The Moment Approximation of the First Passage Time For The Birth Death Diffusion Process with Immigraton to a Moving Linear Barrier

Size: px
Start display at page:

Download "The Moment Approximation of the First Passage Time For The Birth Death Diffusion Process with Immigraton to a Moving Linear Barrier"

Transcription

1 Rece Avaces i Auomaic Corol, oellig a Simulaio The ome Approximaio of he Firs Passage Time For The irh Deah Diffusio Process wih Immigrao o a ovig Liear arrier ASEL. AL-EIDEH Kuwai Uiversiy, College of usiess Amiisraio Dep. of Quaiaive ehos a Iformaio Sysem P. O. ox 5486, Safa 1355, Kuwai E-ail: basel@cba.eu.kw ASTRACT: Toay, he he evelopme of a mahemaical moels for populaio growh of grea imporace i may fiels. The growh a eclie of real populaios ca i may cases be well approximae by he soluios of a sochasic iffereial equaios. However, here are may soluios i which he esseially raom aure of populaio growh shoul be ake io accou. I his paper, we approximaig he momes of he firs passage ime for he birh a eah iffusio process wih immigraio o a movig liear barriers. This was oe by approximaig he iffereial equaios by a equivale ifferece equaios. Key Wors: Firs Passage Time, irh-deah Diffusio Process, Immigraio, Differece Equaios. 1. INTRODUCTION Firs passage ime play a impora rule i he area of applie probabiliy heory especially i sochasic moelig. Several examples of such problems are he exicio ime of a brachig process, or he cycle leghs of a cerai vehicle acuae raffic sigals. Acually he he firs passage imes o a movig barriers for iffusio a oher markov processes arises i biological moelig (Cf. Ewes (1979)), i saisics (Cf. Darlig a Sieger (1953) a Durbi (1971)). ay impora resuls relae o he firs passage ime have bee suie from iffere pois of view of iffere auhors. For example, cneil (197) has erive he isribuio of he iegral fucioal Wx Tx g{ X ( }, where T x is he firs passage ime o he origi i a geeral birh eah process wih X() x a g(.) is a arbirary fucio. Also, Iglehar (1965), cneil a Schach (1973) have bee show a umber of classical birh a eah processes upo akig iffusio limis ISN:

2 Rece Avaces i Auomaic Corol, oellig a Simulaio o asympoically approach he Orsei Uhlebeck (O.U.). ay properies such as a firs passage ime o a barrier, absorbig or reflecig, locae some isace from a iiial sarig poi of he O.U. process a he relae iffusio process a he relae iffusio process such as he case of he firs passage ime of a Wieer process o a liear barrier is a close form expressio for he esiy available is iscusse i Cox a iller (1965). Also, ohers such as, Karli a Taylor (1981), Thomas (1975), Ferebee (198), Tuckwell a Wa (1984), Al-Eieh (4), ec. have bee iscusse he firs passage ime from iffere pois of view. I paricular, Thomas (1975) escribes some mea firs passage ime approximaio for he Orsei Uhleeck process. Tuckwell a Wa (1984) have suie he firs-passage ime of a arkov process o a movig barriers as a firs-exi ime for a vecor whose compoes iclue he process a he barrier. Also, Al-Eieh (4), has iscusse he problem of fiig he momes of he firs passage ime isribuio for he birh-eah iffusio a he Wrigh-Fisher iffusio processes o a movig liear barriers usig he meho of approximaig he iffereial equaios by ifferece equaios. I his paper, we cosier he birh a eah iffusio process wih immigraio a suy he firs passage ime for such a process o a movig liear barrier. ore specifically, he mome approximaios are erive usig he meho of ifferece equaios use i Al-Eieh (4) cosierig he immigraio rae ε.. FIRST PASSAGE TIE OENT APPROXIATIONS Cosier he birh a eah iffusio Process wih immigraio X ( : wih ifiiesimal mea { } bx ε a variace sarig a some x >, where b a a are he rif a he iffusio coefficies respecively aε is he cosa X ( : immigraio rae. Also, { } is a arkov process wih sae space S, a saisfies he Io [ ) sochasic iffereial equaio ( bx( ) ax( W( ) X ( ε (1) Where { ( : } W is a saar Wieer process wih zero mea a variace. Assume ha he exisece a uiqueess coiios are saisfie (Cf. Gihma a Skoroho (197)). Le Y ( : be a movig liear barrier { } equaio such ha Y ( ) k. Or equivalely Y ( c k, wih Y ( c Now, eoe he firs passage ime of a process X ( o a movig liear barrier raom variables Y ( c k by he ISN:

3 Rece Avaces i Auomaic Corol, oellig a Simulaio T Y if{ : X ( c k} () wih probabiliy esiy fucio g ( ; x ) - c k p ( x, x ; Here p ( x, x ; is he probabiliy esiy fucio of X ( coiioal o X () x Le ( x, Y ; ; 1,,3,, be he -h mome of he firs passage ime T Y, i.e. ( x E( ), T Y ; 1,,3,, (3) I follows from he forwar Kolmogorov equaio ha he -h mome of T Y mus saisfy he oriary iffereial equaio c ( x, ( bx ) ( x, ( x, Y ; ε Or equivalely 1 bx ε c 1 Where ( x, Y ; a ( x are he firs erivaives of ( x, Y ; wih respec o x ( x x Y), (4) (5), wih appropriae bouary coiios for 1,,3,.Noe ha x 1 ( )., Now, rewrie he equaio i (5), we obai b ε a ( x, Y ; ( x, Y ; 1 ( x, Y ; (6) Le be he ifferece operaor. The we efie he firs orer ifferece of as follows: ( x Y ;) ( x, Y ;) ( x, Y (7), 1 ; (Cf. Kelley a peerso (1991) ). Noe ha equaio (6) ca be approximae by a 1 (8) y applyig equaio (7) o equaio (8) we ge : 1 a a ( x 1 (9) Now, we will use he marix heory o solve he iffereial equaio efie i equaio (9). If we le x x,, x,, ( ) [ ( ) ( ) ] The we ge, 1 A (1) ISN:

4 Rece Avaces i Auomaic Corol, oellig a Simulaio Where a A Now le This imply 3 ( x, Y ; 4 ( x, Y ; R (11) ( x, R( x, (1) Apply o equaio (1), we ge R ( x, I A R( x ) ( ),. x, (13) Where I is he ieiy marix a is he zero marix. Thus, he soluio of he sysem of equaio i (13) is he give by R x ( x, (, e * A D R( x ) ( ),. x, (14) Where D [ ij ] ; i, j 1 is he iagoal marix wih eries ( c k x ) ; j i ij ; Oherwise (15) A [ a ]; i, j 1 wih eries a ij A ij is he marix i c k l ; j i 1 x ( c kx) ; j i ( c kx) ; j i 1 ; Oherwise Noe ha he marix A is efie by D e I! 3 3! (16) e where This series is coverge sice i is a cauchy operaor of equaio (.6) (Cf. Zeifma (1991)). 3. CONCLUSION I coclusio he avaage of his echique is o use he ifferece equaio o approximae he oriary iffereial equaio sice i is he iscreizaio of he ODE. Also, he sysem of he soluios i equaio (14) gives a explici soluio o he firs passage ime momes for he birh a eah iffusio process wih immigraio o a movig liear barriers. This icreases he applicabiliy of he iffusio process i sochasic moelig or i all area of applie probabiliy heory. Noe ha i case of immigraio whe ε, we obaie he same resuls as i Al-Eieh (4). ISN:

5 Rece Avaces i Auomaic Corol, oellig a Simulaio REFERENCES [1].. Al-Eieh, Firs-passage ime mome approximaio for he birheah iffusio process o a movig liear barriers. J. Sa. & aag. Sysems, Vol.7 (4), No.1, [] D. R. Cox a H. D. iller, The heory of sochasic processes. ehue, Loo (1965). [3] D. Darlig a A. J. F. Sieger, The firs - passage problem for a coiuos arkov process. A. ah. Sais. 4 (1953), [4] J.Durbi, ouary-crossig probabiliies for he rowia moio a Poisso processes a echiques for compuig he power of he Kolmogorov-Smirov es. J. Appl. Prob. 8 (1971), [5] W. J. Ewes, ahemaical Populaio Geeics. Spriger-Verlag, erli (1979). [6]. Ferebee, The age approximaioo oe-sie rowia exi esiies. Z.Wahrscheilichkeish 61 (198), [9] W.G. Kelly a A.C. Peerso, Differece Equaios : A Iroucio wih Applicaios. Acaemic Press, New York (1991). [1] D. R. cneil, Iegral fucioals of birh a eah processes a relae limiig isribuios. A. ah. Sais. 41 (197), [11] D. R. cneil a S. Schach, Ceral limi aalogues for arkov populaio processes. J. R. Sais. Soc.,35 (1973),1-3. [1]. U. Thomas, Some mea firs passage ime approximaios for he Orsei Uhlebeck process.j. Appl. Prob. 1 (1975),6-64. [13] H. C. Tuchwell a F. Y.. Wa, Firs-passage ime of arkov processes o movig barriers. J. Appl. Prob. Vol. 1 (1984), [14] A. I. Zeifma, Some esimaes of he rae of covergece for birh a eah processes. J. Appl. Prob. 8 (1991), [7] D. L. Iglehar, Limiig iffusio approximaio for he may server queuea he repairma problem. J. Appl. Prob. (1965), [8] S. Karli a H.. Taylor, A Seco Course i Sochasic Processes. Acaemic press. New York (1981). ISN:

The Moment Approximation of the First Passage Time for the Birth Death Diffusion Process with Immigraton to a Moving Linear Barrier

The Moment Approximation of the First Passage Time for the Birth Death Diffusion Process with Immigraton to a Moving Linear Barrier America Joural of Applied Mahemaics ad Saisics, 015, Vol. 3, No. 5, 184-189 Available olie a hp://pubs.sciepub.com/ajams/3/5/ Sciece ad Educaio Publishig DOI:10.1691/ajams-3-5- The Mome Approximaio of

More information

First-Passage Time moment Approximation For The Birth Death Diffusion Process To A General moving Barrier

First-Passage Time moment Approximation For The Birth Death Diffusion Process To A General moving Barrier Ieriol Jourl of emics Sisics Iveio IJSI E-ISSN: 3 4767 P-ISSN: 3-4759 Volume 5 Issue 9 Jury 8 PP-4-8 Firs-Pssge Time mome pproimio For Te Bir De Diffusio Process To Geerl movig Brrier Bsel. l-eie KuwiUiversiy

More information

STK4080/9080 Survival and event history analysis

STK4080/9080 Survival and event history analysis STK48/98 Survival ad eve hisory aalysis Marigales i discree ime Cosider a sochasic process The process M is a marigale if Lecure 3: Marigales ad oher sochasic processes i discree ime (recap) where (formally

More information

Samuel Sindayigaya 1, Nyongesa L. Kennedy 2, Adu A.M. Wasike 3

Samuel Sindayigaya 1, Nyongesa L. Kennedy 2, Adu A.M. Wasike 3 Ieraioal Joural of Saisics ad Aalysis. ISSN 48-9959 Volume 6, Number (6, pp. -8 Research Idia Publicaios hp://www.ripublicaio.com The Populaio Mea ad is Variace i he Presece of Geocide for a Simple Birh-Deah-

More information

Ideal Amplifier/Attenuator. Memoryless. where k is some real constant. Integrator. System with memory

Ideal Amplifier/Attenuator. Memoryless. where k is some real constant. Integrator. System with memory Liear Time-Ivaria Sysems (LTI Sysems) Oulie Basic Sysem Properies Memoryless ad sysems wih memory (saic or dyamic) Causal ad o-causal sysems (Causaliy) Liear ad o-liear sysems (Lieariy) Sable ad o-sable

More information

Mean Square Convergent Finite Difference Scheme for Stochastic Parabolic PDEs

Mean Square Convergent Finite Difference Scheme for Stochastic Parabolic PDEs America Joural of Compuaioal Mahemaics, 04, 4, 80-88 Published Olie Sepember 04 i SciRes. hp://www.scirp.org/joural/ajcm hp://dx.doi.org/0.436/ajcm.04.4404 Mea Square Coverge Fiie Differece Scheme for

More information

DETERMINATION OF PARTICULAR SOLUTIONS OF NONHOMOGENEOUS LINEAR DIFFERENTIAL EQUATIONS BY DISCRETE DECONVOLUTION

DETERMINATION OF PARTICULAR SOLUTIONS OF NONHOMOGENEOUS LINEAR DIFFERENTIAL EQUATIONS BY DISCRETE DECONVOLUTION U.P.B. ci. Bull. eries A Vol. 69 No. 7 IN 3-77 DETERMINATION OF PARTIULAR OLUTION OF NONHOMOGENEOU LINEAR DIFFERENTIAL EQUATION BY DIRETE DEONVOLUTION M. I. ÎRNU e preziă o ouă meoă e eermiare a soluţiilor

