Axiomatic Definition of Probability. Problems: Relative Frequency. Event. Sample Space Examples

Size: px
Start display at page:

Download "Axiomatic Definition of Probability. Problems: Relative Frequency. Event. Sample Space Examples"

Transcription

1 Rado Sgals robabl & Rado Varables: Revew M. Sa Fadal roessor o lecrcal geerg Uvers o evada Reo Soe phscal sgals ose cao be epressed as a eplc aheacal orla. These sgals s be descrbed probablsc ers. ose s pcall waed ad o elae we eed o dersad qaavel: we eed probabl heor. Classcal Ive Deo o robabl sse all ocoes are eqall lkel ber o was eve ca occr oal ber o possble ocoes [ ] Relave Freqec Deo o robabl eror a epere es large Co he ber o es ha occrs [ ] 3 4

2 robles: Relave Freqec peres cao be perored a e ver large ber o es sspo ha he relave reqec approaches a cosa l pracce he rao hovers arod a cosa vale oac Deo o robabl ccep a se o aos based o eperece Derve a coplee heor based o aos Rado pere: epere wh odeesc ops. Saple Space: se o ocoes o rado epere s S eleear eves s e coabl e or e 5 6 Saple Space aples pere. Sgle draw ro 5-card deck 5 possble ocoes 5 elees S pere. Vecor Ocoe Throw o wo ar dce observe ber o each de possble ocoes 36 elees S each a -ple ve sse elees o saple space are dso. ve sbse o saple space sbec o cosras e.g. {S 34} S {S 34 5} ves eed o be dso ve Space : se o sbses o S called eves sch ha o erseco coplee o a eve s a eve called sga eld or sga algebra e o s o clded. 8

3 robabl Space Dscree roble: e or coabl e ocoes coplcaos or coos case wh coabl e ocoes. robabl Space: - Saple space - ve space 3- robabl easre os o robabl S U { } dso { } e or coabl e 9 Se Operaos robabl Theor Uo Ierseco Coplee c + aple: Throw o De Saple Space S {3456} 6 elees ve Se {S {} {6}{} {6}.} ower Se o S

4 4 3 Margal robabl Wre arg osde b arra or Codoal robabl robabl o eve gve ha eve has occrred.e. cera eve o deed or possble. Deo agrees wh relave reqec oo. oal ocoes wh oal ocoeswhboh ad 5 aes Theore... all eclsve eves 6 Idepedece Occrrece o oe eve does o aec he lkelhood o he oher

5 Mlple Rado Varables Mlvarae: vecor o rado varables varae: varables Dscree case: o prob. -d. arra Oba argal prob. b addg col. or row p Y Y robabl Dsrbo Fco Deo: The probabl dsrbo co DF o a rado varable s a co deed or each real ber as ollows F < < 7 8 roperes o he DF. F as. F as 3. F s a odecreasg co o. robabl Des Fco For a coos rado varable here ess a oegave co deed o he real le sch ha or ever sbse ab] o he real le he probabl ha akes a vale. The co s called he probabl des co pd a < b d b a 9 5

6 roperes o he pd.. 3. d. df d b a Uor Dsrbo a b elsewhere a b b a robabl o vale a sberval o [ab] s proporoal o s legh. rea o recagle s be. pecao o a Rado Varable peced vale or ea o. d Fco o a Rado Varable Oba he epeced vale o he co sg g g d 3 4 6

7 Moes k h oe Frs oe s he ea k k k k d Varace Secod oe abo he ea Var Var Var { { } } { } { } d 5 6 roperes o he Varace Var a + b a Var Var Var { } { } Var depede Φ oral or Gassa Des [ σ ] ep e d σ Ipora pd becase: Fs a phscal pheoea. Ceral l heore. Copleel descrbed b ea ad varace

8 Rgh Tal robabl robabl o eceedg a gve vale. Copleear clave dsrbo. Q e Φ d Iverse Q s ooocall decreasg ad hece verble. Iverse s pora sgal deeco. Q γ γ Q γ e F d F 9 3 Q rror Fco er erc Φ.5 Q.5 e e d [ + er ] erc erv d er MT slar or Maple >> er% rror co >> erc % Copleear error co >>.5*+ersqr % S. oral < >>.5*ercsqr % S. oral > >> sqr*erv-* % Iverse Q 3 3 8

9 9 33 Ch-Sqare Dsrbo Ceral eger! ~ d e e Y Γ < > Γ degrees o reedo 34 Ch-Sqare oceral degrees o reedo [ ] + Γ < > + λ λ λ λ 4 s cos ep ep ~ d r I I r r r Y 35 Rca Dsrbo θ θ α σ α σ α σ σ d I I + < > + + cos ep ep ~ 36 Mlvarae Dsrbos b vecor < < d F

10 37 Margal Dsrbos Margal pd + d d d d 38 Codoal Dsrbo For codoal des gve ha 39 Correlao ad Covarace Geeralzao o secod oe ad varace o vecor case. { } [ ][ ] { } T T R C C R 4 Mlvarae oral Geeralzao o oral dsrbo o learl depede rado varables. [ ] [ ] T C C de ep

11 Trasorao o Rado Varables g T [ ] [ ] T Wha s he pd o he vecor? How ca we oba ro he pd o? rocedre Solve or a I o solo ess he b I oe or ore solos es g de de [ ] [ ] 4 4 Trasorao o oral RV oral s los he rasorao s olear. oral s preserved he rasorao s lear. T ep C [ ] de C + b b T ~ + b C Reereces row & Hwag Sark & Woods Gra & Davsso M. H. De Groo M. J. Schervsh robabl & Sascs ddso-wesle oso. Ka Fdaeals o Sascal Sgal rocessg: Deeco Theor rece Hall R. D. Hppesel Deeco Theor CRC ress oca Rao

Final Exam Applied Econometrics

Final Exam Applied Econometrics Fal Eam Appled Ecoomercs. 0 Sppose we have he followg regresso resl: Depede Varable: SAT Sample: 437 Iclded observaos: 437 Whe heeroskedasc-cosse sadard errors & covarace Varable Coeffce Sd. Error -Sasc

More information

Solution to Some Open Problems on E-super Vertex Magic Total Labeling of Graphs

Solution to Some Open Problems on E-super Vertex Magic Total Labeling of Graphs Aalable a hp://paed/aa Appl Appl Mah ISS: 9-9466 Vol 0 Isse (Deceber 0) pp 04- Applcaos ad Appled Maheacs: A Ieraoal Joral (AAM) Solo o Soe Ope Probles o E-sper Verex Magc Toal Labelg o Graphs G Marh MS

More information

NUMERICAL EVALUATION of DYNAMIC RESPONSE

NUMERICAL EVALUATION of DYNAMIC RESPONSE NUMERICAL EVALUATION of YNAMIC RESPONSE Aalycal solo of he eqao of oo for a sgle degree of freedo syse s sally o ossble f he excao aled force or grod accelerao ü g -vares arbrarly h e or f he syse s olear.

