Outline. Detection Theory. Background. Background (Cont.)
|
|
- Νεφέλη Ζάρκος
- 5 χρόνια πριν
- Προβολές:
Transcript
1 Outlie etectio heory Chapter7. etermiistic Sigals with Ukow Parameters afiseh S. Mazloum ov. 3th Backgroud Importace of sigal iformatio Ukow amplitude Ukow arrival time Siusoidal detectio Classical liear model Backgroud Statistical ecisio heory I PF for assumed hypothesis is completely kow. pproaches: eyma Pearso theorem Bayesia approach based o miimizatio of Bayes risk Sigal model dl etermiistic sigal Radom sigal Backgroud (Cot. Statistical ecisio heory II PF has ukow parameters (i this chapter determiistic sigals pproaches: UMPdoes otusually exist Geeralized Likelihood Ratio est (GLR ecide if p( x;, LG ( x p( x;, Bayesia approach ecide if (, ( p( x; p x p d p( x; p(, p( d x
2 Importace of Sigal Iformatio ssume there is o kowledge of sigal : x [ ] ω[ ],,..., : x [ ] s [ ] + ω [ ],,...,,, ω[ ] ~ WG with variace s [ ]~ determiistic ad completely ukow GLR decides if p( x; s[],..., s[ ], p ( x ; where s [ ]is MLE of s [ ]for,,...,,, Importace of Sigal Iformatio (Cot. Uder the PF of p ( x; exp [ ] x ( π Uder the PF of hus, x x s [ ] x [ ] p ( x ; s [],..., s [ ], exp ( [ ] [ ] x s ( π ( π exp x [ ] ( π eergy detector ( X x [ ] xs [ ] [ ] Importace of Sigal Iformatio (Cot. etectio performace ( ( P Q Q P d Loss i performace d E d MF dmf log log db d E Matched filter coheretly combies data Eergy detector icoheretly combies data Ukow mplitude etectig a determiistic sigal with ukow amplitude i WG UMP test GLR Bayesia approach : [ ] [ ],,...,,, : x [ ] s [ ] + ω[ ],,..., :ukow amplitude ω[ ] ~ WG with variace s [ ]: determiistic ad completely kow
3 Ukow mplitude (Cot. UMP test: LR decides if exp ( [ ] [ ] xs p( x; ( π p( x; exp [ ] x ( π x [ ] s [ ] > UMP does ot exist xs [ ] [ ] > > xs [ ] [ ]< < Ukow mplitude (Cot. GLR decides if ( ; px, ( π px ( ; is MLE of uder ( X x[ ] s[ ] exp ( x[ ] s[ ] exp x [ ] ( π xs [ ] [ ] s [ ] or xs [ ] [ ] > Ukow mplitude (Cot. Ukow mplitude (Cot. GLR detector performace P Pr u( x ; { } { } P Pr u( x ; xs [ ] [ ] > GLR detector performace ( ( / ( / ( P Q Q P d + Q Q P + d where d, s [ ] uder xs ux ( [ ] [ ] s [ ], s [ ] uder
4 Ukow mplitude (Cot. Ukow mplitude (Cot. Bayesia approach, uder x s + w x + w ( [ s[], s[],..., s[ ] ] radom variable with PF μ, s w WG with variace (, I P test statistic μ ( x ( x s + ( s s x s + s s + correlator Squared correlator P test decides if (results i Sectio 5.6 ( ( x x C + Cω μ + xc C ( C + C x> ω ω μ μ ω C C I if (kow amplitude case ( x x μ s ( x ( x s ss If (ukow amplitude μ Usig Bayesia detector we require kowledge of ad. Ukow rrival ime Ukow rrival ime (Cot. etectig a determiistic sigal with ukow arrival time i WG : [ ],,..., : x [ ] s [ ] + ω[ ],,..., GLR decides if p( x;, p( ( x ; is MLE of uder + M max xs [ ] [ ] ukow delay ω[ ] WG with variace s [ ] detemiistic ad kow [, M-] + M Observatio iterval Uder + M p ( x ;, exp [ ] x π M. exp [ ] [ ] π ( x s. exp x [ ] + M π
