κύβων έστω X η ένδειξη του πρώτου κύβου και Y η µεγαλύτερη από τις δύο ενδείξεις. Να προσδιορισθούν
Διαβάστε περισσότεραA Note on Intuitionistic Fuzzy. Equivalence Relation
International Mathematical Forum, 5, 2010, no. 67, 3301-3307 A Note on Intuitionistic Fuzzy Equivalence Relation D. K. Basnet Dept. of Mathematics, Assam University Silchar-788011, Assam, India dkbasnet@rediffmail.com
Διαβάστε περισσότεραPhys460.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 περισσότερα5.4 The Poisson Distribution.
The worst thing you can do about a situation is nothing. Sr. O Shea Jackson 5.4 The Poisson Distribution. Description of the Poisson Distribution Discrete probability distribution. The random variable
Διαβάστε περισσότεραList MF20. List of Formulae and Statistical Tables. Cambridge Pre-U Mathematics (9794) and Further Mathematics (9795)
List MF0 List of Formulae and Statistical Tables Cambridge Pre-U Mathematics (979) and Further Mathematics (979) For use from 07 in all aers for the above syllabuses. CST7 Mensuration Surface area of shere
Διαβάστε περισσότεραSection 9.2 Polar Equations and Graphs
180 Section 9. Polar Equations and Graphs In this section, we will be graphing polar equations on a polar grid. In the first few examples, we will write the polar equation in rectangular form to help identify
Διαβάστε περισσότεραΕΠΙΧΕΙΡΗΣΙΑΚΗ ΑΛΛΗΛΟΓΡΑΦΙΑ ΚΑΙ ΕΠΙΚΟΙΝΩΝΙΑ ΣΤΗΝ ΑΓΓΛΙΚΗ ΓΛΩΣΣΑ
Ανοικτά Ακαδημαϊκά Μαθήματα στο ΤΕΙ Ιονίων Νήσων ΕΠΙΧΕΙΡΗΣΙΑΚΗ ΑΛΛΗΛΟΓΡΑΦΙΑ ΚΑΙ ΕΠΙΚΟΙΝΩΝΙΑ ΣΤΗΝ ΑΓΓΛΙΚΗ ΓΛΩΣΣΑ Ενότητα 11: The Unreal Past Το περιεχόμενο του μαθήματος διατίθεται με άδεια Creative Commons
Διαβάστε περισσότεραPractice Exam 2. Conceptual Questions. 1. State a Basic identity and then verify it. (a) Identity: Solution: One identity is csc(θ) = 1
Conceptual Questions. State a Basic identity and then verify it. a) Identity: Solution: One identity is cscθ) = sinθ) Practice Exam b) Verification: Solution: Given the point of intersection x, y) of the
Διαβάστε περισσότεραMath 6 SL Probability Distributions Practice Test Mark Scheme
Math 6 SL Probability Distributions Practice Test Mark Scheme. (a) Note: Award A for vertical line to right of mean, A for shading to right of their vertical line. AA N (b) evidence of recognizing symmetry
Διαβάστε περισσότερα6. MAXIMUM LIKELIHOOD ESTIMATION
6 MAXIMUM LIKELIHOOD ESIMAION [1] Maximum Likelihood Estimator (1) Cases in which θ (unknown parameter) is scalar Notational Clarification: From now on, we denote the true value of θ as θ o hen, view θ
Διαβάστε περισσότεραΚΥΠΡΙΑΚΗ ΕΤΑΙΡΕΙΑ ΠΛΗΡΟΦΟΡΙΚΗΣ CYPRUS COMPUTER SOCIETY ΠΑΓΚΥΠΡΙΟΣ ΜΑΘΗΤΙΚΟΣ ΔΙΑΓΩΝΙΣΜΟΣ ΠΛΗΡΟΦΟΡΙΚΗΣ 19/5/2007
Οδηγίες: Να απαντηθούν όλες οι ερωτήσεις. Αν κάπου κάνετε κάποιες υποθέσεις να αναφερθούν στη σχετική ερώτηση. Όλα τα αρχεία που αναφέρονται στα προβλήματα βρίσκονται στον ίδιο φάκελο με το εκτελέσιμο
Διαβάστε περισσότεραEcon 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 περισσότεραArithmetical applications of lagrangian interpolation. Tanguy Rivoal. Institut Fourier CNRS and Université de Grenoble 1
Arithmetical applications of lagrangian interpolation Tanguy Rivoal Institut Fourier CNRS and Université de Grenoble Conference Diophantine and Analytic Problems in Number Theory, The 00th anniversary
Διαβάστε περισσότεραNowhere-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 περισσότεραApproximation of distance between locations on earth given by latitude and longitude
Approximation of distance between locations on earth given by latitude and longitude Jan Behrens 2012-12-31 In this paper we shall provide a method to approximate distances between two points on earth
Διαβάστε περισσότερα1. (a) (5 points) Find the unit tangent and unit normal vectors T and N to the curve. r(t) = 3cost, 4t, 3sint
1. a) 5 points) Find the unit tangent and unit normal vectors T and N to the curve at the point P, π, rt) cost, t, sint ). b) 5 points) Find curvature of the curve at the point P. Solution: a) r t) sint,,
Διαβάστε περισσότεραMatrices 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 περισσότερα70. Let Y be a metrizable topological space and let A Ď Y. Show that Cl Y A scl Y A.