More information

P-Convexity Property in Musielak-Orlicz Function Space of Bohner Type

P-Convexity Property in Musielak-Orlicz Function Space of Bohner Type J N Sce & Mh Res Vol 3 No (7) -7 Alble ole h://orlwlsogocd/deh/sr P-Coey Proery Msel-Orlcz Fco Sce o Boher ye Yl Rodsr Mhecs Edco Deree Fcly o Ss d echology Uerss sl Neger Wlsogo Cerl Jdoes Absrcs Corresodg

More information

Some probability inequalities for multivariate gamma and normal distributions. Abstract

Some probability inequalities for multivariate gamma and normal distributions. Abstract -- Soe probably equales for ulvarae gaa ad oral dsrbuos Thoas oye Uversy of appled sceces Bge, Berlsrasse 9, D-554 Bge, Geray, e-al: hoas.roye@-ole.de Absrac The Gaussa correlao equaly for ulvarae zero-ea

More information

Least Squares Fitting (LSQF) with a complicated function Theexampleswehavelookedatsofarhavebeenlinearintheparameters

Least Squares Fitting (LSQF) with a complicated function Theexampleswehavelookedatsofarhavebeenlinearintheparameters Leas Squares Fg LSQF wh a complcaed fuco Theeampleswehavelookedasofarhavebeelearheparameers ha we have bee rg o deerme e.g. slope, ercep. For he case where he fuco s lear he parameers we ca fd a aalc soluo

More information

Integral Form of Popoviciu Inequality for Convex Function

Integral Form of Popoviciu Inequality for Convex Function Procees of e Paksa Acaey of Sceces: A. Pyscal a ozaoal Sceces 53 3: 339 348 206 oyr Paksa Acaey of Sceces ISSN: 258-4245 r 258-4253 ole Paksa Acaey of Sceces Researc Arcle Ieral For of Pooc Ieqaly for

More information

Fault Tolerant Computing. Fault Tolerant Computing CS 530 Probabilistic methods: overview

Fault Tolerant Computing. Fault Tolerant Computing CS 530 Probabilistic methods: overview Probably 1/19/ CS 53 Probablsc mehods: overvew Yashwa K. Malaya Colorado Sae Uversy 1 Probablsc Mehods: Overvew Cocree umbers presece of uceray Probably Dsjo eves Sascal depedece Radom varables ad dsrbuos

More information

Chapter 1 - Free Vibration of Multi-Degree-of-Freedom Systems - I

Chapter 1 - Free Vibration of Multi-Degree-of-Freedom Systems - I CEE49b Chaper - Free Vbrao of M-Degree-of-Freedo Syses - I Free Udaped Vbrao The basc ype of respose of -degree-of-freedo syses s free daped vbrao Aaogos o sge degree of freedo syses he aayss of free vbrao

More information

Lecture 2: The Simple Regression Model

Lecture 2: The Simple Regression Model Lectre Notes o Advaced coometrcs Lectre : The Smple Regresso Model Takash Yamao Fall Semester 5 I ths lectre we revew the smple bvarate lear regresso model. We focs o statstcal assmptos to obta based estmators.

More information

(1) Cov(, ) E[( E( ))( E( ))]

(1) Cov(, ) E[( E( ))( E( ))] Impac of Auocorrelao o OLS Esmaes ECON 3033/Evas Cosder a smple bvarae me-seres model of he form: y 0 x The four key assumpos abou ε hs model are ) E(ε ) = E[ε x ]=0 ) Var(ε ) =Var(ε x ) = ) Cov(ε, ε )

More information

Asymptotic Behavior of Solutions of Nonlinear Delay Differential Equations With Impulse

Asymptotic Behavior of Solutions of Nonlinear Delay Differential Equations With Impulse P a g e Vol Issue7Ver,oveber Global Joural of Scece Froer Research Asypoc Behavor of Soluos of olear Delay Dffereal Equaos Wh Ipulse Zhag xog GJSFR Classfcao - F FOR 3 Absrac Ths paper sudes he asypoc

More information

EE 6885 Statistical Pattern Recognition

EE 6885 Statistical Pattern Recognition EE 6885 Sascal Paer Recogo Fall 005 Prof. Shh-Fu Chag hp://www.ee.columba.edu/~sfchag Lecure 5 (9//05 4- Readg Model Parameer Esmao ML Esmao, Chap. 3. Mure of Gaussa ad EM Referece Boo, HTF Chap. 8.5 Teboo,

More information

θ = θ Π Π Parametric counting process models θ θ θ Log-likelihood: Consider counting processes: Score functions:

θ = θ Π Π Parametric counting process models θ θ θ Log-likelihood: Consider counting processes: Score functions: Paramerc coug process models Cosder coug processes: N,,..., ha cou he occurreces of a eve of eres for dvduals Iesy processes: Lelhood λ ( ;,,..., N { } λ < Log-lelhood: l( log L( Score fucos: U ( l( log

More information

ELEC 6041 LECTURE NOTES WEEK 3 Dr. Amir G. Aghdam Concordia University

ELEC 6041 LECTURE NOTES WEEK 3 Dr. Amir G. Aghdam Concordia University ecre Noe Prepared b r G. ghda EE 64 ETUE NTE WEE r. r G. ghda ocorda Uer eceraled orol e - Whe corol heor appled o a e ha co of geographcall eparaed copoe or a e cog of a large ber of p-op ao ofe dered

More information

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

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

More information

Lecture Notes 2. The ability to manipulate matrices is critical in economics.