5 Ukow rrival ime (Cot. est statistic + M + M ( xs [ ] [ ] s[ ] + M ( x x[ ] s[ ] + M ( x max x[ ] s[ ] [, M ] + Choose ( > max over < M s [ ] x [ ] Ch ( x s [ ] Ukow rrival ime (Cot. etectio performace of GLR is difficult! PF of correlated Gaussia radom variable has to be determied. + M P Pr max [ ] [ ] ; [, M] x s + M P Pr max xs [ ] [ ] γ ; [, M] > elay is less tha samplig iterval ( x max X( f S( fexp( j π f df [, M] * Siusoidal etectio etectio of a siusoid i WG : x [ ] ω[ ],,..., ω [ ],,...,,,, + M,...,, : x [ ] cos( π f + ϕ + ω[ ], +,..., + M GLR approach ukow, ϕ ukow, ϕ, f ukow, ϕ, f, ukow Siusoidal etectio (Cot. etectio of a siusoid i WG with ukow amplitude ad phase, GLR decides if p ( x ;, ϕ, p( x; ad ϕ are MLE of ad ϕ α + α α ϕ arcta α α x [ ]cos π f α x[ ]siπ f
6 Siusoidal etectio (Cot. We decide if exp ( [ ] cos( ϕ exp [ ] x ( π x π f + ( π x [ ]exp( j π f I( f periodogram detector icoheret or quadrature MF Siusoidal etectio (Cot. GLR detectio performace { } { } P Pr I( f ; P Pr I( f ; I( f ξ + ξ joitly Gaussia ξ ξξ ξ x [ ]cosπ f, I uder ξ x [ ]siπ f ξ cosϕ, I uder si ϕ Siusoidal etectio (Cot. Siusoidal etectio (Cot. GLR detectio performace P Q exp χ γ P Q χ λ ( etectio of a siusoid i WG with ukow GLR decides if p ( x ;, ϕ, f,, p( x;, ϕ, f, P Q l χ ( λ P λ, ϕ, f ad are MLE of, ϕ, f ad α + α α + M ϕ arcta α α M + M x π f M α [ ]cos ( x π f [ ]si (
7 Siusoidal etectio (Cot. fter simplificatio, p( x;, ϕ, f (,, I f l p( x; where I ( f x[ ]exp( j f We decide if I ( f max >, f + M + M π M + Choose max over, f < x [ ] exp( j π f Classical Liear Model Liear Bayesia model etectio problem with ukow sigal parameters coverts to geeral Gaussia problem P detector Classical liear model Parameters assumed determiistic GLR GLR Classical Liear Model ssume that x + w is kow pobservatio matrix of rak GLR for hypothesis testig problem : b : b is p vector of parameters w is oise vector with PF (, I is r pmatrix of rak r b is r vector bis cosistet set of liear equatios p GLR Classical Liear Model (Cot. We decide if ( b ( ( ( l L ( b x G x ( x is the MLE of uder
8 GLR Classical Liear Model (Cot. etectio performace P Q χ ( r GLR Classical Liear Model (Cot. Ukow amplitude sigal i WG : [ ] [ ] x ω,,..., : x [ ] s [ ] + ω [ ],,..., P Q χ λ ( ( r ( b ( ( λ b : x [ ] s [ ] + ω[ ],,,..., : x [ ] s [ ] + ω [ ],,,..., x + w : [ s[], s[],..., s[ ]] : GLR Classical Liear Model (Cot. We decide if ( b ( ( ( l LG ( b x x, b ( x ( x x[ s ] [ ] s [ ] x[ s ] [ ] ( x s [ ] GLR Classical Liear Model (Cot. etectio performace P Q χ ( P Q ( χ ( λ r r s [ ] whereλ ( ( / ( / ( P Q Q P d + Q Q P + d where d
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 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 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 περισσότεραLecture 17: Minimum Variance Unbiased (MVUB) Estimators
ECE 830 Fall 2011 Statistical Sigal Processig istructor: R. Nowak, scribe: Iseok Heo Lecture 17: Miimum Variace Ubiased (MVUB Estimators Ultimately, we would like to be able to argue that a give estimator
Διαβάστε περισσότεραSolutions: Homework 3
Solutios: Homework 3 Suppose that the radom variables Y,, Y satisfy Y i = βx i + ε i : i,, where x,, x R are fixed values ad ε,, ε Normal0, σ ) with σ R + kow Fid ˆβ = MLEβ) IND Solutio: Observe that Y
Διαβάστε περισσότεραLAD Estimation for Time Series Models With Finite and Infinite Variance