More information

Delta Method on Bootstrapping of Autoregressive Process. Abstract

Delta Method on Bootstrapping of Autoregressive Process. Abstract Proceeigs 59h ISI Worl Saisics Cogress 5-30 Augus 03 Hog Kog (Sessio CPS04) p.3959 Dela Meho o Boosrappig of Auoregressive Process Bambag Suprihai Suryo Gurio 3 Sri Haryami 4 Uiversiy of Sriwijaya Palembag

More information

B. Maddah INDE 504 Simulation 09/02/17

B. Maddah INDE 504 Simulation 09/02/17 B. Maddah INDE 54 Simulaio 9/2/7 Queueig Primer Wha is a queueig sysem? A queueig sysem cosiss of servers (resources) ha provide service o cusomers (eiies). A Cusomer requesig service will sar service

More information

Modified Ratio and Product Estimators for Estimating Population Mean in Two-Phase Sampling

Modified Ratio and Product Estimators for Estimating Population Mean in Two-Phase Sampling America Joural of Operaioal esearch 06, 6(3): 6-68 DOI: 0.593/j.ajor.060603.0 Moifie aio a Prouc Esimaors for Esimaig Populaio Mea i Two-Phase Samplig Subhash Kumar Yaav, Sa Gupa, S. S. Mishra 3,, Alok

More information

xp (X = x) = P (X = 1) = θ. Hence, the method of moments estimator of θ is

xp (X = x) = P (X = 1) = θ. Hence, the method of moments estimator of θ is Exercise 7 / page 356 Noe ha X i are ii from Beroulli(θ where 0 θ a Meho of momes: Sice here is oly oe parameer o be esimae we ee oly oe equaio where we equae he rs sample mome wih he rs populaio mome,

More information

[Hussain* et al., 5(8): August, 2016] ISSN: IC Value: 3.00 Impact Factor: 4.116

[Hussain* et al., 5(8): August, 2016] ISSN: IC Value: 3.00 Impact Factor: 4.116 [Hussai* e al., 5(8): Augus, 6] ISSN: 77-9655 IC Value: 3. Impac Facor: 4.6 IJESRT INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY NUMERICAL SOLUTIONS FOR STOCHASTIC PARTIAL DIFFERENTIAL

More information

ODEs II, Supplement to Lectures 6 & 7: The Jordan Normal Form: Solving Autonomous, Homogeneous Linear Systems. April 2, 2003

ODEs II, Supplement to Lectures 6 & 7: The Jordan Normal Form: Solving Autonomous, Homogeneous Linear Systems. April 2, 2003 ODEs II, Suppleme o Lecures 6 & 7: The Jorda Normal Form: Solvig Auoomous, Homogeeous Liear Sysems April 2, 23 I his oe, we describe he Jorda ormal form of a marix ad use i o solve a geeral homogeeous

More information

The Eigen Function of Linear Systems

The Eigen Function of Linear Systems 1/25/211 The Eige Fucio of Liear Sysems.doc 1/7 The Eige Fucio of Liear Sysems Recall ha ha we ca express (expad) a ime-limied sigal wih a weighed summaio of basis fucios: v ( ) a ψ ( ) = where v ( ) =

More information

The Central Limit Theorem

The Central Limit Theorem The Ceral Limi Theorem The ceral i heorem is oe of he mos impora heorems i probabiliy heory. While here a variey of forms of he ceral i heorem, he mos geeral form saes ha give a sufficiely large umber,

More information

FIXED FUZZY POINT THEOREMS IN FUZZY METRIC SPACE

FIXED FUZZY POINT THEOREMS IN FUZZY METRIC SPACE Mohia & Samaa, Vol. 1, No. II, December, 016, pp 34-49. ORIGINAL RESEARCH ARTICLE OPEN ACCESS FIED FUZZY POINT THEOREMS IN FUZZY METRIC SPACE 1 Mohia S. *, Samaa T. K. 1 Deparme of Mahemaics, Sudhir Memorial

More information

ME 3210 Mechatronics II Laboratory Lab 6: Second-Order Dynamic Response

ME 3210 Mechatronics II Laboratory Lab 6: Second-Order Dynamic Response Iroucio ME 30 Mecharoics II Laboraory Lab 6: Seco-Orer Dyamic Respose Seco orer iffereial equaios approimae he yamic respose of may sysems. I his lab you will moel a alumium bar as a seco orer Mass-Sprig-Damper