Lecture Notes 2. The ability to manipulate matrices is critical in economics. Lecture Notes. Revew of Matrces he ablt to mapulate matrces s crtcal ecoomcs.. Matr a rectagular arra of umbers, parameters, or varables placed rows ad colums. Matrces are assocated wth lear equatos. lemets

More information

AML710 CAD LECTURE 12 CUBIC SPLINE CURVES. Cubic Splines Matrix formulation Normalised cubic splines Alternate end conditions Parabolic blending

AML710 CAD LECTURE 12 CUBIC SPLINE CURVES. Cubic Splines Matrix formulation Normalised cubic splines Alternate end conditions Parabolic blending CUIC SLINE CURVES Cubc Sples Marx formulao Normalsed cubc sples Alerae ed codos arabolc bledg AML7 CAD LECTURE CUIC SLINE The ame sple comes from he physcal srume sple drafsme use o produce curves A geeral

More information

The Linear Regression Of Weighted Segments

The Linear Regression Of Weighted Segments The Lear Regresso Of Weghed Segmes George Dael Maeescu Absrac. We proposed a regresso model where he depede varable s made o up of pos bu segmes. Ths suao correspods o he markes hroughou he da are observed

More information

Geometric Modeling

Geometric Modeling Geomerc Modelg 9.58. Crves coed Cc Bezer ad B-Sle Crves Far Chaers 4-5 8 Moreso Chaers 4 5 4 Tycal Tyes of Paramerc Crves Corol os flece crve shae. Ierolag Crve asses hrogh all corol os. Herme Defed y

More information

4. THE DENSITY MATRIX

4. THE DENSITY MATRIX 4. THE DENSTY MATRX The desy marx or desy operaor s a alerae represeao of he sae of a quaum sysem for whch we have prevously used he wavefuco. Alhough descrbg a quaum sysem wh he desy marx s equvale o

More information

ONE APPROACH FOR THE OPTIMIZATION OF ESTIMATES CALCULATING ALGORITHMS A.A. Dokukin

ONE APPROACH FOR THE OPTIMIZATION OF ESTIMATES CALCULATING ALGORITHMS A.A. Dokukin Iero Jor "Iforo Theore & co" Vo 463 ONE PPROH FOR THE OPTIIZTION OF ETITE UTING GORITH Do rc: I h rce he ew roch for ozo of eo ccg gorh ggeed I c e ed for fdg he correc gorh of coexy he coex of gerc roch

More information

( ) ( ) ( ( )) ( ) ( ) ( ) ( ) ( ) = ( ) ( ) + ( ) ( ) = ( ( )) ( ) + ( ( )) ( ) Review. Second Derivatives for f : y R. Let A be an m n matrix.

( ) ( ) ( ( )) ( ) ( ) ( ) ( ) ( ) = ( ) ( ) + ( ) ( ) = ( ( )) ( ) + ( ( )) ( ) Review. Second Derivatives for f : y R. Let A be an m n matrix. Revew + v, + y = v, + v, + y, + y, Cato! v, + y, + v, + y geeral Let A be a atr Let f,g : Ω R ( ) ( ) R y R Ω R h( ) f ( ) g ( ) ( ) ( ) ( ( )) ( ) dh = f dg + g df A, y y A Ay = = r= c= =, : Ω R he Proof

More information

Chapter 6 DETECTION AND ESTIMATION: Model of digital communication system. Fundamental issues in digital communications are

Chapter 6 DETECTION AND ESTIMATION: Model of digital communication system. Fundamental issues in digital communications are Chaper 6 DCIO AD IMAIO: Fndaenal sses n dgal concaons are. Deecon and. saon Deecon heory: I deals wh he desgn and evalaon of decson ang processor ha observes he receved sgnal and gesses whch parclar sybol

More information

Continuous Random Variables: Conditioning, Expectation and Independence

Continuous Random Variables: Conditioning, Expectation and Independence Cotuous Radom Varables: Codtog, xectato ad Ideedece Berl Che Deartmet o Comuter cece & Iormato geerg atoal Tawa ormal Uverst Reerece: - D.. Bertsekas, J.. Tstskls, Itroducto to robablt, ectos 3.4-3.5 Codtog

More information

Some Probability Inequalities for Quadratic Forms of Negatively Dependent Subgaussian Random Variables

Some Probability Inequalities for Quadratic Forms of Negatively Dependent Subgaussian Random Variables Joural of Sceces Islamc epublc of Ira 6(: 63-67 (005 Uvers of ehra ISSN 06-04 hp://scecesuacr Some Probabl Iequales for Quadrac Forms of Negavel Depede Subgaussa adom Varables M Am A ozorga ad H Zare 3

More information

Two-Dimensional Quantum Harmonic Oscillator

Two-Dimensional Quantum Harmonic Oscillator D Qa Haroc Oscllaor Two-Dsoal Qa Haroc Oscllaor 6 Qa Mchacs Prof. Y. F. Ch D Qa Haroc Oscllaor D Qa Haroc Oscllaor ch5 Schrödgr cosrcd h cohr sa of h D H.O. o dscrb a classcal arcl wh a wav ack whos cr

More information

VISCOSITY APPROXIMATION TO COMMON FIXED POINTS OF kn- LIPSCHITZIAN NONEXPANSIVE MAPPINGS IN BANACH SPACES

VISCOSITY APPROXIMATION TO COMMON FIXED POINTS OF kn- LIPSCHITZIAN NONEXPANSIVE MAPPINGS IN BANACH SPACES Joral o Maheaical Scieces: Advaces ad Alicaios Vole Nber 9 Pages -35 VISCOSIY APPROXIMAION O COMMON FIXED POINS OF - LIPSCHIZIAN NONEXPANSIVE MAPPINGS IN BANACH SPACES HONGLIANG ZUO ad MIN YANG Deare o