LAD Estimatio for Time Series Moels With Fiite a Ifiite Variace Richar A. Davis Colorao State Uiversity William Dusmuir Uiversity of New South Wales 1 LAD Estimatio for ARMA Moels fiite variace ifiite
Διαβάστε περισσότεραParameter Estimation Fitting Probability Distributions Bayesian Approach
Parameter Estimatio Fittig Probability Distributios Bayesia Approach MIT 18.443 Dr. Kempthore Sprig 2015 1 MIT 18.443 Parameter EstimatioFittig Probability DistributiosBayesia Ap Outlie Bayesia Approach
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 περισσότεραSUPERPOSITION, 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 περισσότεραp n r.01.05.10.15.20.25.30.35.40.45.50.55.60.65.70.75.80.85.90.95
r r Table 4 Biomial Probability Distributio C, r p q This table shows the probability of r successes i idepedet trials, each with probability of success p. p r.01.05.10.15.0.5.30.35.40.45.50.55.60.65.70.75.80.85.90.95
Διαβάστε περισσότεραTransmitter Channel Receiver. (modulated signal ) s(t) + r(t) (received signal ) n(t) (noise) G n (f)
Ψηφιακές Επικοινωνίες Ανίχνευση εδοµένων σε Βασική Ζώνη Θεωρία Θορύβου (additive white Gaussia Noise/AWGN) υαδική (Biary) Μετάδοση Σήµατος Ανίχνευση (Detectio) Biary Σήµατος σε Gaussia Θόρυβο Προσαρµοσµένο
Διαβάστε περισσότεραΨηφιακή 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 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 περισσότεραThe Equivalence Theorem in Optimal Design
he Equivalece heorem i Optimal Desig Raier Schwabe & homas Schmelter, Otto vo Guericke Uiversity agdeburg Bayer Scherig Pharma, Berli rschwabe@ovgu.de PODE 007 ay 4, 007 Outlie Prologue: Simple eamples.
Διαβάστε περισσότερα1. 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 περισσότεραStatistical 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 περισσότεραn r f ( n-r ) () x g () r () x (1.1) = Σ g() x = Σ n f < -n+ r> g () r -n + r dx r dx n + ( -n,m) dx -n n+1 1 -n -1 + ( -n,n+1)
8 Higher Derivative of the Product of Two Fuctios 8. Leibiz Rule about the Higher Order Differetiatio Theorem 8.. (Leibiz) Whe fuctios f ad g f g are times differetiable, the followig epressio holds. r
Διαβάστε περισσότεραOutline. 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 περισσότεραEstimation for ARMA Processes with Stable Noise. Matt Calder & Richard A. Davis Colorado State University
Estimation for ARMA Processes with Stable Noise Matt Calder & Richard A. Davis Colorado State University rdavis@stat.colostate.edu 1 ARMA processes with stable noise Review of M-estimation Examples of
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 περισσότεραHW 3 Solutions 1. a) I use the auto.arima R function to search over models using AIC and decide on an ARMA(3,1)
HW 3 Solutions a) I use the autoarima R function to search over models using AIC and decide on an ARMA3,) b) I compare the ARMA3,) to ARMA,0) ARMA3,) does better in all three criteria c) The plot of the
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 περισσότεραBiorthogonal Wavelets and Filter Banks via PFFS. Multiresolution Analysis (MRA) subspaces V j, and wavelet subspaces W j. f X n f, τ n φ τ n φ.