More information

Moment Generating Function

Moment Generating Function 1 Mome Geeraig Fucio m h mome m m m E[ ] x f ( x) dx m h ceral mome m m m E[( ) ] ( ) ( x ) f ( x) dx Mome Geeraig Fucio For a real, M () E[ e ] e k x k e p ( x ) discree x k e f ( x) dx coiuous Example

More information

MATH 507a ASSIGNMENT 4 SOLUTIONS FALL 2018 Prof. Alexander. g (x) dx = g(b) g(0) = g(b),

MATH 507a ASSIGNMENT 4 SOLUTIONS FALL 2018 Prof. Alexander. g (x) dx = g(b) g(0) = g(b), MATH 57a ASSIGNMENT 4 SOLUTIONS FALL 28 Prof. Alexader (2.3.8)(a) Le g(x) = x/( + x) for x. The g (x) = /( + x) 2 is decreasig, so for a, b, g(a + b) g(a) = a+b a g (x) dx b so g(a + b) g(a) + g(b). Sice

More information

A TAUBERIAN THEOREM FOR THE WEIGHTED MEAN METHOD OF SUMMABILITY

A TAUBERIAN THEOREM FOR THE WEIGHTED MEAN METHOD OF SUMMABILITY U.P.B. Sci. Bull., Series A, Vol. 78, Iss. 2, 206 ISSN 223-7027 A TAUBERIAN THEOREM FOR THE WEIGHTED MEAN METHOD OF SUMMABILITY İbrahim Çaak I his paper we obai a Tauberia codiio i erms of he weighed classical

More information

F D D D D F. smoothed value of the data including Y t the most recent data.

F D D D D F. smoothed value of the data including Y t the most recent data. Module 2 Forecasig 1. Wha is forecasig? Forecasig is defied as esimaig he fuure value ha a parameer will ake. Mos scieific forecasig mehods forecas he fuure value usig pas daa. I Operaios Maageme forecasig

More information

The Solution of the One Species Lotka-Volterra Equation Using Variational Iteration Method ABSTRACT INTRODUCTION

The Solution of the One Species Lotka-Volterra Equation Using Variational Iteration Method ABSTRACT INTRODUCTION Malaysia Joural of Mahemaical Scieces 2(2): 55-6 (28) The Soluio of he Oe Species Loka-Volerra Equaio Usig Variaioal Ieraio Mehod B. Baiha, M.S.M. Noorai, I. Hashim School of Mahemaical Scieces, Uiversii

More information

Fractional Calculus: A Tutorial Presented at

Fractional Calculus: A Tutorial Presented at Syhesis a Processig of Maerials U.S. Army Research, Developme a Egieerig Comma Presee a Nework Froier Workshop Norhweser Uiversiy December 4, 2013 Bruce J. Wes ST- Chief Scieis Mahemaics Army Research

More information

Research Article A Generalized Nonlinear Sum-Difference Inequality of Product Form

Research Article A Generalized Nonlinear Sum-Difference Inequality of Product Form Joural of Applied Mahemaics Volume 03, Aricle ID 47585, 7 pages hp://dx.doi.org/0.55/03/47585 Research Aricle A Geeralized Noliear Sum-Differece Iequaliy of Produc Form YogZhou Qi ad Wu-Sheg Wag School

More information

Approximating Solutions for Ginzburg Landau Equation by HPM and ADM

Approximating Solutions for Ginzburg Landau Equation by HPM and ADM Available a hp://pvamu.edu/aam Appl. Appl. Mah. ISSN: 193-9466 Vol. 5, No. Issue (December 1), pp. 575 584 (Previously, Vol. 5, Issue 1, pp. 167 1681) Applicaios ad Applied Mahemaics: A Ieraioal Joural

More information

Section 8 Convolution and Deconvolution

Section 8 Convolution and Deconvolution APPLICATIONS IN SIGNAL PROCESSING Secio 8 Covoluio ad Decovoluio This docume illusraes several echiques for carryig ou covoluio ad decovoluio i Mahcad. There are several operaors available for hese fucios:

More information

A Multivariate CLT for Local Dependence with n &12 log n Rate and Applications to Multivariate Graph Related Statistics

A Multivariate CLT for Local Dependence with n &12 log n Rate and Applications to Multivariate Graph Related Statistics joural of mulivariae aalysis 56, 333350 (1996) aricle o. 0017 A Mulivariae CLT for Local Depeece wih &12 log Rae a Applicaios o Mulivariae Graph Relae Saisics Yosef Rio* Uiversiy of Califoria, Sa Diego

More information

1 Notes on Little s Law (l = λw)

1 Notes on Little s Law (l = λw) Copyrigh c 26 by Karl Sigma Noes o Lile s Law (l λw) We cosider here a famous ad very useful law i queueig heory called Lile s Law, also kow as l λw, which assers ha he ime average umber of cusomers i

More information

TAKA KUSANO. laculty of Science Hrosh tlnlersty 1982) (n-l) + + Pn(t)x 0, (n-l) + + Pn(t)Y f(t,y), XR R are continuous functions.