More information

NUMERICAL SOLUTIONS TO ORDINARY DIFFERENTIAL EQUATIONS

NUMERICAL SOLUTIONS TO ORDINARY DIFFERENTIAL EQUATIONS NUMERICAL SOLUTIONS TO ORDINARY DIFFERENTIAL EQUATIONS If e eqao coas dervaves of a - order s sad o be a - order dffereal eqao. For eample a secod-order eqao descrbg e oscllao of a weg aced po b a sprg

More information

Processamento Digital de Sinal

Processamento Digital de Sinal Deparaeo de Elecróica e Telecouicações da Uiversidade de Aveiro Processaeo Digial de ial Processos Esocásicos uar ado Processes aioar ad ergodic Correlaio auo ad cross Fucio Covariace Fucio Esiaes of he

More information

Brownian Motion and Stochastic Calculus. Brownian Motion and Stochastic Calculus

Brownian Motion and Stochastic Calculus. Brownian Motion and Stochastic Calculus Browa Moo Sochasc Calculus Xogzh Che Uversy of Hawa a Maoa earme of Mahemacs Seember, 8 Absrac Ths oe s abou oob decomoso he bascs of Suare egrable margales Coes oob-meyer ecomoso Suare Iegrable Margales

More information

Generalized Linear Models. Statistical Models. Classical Linear Regression Why easy formulation if complicated formulation exists?

Generalized Linear Models. Statistical Models. Classical Linear Regression Why easy formulation if complicated formulation exists? Statstcal Models Geeralzed Lear Models Classcal lear regresso complcated formlato of smple model, strctral ad radom compoet of the model Lectre 5 Geeralzed Lear Models Geeralzed lear models geeral descrpto

More information

Chapter 2. Review of Hydrodynamics and Vector Analysis

Chapter 2. Review of Hydrodynamics and Vector Analysis her. Ree o Hdrodmcs d Vecor Alss. Tlor seres L L L L ' ' L L " " " M L L! " ' L " ' I s o he c e romed he Tlor seres. O he oher hd ' " L . osero o mss -dreco: L L IN ] OUT [mss l [mss l] mss ccmled h me

More information

Dimension Reduction. Curse of dimensionality

Dimension Reduction. Curse of dimensionality Deso Reuco Deso Reuco Curse of esoaly h 5 feaures esos, each quaze o levels, creae 5 possble feaure cobaos, age ho ay saples you ee o esae p? ho o you vsualze he srucure a 5 esoal space? Oher probles ze

More information

14. Poisson Processes

14. Poisson Processes 4. Posso Processes I Lecure 4 we roduced Posso arrvals as he lmg behavor of Bomal radom varables. Refer o Posso approxmao of Bomal radom varables. From he dscusso here see 4-6-4-8 Lecure 4 " arrvals occur

More information

Some Different Perspectives on Linear Least Squares

Some Different Perspectives on Linear Least Squares Soe Dfferet Perspectves o Lear Least Squares A stadard proble statstcs s to easure a respose or depedet varable, y, at fed values of oe or ore depedet varables. Soetes there ests a deterstc odel y f (,,

More information

Solution set Stat 471/Spring 06. Homework 2

Solution set Stat 471/Spring 06. Homework 2 oluo se a 47/prg 06 Homework a Whe he upper ragular elemes are suppressed due o smmer b Le Y Y Y Y A weep o he frs colum o oba: A ˆ b chagg he oao eg ad ec YY weep o he secod colum o oba: Aˆ YY weep o

More information

Suppose we have observed values t 1, t 2, t n of a random variable T.

Suppose we have observed values t 1, t 2, t n of a random variable T. Sppose we have obseved vales, 2, of a adom vaable T. The dsbo of T s ow o belog o a cea ype (e.g., expoeal, omal, ec.) b he veco θ ( θ, θ2, θp ) of ow paamees assocaed wh s ow (whee p s he mbe of ow paamees).

More information

Midterm Exam. Tuesday, September hour, 15 minutes

Midterm Exam. Tuesday, September hour, 15 minutes Ecoomcs of Growh, ECON560 Sa Fracsco Sae Uvers Mchael Bar Fall 203 Mderm Exam Tuesda, Sepember 24 hour, 5 mues Name: Isrucos. Ths s closed boo, closed oes exam. 2. No calculaors of a d are allowed. 3.

More information

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

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

More information

The Poisson Process Properties of the Poisson Process

The Poisson Process Properties of the Poisson Process Posso Processes Summary The Posso Process Properes of he Posso Process Ierarrval mes Memoryless propery ad he resdual lfeme paradox Superposo of Posso processes Radom seleco of Posso Pos Bulk Arrvals ad

More information

Density estimation III.

Density estimation III. Lecure 6 esy esmao III. Mlos Hausrec mlos@cs..eu 539 Seo Square Oule Oule: esy esmao: Bomal srbuo Mulomal srbuo ormal srbuo Eoeal famly aa: esy esmao {.. } a vecor of arbue values Objecve: ry o esmae e

More information

Continuous Time Markov Chains

Continuous Time Markov Chains Couous me Markov chas have seay sae probably soluos f a oly f hey are ergoc, us lke scree me Markov chas. Fg he seay sae probably vecor for a couous me Markov cha s o more ffcul ha s he scree me case,

More information

Algorithms behind the Correlation Setting Window

Algorithms behind the Correlation Setting Window Algorths behd the Correlato Settg Wdow Itroducto I ths report detaled forato about the correlato settg pop up wdow s gve. See Fgure. Ths wdow s obtaed b clckg o the rado butto labelled Kow dep the a scree

More information

Fundamentals of Speech Recognition Suggested Project The Hidden Markov Model

Fundamentals of Speech Recognition Suggested Project The Hidden Markov Model . Projec Iroduco Fudameals of Speech Recogo Suggesed Projec The Hdde Markov Model For hs projec, s proposed ha you desg ad mpleme a hdde Markov model (HMM) ha opmally maches he behavor of a se of rag sequeces

More information

P a g e 3 6 of R e p o r t P B 4 / 0 9

P a g e 3 6 of R e p o r t P B 4 / 0 9 P a g e 3 6 of R e p o r t P B 4 / 0 9 p r o t e c t h um a n h e a l t h a n d p r o p e r t y fr om t h e d a n g e rs i n h e r e n t i n m i n i n g o p e r a t i o n s s u c h a s a q u a r r y. J

More information

Solution. The straightforward approach is surprisingly difficult because one has to be careful about the limits.