Chapter 3. Biorthogoal Wavelets ad Filter Baks via PFFS 3.0 PFFS applied to shift-ivariat subspaces Defiitio: X is a shift-ivariat subspace if h X h( ) τ h X. Ex: Multiresolutio Aalysis (MRA) subspaces
Διαβάστε περισσότεραEN40: Dynamics and Vibrations
EN40: Dyamics a Vibratios School of Egieerig Brow Uiversity Solutios to Differetial Equatios of Motio for Vibratig Systems Here, we summarize the solutios to the most importat ifferetial equatios of motio
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 περισσότεραFREE VIBRATION OF A SINGLE-DEGREE-OF-FREEDOM SYSTEM Revision B
FREE VIBRATION OF A SINGLE-DEGREE-OF-FREEDOM SYSTEM Revisio B By Tom Irvie Email: tomirvie@aol.com February, 005 Derivatio of the Equatio of Motio Cosier a sigle-egree-of-freeom system. m x k c where m
Διαβάστε περισσότεραLocal Approximation with Kernels
Local Approximation with Kernels Thomas Hangelbroek University of Hawaii at Manoa 5th International Conference Approximation Theory, 26 work supported by: NSF DMS-43726 A cubic spline example Consider
Διαβάστε περισσότεραEE 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 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 περισσότεραAn Introduction to Signal Detection and Estimation - Second Edition Chapter II: Selected Solutions
An Introduction to Signal Detection Estimation - Second Edition Chapter II: Selected Solutions H V Poor Princeton University March 16, 5 Exercise : The likelihood ratio is given by L(y) (y +1), y 1 a With
Διαβάστε περισσότεραSecond 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 περισσότεραECE 308 SIGNALS AND SYSTEMS FALL 2017 Answers to selected problems on prior years examinations
ECE 308 SIGNALS AND SYSTEMS FALL 07 Answers to selected problems on prior years examinations Answers to problems on Midterm Examination #, Spring 009. x(t) = r(t + ) r(t ) u(t ) r(t ) + r(t 3) + u(t +
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 περισσότεραBaseband Transmission
Ψηφιακές Επικοινωνίες Baseband ransmission Antipodal Signalling - Binary Orthogonal Signalling Probability of Error M-ary Orthogonal Signalling Waveforms Detection M-PAM detection Probability of error
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 περισσότεραHMY 795: Αναγνώριση Προτύπων
HMY 795: Αναγνώριση Προτύπων Επανάληψη Expectatio maximizatio for Gaussia mixtures. Αρχικοποιούμε τις άγνωστες παραμέτρους µ k, Σ k και π k 2. Υπολογίσμος των resposibilitiesγ(z k : γ ( z = k π ( x µ ˆ,
Διαβάστε περισσότεραL.K.Gupta (Mathematic Classes) www.pioeermathematics.com MOBILE: 985577, 4677 + {JEE Mai 04} Sept 0 Name: Batch (Day) Phoe No. IT IS NOT ENOUGH TO HAVE A GOOD MIND, THE MAIN THING IS TO USE IT WELL Marks:
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 περισσότεραThree Classical Tests; Wald, LM(Score), and LR tests
Eco 60 Three Classical Tests; Wald, MScore, ad R tests Suppose that we have the desity l y; θ of a model with the ull hypothesis of the form H 0 ; θ θ 0. et θ be the lo-likelihood fuctio of the model ad
Διαβάστε περισσότεραα β
6. Eerg, Mometum coefficiets for differet velocit distributios Rehbock obtaied ) For Liear Velocit Distributio α + ε Vmax { } Vmax ε β +, i which ε v V o Give: α + ε > ε ( α ) Liear velocit distributio
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 περισσότερα1 1 1 2 1 2 2 1 43 123 5 122 3 1 312 1 1 122 1 1 1 1 6 1 7 1 6 1 7 1 3 4 2 312 43 4 3 3 1 1 4 1 1 52 122 54 124 8 1 3 1 1 1 1 1 152 1 1 1 1 1 1 152 1 5 1 152 152 1 1 3 9 1 159 9 13 4 5 1 122 1 4 122 5
Διαβάστε περισσότεραCS 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 περισσότεραAdvanced Statistics. Chen, L.-A. Distribution of order statistics: Review : Let X 1,..., X k be random variables with joint p.d.f f(x 1,...