Homework for MATH 4603 (Advanced Calculus I) Fall 2017 Homework 14: Due on Tuesday 12 December 66 Let s P pr 2 q N let a b P R Define p q : R 2 Ñ R by ppx yq x qpx yq y Show: r s Ñ pa bq in R 2 s ô r ppp
Διαβάστε περισσότεραP(Ο Χρήστος κερδίζει) = 1 P(Ο Χρήστος χάνει) = 1 P(X > Y ) = 1 2. P(Ο Χρήστος νικά σε 7 από τους 10 αγώνες) = 7
ΠΑΝΕΠΙΣΤΗΜΙΟ ΚΡΗΤΗΣ Τµήµα Επιστήµης Υπολογιστών HY-27: Πιθανότητες - Χειµερινό Εξάµηνο 28 ιδάσκων: Π. Τσακαλίδης Λύσεις Εβδοµης Σειράς Ασκήσεων Ηµεροµηνία Ανάθεσης: 3/2/28 Ηµεροµηνία Παράδοσης: 7/2/28
Διαβάστε περισσότεραDistances in Sierpiński Triangle Graphs
Distances in Sierpiński Triangle Graphs Sara Sabrina Zemljič joint work with Andreas M. Hinz June 18th 2015 Motivation Sierpiński triangle introduced by Wac law Sierpiński in 1915. S. S. Zemljič 1 Motivation
Διαβάστε περισσότεραEvery 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 περισσότεραA Two-Sided Laplace Inversion Algorithm with Computable Error Bounds and Its Applications in Financial Engineering
Electronic Companion A Two-Sie Laplace Inversion Algorithm with Computable Error Bouns an Its Applications in Financial Engineering Ning Cai, S. G. Kou, Zongjian Liu HKUST an Columbia University Appenix
Διαβάστε περισσότεραA Lambda Model Characterizing Computational Behaviours of Terms
A Lambda Model Characterizing Computational Behaviours of Terms joint paper with Silvia Ghilezan RPC 01, Sendai, October 26, 2001 1 Plan of the talk normalization properties inverse limit model Stone dualities
Διαβάστε περισσότεραΚΑΤΑΝΟΜΕΣ Ι ΙΑΣΤΑΤΩΝ ΤΥΧΑΙΩΝ ΜΕΤΑΒΛΗΤΩΝ
ΚΑΤΑΝΟΜΕΣ Ι ΙΑΣΤΑΤΩΝ ΤΥΧΑΙΩΝ ΜΕΤΑΒΛΗΤΩΝ Χαράλαµπος Α. Χαραλαµπίδης 21 εκεµβρίου 2009 ΑΝΕΞΑΡΤΗΣΙΑ ΤΥΧΑΙΩΝ ΜΕΤΑΒΛΗΤΩΝ Ορισµός (α) Εστω (X, Y) διακριτή διδιάστατη τυχαία µεταβλητή µε συνάρτηση πιθανότητας
Διαβάστε περισσότεραk A = [k, k]( )[a 1, a 2 ] = [ka 1,ka 2 ] 4For the division of two intervals of confidence in R +
Chapter 3. Fuzzy Arithmetic 3- Fuzzy arithmetic: ~Addition(+) and subtraction (-): Let A = [a and B = [b, b in R If x [a and y [b, b than x+y [a +b +b Symbolically,we write A(+)B = [a (+)[b, b = [a +b