Solution. The straightforward approach is surprisingly difficult because one has to be careful about the limits. ose ad Varably Homewor # (8), aswers Q: Power spera of some smple oses A Posso ose A Posso ose () s a sequee of dela-fuo pulses, eah ourrg depedely, a some rae r (More formally, s a sum of pulses of wdh

More information

Calibration Approach Based Estimators of Finite Population Mean in Two - Stage Stratified Random Sampling

Calibration Approach Based Estimators of Finite Population Mean in Two - Stage Stratified Random Sampling I.J.Curr.crobol.App.Sc (08) 7(): 808-85 Ieraoal Joural of Curre crobolog ad Appled Scece ISS: 39-7706 olue 7 uber 0 (08) Joural hoepage: hp://www.jca.co Orgal Reearch Arcle hp://do.org/0.0546/jca.08.70.9

More information

Machine Learning. Introduction to Regression. Lecture 3, September 19, Reading: Chap. 3, CB

Machine Learning. Introduction to Regression. Lecture 3, September 19, Reading: Chap. 3, CB ache Learg 0-70/5 70/5-78 78 all 006 Iroduco o Regresso Erc g Lecure 3 Sepember 9 006 Readg: Chap. 3 C Iferece wh he Jo Compue Codoals 0.4 0. P lu eadhead P lu eadhead P eadhead 0.7 0. 0.05 0.05 0.05 0.05

More information

J i-1 i. J i i+1. Numerical integration of the diffusion equation (I) Finite difference method. Spatial Discretization. Internal nodes.

J i-1 i. J i i+1. Numerical integration of the diffusion equation (I) Finite difference method. Spatial Discretization. Internal nodes. umercal negraon of he dffuson equaon (I) Fne dfference mehod. Spaal screaon. Inernal nodes. R L V For hermal conducon le s dscree he spaal doman no small fne spans, =,,: Balance of parcles for an nernal

More information

Theory study about quarter-wave-stack dielectric mirrors

Theory study about quarter-wave-stack dielectric mirrors Theor tud about quarter-wave-tack delectrc rror Stratfed edu tratted reflected reflected Stratfed edu tratted cdet cdet T T Frt, coder a wave roagato a tratfed edu. A we kow, a arbtrarl olared lae wave

More information

International Journal of Scientific & Engineering Research, Volume 3, Issue 10, October ISSN

International Journal of Scientific & Engineering Research, Volume 3, Issue 10, October ISSN Ieraoal Joural of cefc & Egeerg Research, Volue, Issue 0, Ocober-0 The eady-ae oluo Of eral hael Wh Feedback Ad Reegg oeced Wh o-eral Queug Processes Wh Reegg Ad Balkg ayabr gh* ad Dr a gh** *Assoc Prof

More information

Linear Regression Linear Regression with Shrinkage

Linear Regression Linear Regression with Shrinkage Lear Regresso Lear Regresso h Shrkage Iroduco Regresso meas predcg a couous (usuall scalar oupu from a vecor of couous pus (feaures x. Example: Predcg vehcle fuel effcec (mpg from 8 arbues: Lear Regresso

More information

Numerical methods for ordinary differential equations

Numerical methods for ordinary differential equations A Maser Scieiic copuig Nuerical ehos or oriar iereial equaios Oriar iereial equaios Le be a ocio o oe variable havig successive erivaives. A h orer oriar iereial equaio ODE is a equaio o he or: A s orer

More information

Increasing the Image Quality of Atomic Force Microscope by Using Improved Double Tapered Micro Cantilever

Increasing the Image Quality of Atomic Force Microscope by Using Improved Double Tapered Micro Cantilever Rece Reseaces Teecocaos foacs Eecocs a Sga Pocessg ceasg e age Qa of oc Foce Mcope Usg pove oe Tapee Mco aeve Saeg epae of Mecaca Egeeg aava Bac sac za Uves aava Tea a a_saeg@aavaa.ac. sac: Te esoa feqec

More information

The Mean Residual Lifetime of (n k + 1)-out-of-n Systems in Discrete Setting

The Mean Residual Lifetime of (n k + 1)-out-of-n Systems in Discrete Setting Appled Mahemacs 4 5 466-477 Publshed Ole February 4 (hp//wwwscrporg/oural/am hp//dxdoorg/436/am45346 The Mea Resdual Lfeme of ( + -ou-of- Sysems Dscree Seg Maryam Torab Sahboom Deparme of Sascs Scece ad

More information

Generative classification models

Generative classification models CS 75 Mache Learg Lecture Geeratve classfcato models Mlos Hauskrecht mlos@cs.ptt.edu 539 Seott Square Data: D { d, d,.., d} d, Classfcato represets a dscrete class value Goal: lear f : X Y Bar classfcato

More information

The conditional density p(x s ) Bayes rule explained. Bayes rule for a classification problem INF

The conditional density p(x s ) Bayes rule explained. Bayes rule for a classification problem INF INF 4300 04 Mulvarae clafcao Ae Solberg ae@fuoo Baed o Chaper -6 Duda ad Har: Paer Clafcao Baye rule for a clafcao proble Suppoe we have J, =,J clae he cla label for a pel, ad he oberved feaure vecor We

More information

Analysis of a Stochastic Lotka-Volterra Competitive System with Distributed Delays

Analysis of a Stochastic Lotka-Volterra Competitive System with Distributed Delays Ieraoal Coferece o Appled Maheac Sulao ad Modellg (AMSM 6) Aaly of a Sochac Loa-Volerra Copeve Sye wh Drbued Delay Xagu Da ad Xaou L School of Maheacal Scece of Togre Uvery Togre 5543 PR Cha Correpodg

More information

Statistics. Correlational. Dr. Ayman Eldeib. Simple Linear Regression and Correlation. SBE 304: Linear Regression & Correlation 1/3/2018