Avace Statistics Che, L.-A. Distributio of orer statistics: Review : Let X,..., X k be raom variables with joit p..f f(x,..., x k a Y h (X,..., X k, Y h (X,..., X k,..., Y k h k (X,..., X k be - trasformatio
Διαβάστε περισσότεραIIT JEE (2013) (Trigonomtery 1) Solutions
L.K. Gupta (Mathematic Classes) www.pioeermathematics.com MOBILE: 985577, 677 (+) PAPER B IIT JEE (0) (Trigoomtery ) Solutios TOWARDS IIT JEE IS NOT A JOURNEY, IT S A BATTLE, ONLY THE TOUGHEST WILL SURVIVE
Διαβάστε περισσότεραSpace-Time Symmetries
Chapter Space-Time Symmetries In classical fiel theory any continuous symmetry of the action generates a conserve current by Noether's proceure. If the Lagrangian is not invariant but only shifts by a
Διαβάστε περισσότεραIntroduction of Numerical Analysis #03 TAGAMI, Daisuke (IMI, Kyushu University)
Itroductio of Numerical Aalysis #03 TAGAMI, Daisuke (IMI, Kyushu Uiversity) web page of the lecture: http://www2.imi.kyushu-u.ac.jp/~tagami/lec/ Strategy of Numerical Simulatios Pheomea Error modelize
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 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 περισσότεραw 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 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 περισσότεραDoes anemia contribute to end-organ dysfunction in ICU patients Statistical Analysis
Does anemia contribute to end-organ dysfunction in ICU patients Statistical Analysis Xue Han, MPH and Matt Shotwell, PhD Department of Biostatistics Vanderbilt University School of Medicine March 14, 2014
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 περισσότεραDESIGN OF MACHINERY SOLUTION MANUAL h in h 4 0.
DESIGN OF MACHINERY SOLUTION MANUAL -7-1! PROBLEM -7 Statement: Design a double-dwell cam to move a follower from to 25 6, dwell for 12, fall 25 and dwell for the remader The total cycle must take 4 sec
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 περισσότεραRepeated measures Επαναληπτικές μετρήσεις
ΠΡΟΒΛΗΜΑ Στο αρχείο δεδομένων diavitis.sav καταγράφεται η ποσότητα γλυκόζης στο αίμα 10 ασθενών στην αρχή της χορήγησης μιας θεραπείας, μετά από ένα μήνα και μετά από δύο μήνες. Μελετήστε την επίδραση
Διαβάστε περισσότεραSpherical Coordinates
Spherical Coordinates MATH 311, Calculus III J. Robert Buchanan Department of Mathematics Fall 2011 Spherical Coordinates Another means of locating points in three-dimensional space is known as the spherical
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 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 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 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 περισσότερα6.1. Dirac Equation. Hamiltonian. Dirac Eq.
6.1. Dirac Equation Ref: M.Kaku, Quantum Field Theory, Oxford Univ Press (1993) η μν = η μν = diag(1, -1, -1, -1) p 0 = p 0 p = p i = -p i p μ p μ = p 0 p 0 + p i p i = E c 2 - p 2 = (m c) 2 H = c p 2
Διαβάστε περισσότεραLifting Entry (continued)
ifting Entry (continued) Basic planar dynamics of motion, again Yet another equilibrium glide Hypersonic phugoid motion Planar state equations MARYAN 1 01 avid. Akin - All rights reserved http://spacecraft.ssl.umd.edu
Διαβάστε περισσότεραΛύσεις Θεµάτων Εξεταστικής Ιανουαρίου 2009 Mάθηµα: «Ψηφιακές Επικοινωνίες» G F = 0.8 T F = 73 0 K
Λύσεις Θεµάτων Εξεταστικής Ιανουαρίου 9 Mάθηµα: «Ψηφιακές Επικοινωνίες» Θέµα 1 ο (3%) A =6 o K P R = 1pWatt SNR IN G LNA =13dB LNA =3 K LNA G F =.8 F = 73 K Φίλτρο G = db F = 8 db Ενισχυτής IF SNR OU 1.