Διαβάστε περισσότεραΚΥΠΡΙΑΚΗ ΕΤΑΙΡΕΙΑ ΠΛΗΡΟΦΟΡΙΚΗΣ CYPRUS COMPUTER SOCIETY ΠΑΓΚΥΠΡΙΟΣ ΜΑΘΗΤΙΚΟΣ ΔΙΑΓΩΝΙΣΜΟΣ ΠΛΗΡΟΦΟΡΙΚΗΣ 6/5/2006
Οδηγίες: Να απαντηθούν όλες οι ερωτήσεις. Ολοι οι αριθμοί που αναφέρονται σε όλα τα ερωτήματα είναι μικρότεροι το 1000 εκτός αν ορίζεται διαφορετικά στη διατύπωση του προβλήματος. Διάρκεια: 3,5 ώρες Καλή
Διαβάστε περισσότεραΕΛΛΗΝΙΚΗ ΔΗΜΟΚΡΑΤΙΑ ΠΑΝΕΠΙΣΤΗΜΙΟ ΚΡΗΤΗΣ. Ψηφιακή Οικονομία. Διάλεξη 7η: Consumer Behavior Mαρίνα Μπιτσάκη Τμήμα Επιστήμης Υπολογιστών
ΕΛΛΗΝΙΚΗ ΔΗΜΟΚΡΑΤΙΑ ΠΑΝΕΠΙΣΤΗΜΙΟ ΚΡΗΤΗΣ Ψηφιακή Οικονομία Διάλεξη 7η: Consumer Behavior Mαρίνα Μπιτσάκη Τμήμα Επιστήμης Υπολογιστών Τέλος Ενότητας Χρηματοδότηση Το παρόν εκπαιδευτικό υλικό έχει αναπτυχθεί
Διαβάστε περισσότερα( 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 περισσότεραHomework 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 περισσότεραNew bounds for spherical two-distance sets and equiangular lines
New bounds for spherical two-distance sets and equiangular lines Michigan State University Oct 8-31, 016 Anhui University Definition If X = {x 1, x,, x N } S n 1 (unit sphere in R n ) and x i, x j = a
Διαβάστε περισσότεραChapter 6: Systems of Linear Differential. be continuous functions on the interval
Chapter 6: Systems of Linear Differential Equations Let a (t), a 2 (t),..., a nn (t), b (t), b 2 (t),..., b n (t) be continuous functions on the interval I. The system of n first-order differential equations
Διαβάστε περισσότεραPULLEYS 1. GROOVE SPECIFICATIONS FOR V-BELT PULLEYS. Groove dimensions and tolerances for Hi-Power PowerBand according to RMA engineering standards
1. GROOVE SPECIFICATIONS FOR V-BELT PULLEYS Figur 3 - Groov dimnsion nomnclatur or V-blts α go lp b Ectiv diamtr Datum diamtr d Tabl No. 1 - Groov dimnsions and tolrancs or Hi-Powr PowrBand according to
Διαβάστε περισσότεραΕΦΑΡΜΟΣΜΕΝΗ ΣΤΑΤΙΣΤΙΚΗ I Παντελής Δημήτριος Τμήμα Μηχανολόγων Μηχανικών
ΕΦΑΡΜΟΣΜΕΝΗ ΣΤΑΤΙΣΤΙΚΗ I Παντελής Δημήτριος Τμήμα Μηχανολόγων Μηχανικών ΤΥΧΑΙΕΣ ΜΕΤΑΒΛΗΤΕΣ Σε κάθε αποτέλεσμα του πειράματος αντιστοιχεί μία αριθμητική τιμή Μαθηματικός ορισμός: Τυχαία μεταβλητή X είναι
Διαβάστε περισσότερα