Statistics. Correlational. Dr. Ayman Eldeib. Simple Linear Regression and Correlation. SBE 304: Linear Regression & Correlation 1/3/2018 /3/08 Sstems & Bomedcal Egeerg Departmet SBE 304: Bo-Statstcs Smple Lear Regresso ad Correlato Dr. Ama Eldeb Fall 07 Descrptve Orgasg, summarsg & descrbg data Statstcs Correlatoal Relatoshps Iferetal Geeralsg

More information

A New Algorithm for Solving Coupled. Schrödinger KdV Equation: An Application. of the Fourier Transform Adomian. Decomposition Method

A New Algorithm for Solving Coupled. Schrödinger KdV Equation: An Application. of the Fourier Transform Adomian. Decomposition Method . Ses Theor. Phys. Vol. 8 o. 8 57-6 HIKRI.-hkar.o hp://.o.or/.988/asp..6 e lorh for Sol ople Shröer KV Eqao: pplao of he orer Trasfor oa Deoposo Meho reshr ha Sahareh Depare of Mehaal Eeer Soh Tehra rah

More information

Chapter 4: Linear Momentum and Collisions

Chapter 4: Linear Momentum and Collisions Chater 4: Lear oetu ad Collsos 4.. The Ceter o ass, Newto s Secod Law or a Syste o artcles 4.. Lear oetu ad Its Coserato 4.3. Collso ad Iulse 4.4. oetu ad Ketc Eergy Collsos 4.. The Ceter o ass. Newto

More information

8. Queueing systems lect08.ppt S Introduction to Teletraffic Theory - Fall

8. Queueing systems lect08.ppt S Introduction to Teletraffic Theory - Fall 8. Queueg sysems lec8. S-38.45 - Iroduco o Teleraffc Theory - Fall 8. Queueg sysems Coes Refresher: Smle eleraffc model M/M/ server wag laces M/M/ servers wag laces 8. Queueg sysems Smle eleraffc model

More information

9.1 Introduction to the probit and logit models

9.1 Introduction to the probit and logit models EC3000 Ecoometrcs Lecture 9 Probt & Logt Aalss 9. Itroducto to the probt ad logt models 9. The logt model 9.3 The probt model Appedx 9. Itroducto to the probt ad logt models These models are used regressos

More information

IMPROVED PORTFOLIO OPTIMIZATION MODEL WITH TRANSACTION COST AND MINIMAL TRANSACTION LOTS

IMPROVED PORTFOLIO OPTIMIZATION MODEL WITH TRANSACTION COST AND MINIMAL TRANSACTION LOTS Vol.7 No.4 (200) p73-78 Joural of Maageme Scece & Sascal Decso IMPROVED PORTFOLIO OPTIMIZATION MODEL WITH TRANSACTION COST AND MINIMAL TRANSACTION LOTS TIANXIANG YAO AND ZAIWU GONG College of Ecoomcs &

More information

International Journal Of Engineering And Computer Science ISSN: Volume 5 Issue 12 Dec. 2016, Page No.

International Journal Of Engineering And Computer Science ISSN: Volume 5 Issue 12 Dec. 2016, Page No. www.jecs. Ieraoal Joural Of Egeerg Ad Compuer Scece ISSN: 19-74 Volume 5 Issue 1 Dec. 16, Page No. 196-1974 Sofware Relably Model whe mulple errors occur a a me cludg a faul correco process K. Harshchadra

More information

A Second Kind Chebyshev Polynomial Approach for the Wave Equation Subject to an Integral Conservation Condition

A Second Kind Chebyshev Polynomial Approach for the Wave Equation Subject to an Integral Conservation Condition SSN 76-7659 Eglad K Joural of forao ad Copug Scece Vol 7 No 3 pp 63-7 A Secod Kd Chebyshev olyoal Approach for he Wave Equao Subec o a egral Coservao Codo Soayeh Nea ad Yadollah rdokha Depare of aheacs

More information

JORIND 9(2) December, ISSN

JORIND 9(2) December, ISSN JORIND 9() December, 011. ISSN 1596 8308. www.rascampus.org., www.ajol.o/jourals/jord THE EXONENTIAL DISTRIBUTION AND THE ALICATION TO MARKOV MODELS Usma Yusu Abubakar Deparme o Mahemacs/Sascs Federal

More information

Chapter 5. Long Waves

Chapter 5. Long Waves ape 5. Lo Waes Wae e s o compaed ae dep: < < L π Fom ea ae eo o s s ; amos ozoa moo z p s ; dosac pesse Dep-aeaed coseao o mass

More information

4. Runge-Kutta Formula For Differential Equations

4. Runge-Kutta Formula For Differential Equations NCTU Deprme o Elecrcl d Compuer Egeerg 5 Sprg Course by Pro. Yo-Pg Ce. Ruge-Ku Formul For Derel Equos To solve e derel equos umerclly e mos useul ormul s clled Ruge-Ku ormul

More information

3/3/2014. CDS M Phil Econometrics. Heteroskedasticity is a problem where the error terms do not have a constant variance.

3/3/2014. CDS M Phil Econometrics. Heteroskedasticity is a problem where the error terms do not have a constant variance. 3/3/4 a Plla N OS Volao of Assmpos Assmpo of Sphercal Dsrbaces Var T T I Var O Cov, j, j,..., Therefore he reqreme for sphercal dsrbaces s ad j I O homoskedascy No aocorrelao Heeroskedascy: Defo Heeroscedascy

More information

ON TOTAL TIME ON TEST TRANSFORM ORDER ABSTRACT

ON TOTAL TIME ON TEST TRANSFORM ORDER ABSTRACT V M Chacko E CONVE AND INCREASIN CONVE OAL IME ON ES RANSORM ORDER R&A # 4 9 Vol. Decembe ON OAL IME ON ES RANSORM ORDER V. M. Chacko Depame of Sascs S. homas Collee hss eala-68 Emal: chackovm@mal.com

More information

Supplement Material for Inverse Probability Weighted Estimation of Local Average Treatment Effects: A Higher Order MSE Expansion

Supplement Material for Inverse Probability Weighted Estimation of Local Average Treatment Effects: A Higher Order MSE Expansion Suppleme Maeral for Iverse Probably Weged Esmao of Local Average Treame Effecs: A Hger Order MSE Expaso Sepe G. Doald Deparme of Ecoomcs Uversy of Texas a Aus Yu-C Hsu Isue of Ecoomcs Academa Sca Rober

More information

Probability Bracket Notation and Probability Modeling. Xing M. Wang Sherman Visual Lab, Sunnyvale, CA 94087, USA. Abstract

Probability Bracket Notation and Probability Modeling. Xing M. Wang Sherman Visual Lab, Sunnyvale, CA 94087, USA. Abstract Probably Bracke Noao ad Probably Modelg Xg M. Wag Sherma Vsual Lab, Suyvale, CA 94087, USA Absrac Ispred by he Drac oao, a ew se of symbols, he Probably Bracke Noao (PBN) s proposed for probably modelg.