Διαβάστε περισσότεραExercise 2: The form of the generalized likelihood ratio
Stats 2 Winter 28 Homework 9: Solutions Due Friday, March 6 Exercise 2: The form of the generalized likelihood ratio We want to test H : θ Θ against H : θ Θ, and compare the two following rules of rejection:
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 περισσότεραProblem 7.19 Ignoring reflection at the air soil boundary, if the amplitude of a 3-GHz incident wave is 10 V/m at the surface of a wet soil medium, at what depth will it be down to 1 mv/m? Wet soil is
Διαβάστε περισσότεραTable 1: Military Service: Models. Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9 num unemployed mili mili num unemployed
Tables: Military Service Table 1: Military Service: Models Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9 num unemployed mili mili num unemployed mili 0.489-0.014-0.044-0.044-1.469-2.026-2.026
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 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 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 περισσότεραExercises 10. Find a fundamental matrix of the given system of equations. Also find the fundamental matrix Φ(t) satisfying Φ(0) = I. 1.
Exercises 0 More exercises are available in Elementary Differential Equations. If you have a problem to solve any of them, feel free to come to office hour. Problem Find a fundamental matrix of the given
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 περισσότεραΠ 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 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 περισσότεραStatistical analysis of extreme events in a nonstationary context via a Bayesian framework. Case study with peak-over-threshold data
Statistical analysis of extreme events in a nonstationary context via a Bayesian framework. Case study with peak-over-threshold data B. Renard, M. Lang, P. Bois To cite this version: B. Renard, M. Lang,
Διαβάστε περισσότεραForced Pendulum Numerical approach
Numerical approach UiO April 8, 2014 Physical problem and equation We have a pendulum of length l, with mass m. The pendulum is subject to gravitation as well as both a forcing and linear resistance force.
Διαβάστε περισσότεραIterated trilinear fourier integrals with arbitrary symbols
Cornell University ICM 04, Satellite Conference in Harmonic Analysis, Chosun University, Gwangju, Korea August 6, 04 Motivation the Coifman-Meyer theorem with classical paraproduct(979) B(f, f )(x) :=
Διαβάστε περισσότεραD Alembert s Solution to the Wave Equation
D Alembert s Solution to the Wave Equation MATH 467 Partial Differential Equations J. Robert Buchanan Department of Mathematics Fall 2018 Objectives In this lesson we will learn: a change of variable technique
Διαβάστε περισσότεραEE512: Error Control Coding
EE512: Error Control Coding Solution for Assignment on Finite Fields February 16, 2007 1. (a) Addition and Multiplication tables for GF (5) and GF (7) are shown in Tables 1 and 2. + 0 1 2 3 4 0 0 1 2 3
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 περισσότεραInverse 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 περισσότεραΠανεπιστήµιο 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 περισσότεραLifting Entry 2. Basic planar dynamics of motion, again Yet another equilibrium glide Hypersonic phugoid motion MARYLAND U N I V E R S I T Y O F
ifting Entry Basic planar dynamics of motion, again Yet another equilibrium glide Hypersonic phugoid motion MARYAN 1 010 avid. Akin - All rights reserved http://spacecraft.ssl.umd.edu ifting Atmospheric
Διαβάστε περισσότεραRapid Acquisitio n of Doppler Shift in Satellite Co mmunicatio ns
7 3 7 ATA ELETRONIA SINIA Vol. 31 No. 7 July 3 1, 1, (1., 184 ;., 444) :., PN,.,,. : ; ; ; : TN9 : A : 3711 (3) 7155 Rapid Acquisitio of Doppler Shift i Satellite o mmuicatio s HUAN Zhe 1,LU Jiahua 1,YAN