More information

Differential Equation of Eigenvalues for Sturm Liouville Boundary Value Problem with Neumann Boundary Conditions

Differential Equation of Eigenvalues for Sturm Liouville Boundary Value Problem with Neumann Boundary Conditions Ierol Reserc Jorl o Aled d Bsc Sceces 3 Avlle ole www.rjs.co ISSN 5-838X / Vol 4 : 997-33 Scece Exlorer Plcos Derel Eqo o Eevles or Sr Lovlle Bodry Vle Prole w Ne Bodry Codos Al Kll Gold Dere o Mecs Azr

More information

Solution of Impulsive Differential Equations with Boundary Conditions in Terms of Integral Equations

Solution of Impulsive Differential Equations with Boundary Conditions in Terms of Integral Equations Joural of aheacs ad copuer Scece (4 39-38 Soluo of Ipulsve Dffereal Equaos wh Boudary Codos Ters of Iegral Equaos Arcle hsory: Receved Ocober 3 Acceped February 4 Avalable ole July 4 ohse Rabba Depare

More information

We have also learned that, thanks to the Central Limit Theorem and the Law of Large Numbers,

We have also learned that, thanks to the Central Limit Theorem and the Law of Large Numbers, Cofidece Itervals III What we kow so far: We have see how to set cofidece itervals for the ea, or expected value, of a oral probability distributio, both whe the variace is kow (usig the stadard oral,

More information

Maximum likelihood estimate of phylogeny. BIOL 495S/ CS 490B/ MATH 490B/ STAT 490B Introduction to Bioinformatics April 24, 2002

Maximum likelihood estimate of phylogeny. BIOL 495S/ CS 490B/ MATH 490B/ STAT 490B Introduction to Bioinformatics April 24, 2002 Mmm lkelhood eme of phylogey BIO 9S/ S 90B/ MH 90B/ S 90B Iodco o Bofomc pl 00 Ovevew of he pobblc ppoch o phylogey o k ee ccodg o he lkelhood d ee whee d e e of eqece d ee by ee wh leve fo he eqece. he

More information

1 Widrow-Hoff Algorithm

1 Widrow-Hoff Algorithm COS 511: heoreical Machine Learning Lecurer: Rob Schapire Lecure # 18 Scribe: Shaoqing Yang April 10, 014 1 Widrow-Hoff Algorih Firs le s review he Widrow-Hoff algorih ha was covered fro las lecure: Algorih

More information

Some Improved Estimators for Population Variance Using Two Auxiliary Variables in Double Sampling

Some Improved Estimators for Population Variance Using Two Auxiliary Variables in Double Sampling Vplav Kumar gh Rajeh gh Deparme of ac Baara Hdu Uver Varaa-00 Ida Flore maradache Uver of ew Meco Gallup UA ome Improved Emaor for Populao Varace Ug Two Aular Varable Double amplg Publhed : Rajeh gh Flore

More information

Chapter 8. Simple Linear Regression

Chapter 8. Simple Linear Regression Chaper 8. Smple Lear Regresso Regresso aalyss: regresso aalyss s a sascal mehodology o esmae he relaoshp of a respose varable o a se of predcor varable. whe here s jus oe predcor varable, we wll use smple

More information

DISTURBANCE TERMS. is a scalar and x i

DISTURBANCE TERMS. is a scalar and x i DISTURBANCE TERMS I a feld of research desg, we ofte have the qesto abot whether there s a relatoshp betwee a observed varable (sa, ) ad the other observed varables (sa, x ). To aswer the qesto, we ma

More information

Chapter 3: Maximum-Likelihood & Bayesian Parameter Estimation (part 1)

Chapter 3: Maximum-Likelihood & Bayesian Parameter Estimation (part 1) Aoucemes Reags o E-reserves Proec roosal ue oay Parameer Esmao Bomercs CSE 9-a Lecure 6 CSE9a Fall 6 CSE9a Fall 6 Paer Classfcao Chaer 3: Mamum-Lelhoo & Bayesa Parameer Esmao ar All maerals hese sles were

More information

The expected value of a sum of random variables,, is the sum of the expected values:

The expected value of a sum of random variables,, is the sum of the expected values: Sums of Radom Varables xpected Values ad Varaces of Sums ad Averages of Radom Varables The expected value of a sum of radom varables, say S, s the sum of the expected values: ( ) ( ) S Ths s always true

More information

Lecture 3 Topic 2: Distributions, hypothesis testing, and sample size determination

Lecture 3 Topic 2: Distributions, hypothesis testing, and sample size determination Lecure 3 Topc : Drbuo, hypohe eg, ad ample ze deermao The Sude - drbuo Coder a repeaed drawg of ample of ze from a ormal drbuo of mea. For each ample, compue,,, ad aoher ac,, where: The ac he devao of

More information

F-Tests and Analysis of Variance (ANOVA) in the Simple Linear Regression Model. 1. Introduction

F-Tests and Analysis of Variance (ANOVA) in the Simple Linear Regression Model. 1. Introduction ECOOMICS 35* -- OTE 9 ECO 35* -- OTE 9 F-Tess and Analyss of Varance (AOVA n he Smple Lnear Regresson Model Inroducon The smple lnear regresson model s gven by he followng populaon regresson equaon, or

More information

Upper Bound For Matrix Operators On Some Sequence Spaces

Upper Bound For Matrix Operators On Some Sequence Spaces Suama Uer Bou formar Oeraors Uer Bou For Mar Oeraors O Some Sequece Saces Suama Dearme of Mahemacs Gaah Maa Uersy Yogyaara 558 INDONESIA Emal: suama@ugmac masomo@yahoocom Isar D alam aer aa susa masalah

More information

Least squares and motion. Nuno Vasconcelos ECE Department, UCSD

Least squares and motion. Nuno Vasconcelos ECE Department, UCSD Leas squares ad moo uo Vascocelos ECE Deparme UCSD Pla for oda oda we wll dscuss moo esmao hs s eresg wo was moo s ver useful as a cue for recogo segmeao compresso ec. s a grea eample of leas squares problem

More information

EE 6885 Statistical Pattern Recognition

EE 6885 Statistical Pattern Recognition EE 6885 Sascal Paer Recogo Fall 005 Prof. Shh-Fu Chag hp://.ee.columba.edu/~sfchag Lecure 8 (/8/05 8- Readg Feaure Dmeso Reduco PCA, ICA, LDA, Chaper 3.8, 0.3 ICA Tuoral: Fal Exam Aapo Hyväre ad Erkk Oja,

More information

Density estimation. Density estimations. CS 2750 Machine Learning. Lecture 5. Milos Hauskrecht 5329 Sennott Square

Density estimation. Density estimations. CS 2750 Machine Learning. Lecture 5. Milos Hauskrecht 5329 Sennott Square Lecure 5 esy esmao Mlos Hauskrec mlos@cs..edu 539 Seo Square esy esmaos ocs: esy esmao: Mamum lkelood ML Bayesa arameer esmaes M Beroull dsrbuo. Bomal dsrbuo Mulomal dsrbuo Normal dsrbuo Eoeal famly Noaramerc

More information

DUALITY FOR MINIMUM MATRIX NORM PROBLEMS

DUALITY FOR MINIMUM MATRIX NORM PROBLEMS HE PUBLISHING HOUSE PROCEEDINGS OF HE ROMNIN CDEMY, Seres, OF HE ROMNIN CDEMY Vole 6, Nber /2005,. 000-000 DULIY FOR MINIMUM MRI NORM PROBLEMS Vasle PRED, Crstca FULG Uverst of Bcharest, Faclt of Matheatcs

More information

( ) ( ) Weibull Distribution: k ti. u u. Suppose t 1, t 2, t n are times to failure of a group of n mechanisms. The likelihood function is

( ) ( ) Weibull Distribution: k ti. u u. Suppose t 1, t 2, t n are times to failure of a group of n mechanisms. The likelihood function is Webll Dsbo: Des Bce Dep of Mechacal & Idsal Egeeg The Uvesy of Iowa pdf: f () exp Sppose, 2, ae mes o fale of a gop of mechasms. The lelhood fco s L ( ;, ) exp exp MLE: Webll 3//2002 page MLE: Webll 3//2002

More information

Moments of Order Statistics from Nonidentically Distributed Three Parameters Beta typei and Erlang Truncated Exponential Variables

Moments of Order Statistics from Nonidentically Distributed Three Parameters Beta typei and Erlang Truncated Exponential Variables Joural of Mahemacs ad Sascs 6 (4): 442-448, 200 SSN 549-3644 200 Scece Publcaos Momes of Order Sascs from Nodecally Dsrbued Three Parameers Bea ype ad Erlag Trucaed Expoeal Varables A.A. Jamoom ad Z.A.

More information

Sliding mode: Basic theory and new perspectives

Sliding mode: Basic theory and new perspectives Dep o Elecrcal ad Elecroc Eg. Uversy o Caglar 5 h Worshop o Srcral Dyamcal Sysems: Compaoal Aspecs Capolo (BA) Ialy Sldg mode: Basc heory ad ew perspecves Elo USAI esa@dee.ca. SDS 8 - Capolo (BA) Je 8

More information

Maximum Likelihood Estimation

Maximum Likelihood Estimation Marquette Uverst Maxmum Lkelhood Estmato Dael B. Rowe, Ph.D. Professor Departmet of Mathematcs, Statstcs, ad Computer Scece Coprght 08 b Marquette Uverst Maxmum Lkelhood Estmato We have bee sag that ~

More information

Interval Regression Analysis with Reduced Support Vector Machine

Interval Regression Analysis with Reduced Support Vector Machine Ieraoal DSI / Asa ad Pacfc DSI 007 Full Paper (July, 007) Ierval Regresso Aalyss wh Reduced Suppor Vecor Mache Cha-Hu Huag,), Ha-Yg ao ) ) Isue of Iforao Maagee, Naoal Chao Tug Uversy (leohkko@yahoo.co.w)

More information

To Estimate or to Predict

To Estimate or to Predict Raer Schwabe o Esmae or o Predc Implcaos o he esg or Lear Mxed Models o Esmae or o Predc - Implcaos o he esg or Lear Mxed Models Raer Schwabe, Marya Prus raer.schwabe@ovgu.de suppored by SKAVOE Germa ederal

More information

Notes on the stability of dynamic systems and the use of Eigen Values.

Notes on the stability of dynamic systems and the use of Eigen Values. Noes on he sabl of dnamc ssems and he use of Egen Values. Source: Macro II course noes, Dr. Davd Bessler s Tme Seres course noes, zarads (999) Ineremporal Macroeconomcs chaper 4 & Techncal ppend, and Hamlon

More information

Real-Time Systems. Example: scheduling using EDF. Feasibility analysis for EDF. Example: scheduling using EDF

Real-Time Systems. Example: scheduling using EDF. Feasibility analysis for EDF. Example: scheduling using EDF EDA/DIT6 Real-Tme Sysems, Chalmers/GU, 0/0 ecure # Updaed February, 0 Real-Tme Sysems Specfcao Problem: Assume a sysem wh asks accordg o he fgure below The mg properes of he asks are gve he able Ivesgae

More information

Queuing Theory: Memory Buffer Limits on Superscalar Processing

Queuing Theory: Memory Buffer Limits on Superscalar Processing Cle/ Model of I/O Queug Theory: Memory Buffer Lms o Superscalar Processg Cle reques respose Devce Fas CPU s cle for slower I/O servces Buffer sores cle requess ad s a slower server respose rae Laecy Tme

More information