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 περισσότεραMATHEMATICS. 1. If A and B are square matrices of order 3 such that A = -1, B =3, then 3AB = 1) -9 2) -27 3) -81 4) 81
1. If A and B are square matrices of order 3 such that A = -1, B =3, then 3AB = 1) -9 2) -27 3) -81 4) 81 We know that KA = A If A is n th Order 3AB =3 3 A. B = 27 1 3 = 81 3 2. If A= 2 1 0 0 2 1 then
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 περισσότεραNumerical Analysis FMN011
Numerical Analysis FMN011 Carmen Arévalo Lund University carmen@maths.lth.se Lecture 12 Periodic data A function g has period P if g(x + P ) = g(x) Model: Trigonometric polynomial of order M T M (x) =
Διαβάστε περισσότεραQueensland University of Technology Transport Data Analysis and Modeling Methodologies
Queensland University of Technology Transport Data Analysis and Modeling Methodologies Lab Session #7 Example 5.2 (with 3SLS Extensions) Seemingly Unrelated Regression Estimation and 3SLS A survey of 206
Διαβάστε περισσότεραThe Probabilistic Method - Probabilistic Techniques. Lecture 7: The Janson Inequality
The Probabilistic Method - Probabilistic Techniques Lecture 7: The Janson Inequality Sotiris Nikoletseas Associate Professor Computer Engineering and Informatics Department 2014-2015 Sotiris Nikoletseas,
Διαβάστε περισσότεραJesse Maassen and Mark Lundstrom Purdue University November 25, 2013
Notes on Average Scattering imes and Hall Factors Jesse Maassen and Mar Lundstrom Purdue University November 5, 13 I. Introduction 1 II. Solution of the BE 1 III. Exercises: Woring out average scattering
Διαβάστε περισσότεραΚΥΠΡΙΑΚΗ ΕΤΑΙΡΕΙΑ ΠΛΗΡΟΦΟΡΙΚΗΣ CYPRUS COMPUTER SOCIETY ΠΑΓΚΥΠΡΙΟΣ ΜΑΘΗΤΙΚΟΣ ΔΙΑΓΩΝΙΣΜΟΣ ΠΛΗΡΟΦΟΡΙΚΗΣ 24/3/2007
Οδηγίες: Να απαντηθούν όλες οι ερωτήσεις. Όλοι οι αριθμοί που αναφέρονται σε όλα τα ερωτήματα μικρότεροι του 10000 εκτός αν ορίζεται διαφορετικά στη διατύπωση του προβλήματος. Αν κάπου κάνετε κάποιες υποθέσεις
Διαβάστε περισσότερα4.6 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: Monte Carlo EM 313, Louis (1982) EM, EM Newton-Raphson, /. EM, 2 Monte Carlo EM Newton-Raphson, Monte Carlo EM, Monte Carlo EM, /. 3, Monte Carlo EM
2008 6 Chinese Journal of Applied Probability and Statistics Vol.24 No.3 Jun. 2008 Monte Carlo EM 1,2 ( 1,, 200241; 2,, 310018) EM, E,,. Monte Carlo EM, EM E Monte Carlo,. EM, Monte Carlo EM,,,,. Newton-Raphson.
Διαβάστε περισσότεραLecture 3: Asymptotic Normality of M-estimators
Lecture 3: Asymptotic Istructor: Departmet of Ecoomics Staford Uiversity Prepared by Webo Zhou, Remi Uiversity Refereces Takeshi Amemiya, 1985, Advaced Ecoometrics, Harvard Uiversity Press Newey ad McFadde,
Διαβάστε περισσότερα12. Radon-Nikodym Theorem
Tutorial 12: Radon-Nikodym Theorem 1 12. Radon-Nikodym Theorem In the following, (Ω, F) is an arbitrary measurable space. Definition 96 Let μ and ν be two (possibly complex) measures on (Ω, F). We say
Διαβάστε περισσότεραWeb-based supplementary materials for Bayesian Quantile Regression for Ordinal Longitudinal Data
Web-based supplementary materials for Bayesian Quantile Regression for Ordinal Longitudinal Data Rahim Alhamzawi, Haithem Taha Mohammad Ali Department of Statistics, College of Administration and Economics,
Διαβάστε περισσότεραProbability theory STATISTICAL MODELING OF MULTIVARIATE EXTREMES, FMSN15/MASM23 TABLE OF FORMULÆ. Basic probability theory
Lud Istitute of Techology Cetre for Mathematical Scieces Mathematical Statistics STATISTICAL MODELING OF MULTIVARIATE EXTREMES, FMSN5/MASM3 Probability theory Basic probability theory TABLE OF FORMULÆ
Διαβάστε περισσότεραITU-R BT ITU-R BT ( ) ITU-T J.61 (
ITU-R BT.439- ITU-R BT.439- (26-2). ( ( ( ITU-T J.6 ( ITU-T J.6 ( ( 2 2 2 3 ITU-R BT.439-2 4 3 4 K : 5. ITU-R BT.24 :. ITU-T J.6. : T u ( ) () (S + L = M) :A :B :C : D :E :F :G :H :J :K :L :M :S :Tsy :Tlb
Διαβάστε περισσότερα