TAKA KUSANO. laculty of Science Hrosh tlnlersty 1982) (n-l) + + Pn(t)x 0, (n-l) + + Pn(t)Y f(t,y), XR R are continuous functions. Iera. J. Mah. & Mah. Si. Vol. 6 No. 3 (1983) 559-566 559 ASYMPTOTIC RELATIOHIPS BETWEEN TWO HIGHER ORDER ORDINARY DIFFERENTIAL EQUATIONS TAKA KUSANO laculy of Sciece Hrosh llersy 1982) ABSTRACT. Some asympoic

More information

Chapter 2: Time-Domain Representations of Linear Time-Invariant Systems. Chih-Wei Liu

Chapter 2: Time-Domain Representations of Linear Time-Invariant Systems. Chih-Wei Liu Caper : Time-Domai Represeaios of Liear Time-Ivaria Sysems Ci-Wei Liu Oulie Iroucio Te Covoluio Sum Covoluio Sum Evaluaio Proceure Te Covoluio Iegral Covoluio Iegral Evaluaio Proceure Iercoecios of LTI

More information

Available online at J. Math. Comput. Sci. 4 (2014), No. 4, ISSN:

Available online at   J. Math. Comput. Sci. 4 (2014), No. 4, ISSN: Available olie a hp://sci.org J. Mah. Compu. Sci. 4 (2014), No. 4, 716-727 ISSN: 1927-5307 ON ITERATIVE TECHNIQUES FOR NUMERICAL SOLUTIONS OF LINEAR AND NONLINEAR DIFFERENTIAL EQUATIONS S.O. EDEKI *, A.A.

More information

Economics 8723 Macroeconomic Theory Problem Set 2 Professor Sanjay Chugh Spring 2017

Economics 8723 Macroeconomic Theory Problem Set 2 Professor Sanjay Chugh Spring 2017 Deparme of Ecoomics The Ohio Sae Uiversiy Ecoomics 8723 Macroecoomic Theory Problem Se 2 Professor Sajay Chugh Sprig 207 Labor Icome Taxes, Nash-Bargaied Wages, ad Proporioally-Bargaied Wages. I a ecoomy

More information

th m m m m central moment : E[( X X) ] ( X X) ( x X) f ( x)

th m m m m central moment : E[( X X) ] ( X X) ( x X) f ( x) 1 Trasform Techiques h m m m m mome : E[ ] x f ( x) dx h m m m m ceral mome : E[( ) ] ( ) ( x) f ( x) dx A coveie wa of fidig he momes of a radom variable is he mome geeraig fucio (MGF). Oher rasform echiques

More information

LINEAR APPROXIMATION OF THE BASELINE RBC MODEL SEPTEMBER 17, 2013

LINEAR APPROXIMATION OF THE BASELINE RBC MODEL SEPTEMBER 17, 2013 LINEAR APPROXIMATION OF THE BASELINE RBC MODEL SEPTEMBER 7, 203 Iroducio LINEARIZATION OF THE RBC MODEL For f( xyz,, ) = 0, mulivariable Taylor liear expasio aroud f( xyz,, ) f( xyz,, ) + f( xyz,, )( x

More information

Time Dependent Queuing

Time Dependent Queuing Time Depede Queuig Mark S. Daski Deparme of IE/MS, Norhweser Uiversiy Evaso, IL 628 Sprig, 26 Oulie Will look a M/M/s sysem Numerically iegraio of Chapma- Kolmogorov equaios Iroducio o Time Depede Queue

More information

Mathematical Issues. Written by Okito Yamashita, Last revised 2009/07/27

Mathematical Issues. Written by Okito Yamashita, Last revised 2009/07/27 Mahemaical Issues Wrie by Okio Yamashia, Las revise 009/07/7 I SLR oolbox, here are seve biary classificaio algorihms implemee. I his ocume, he probabilisic moels of he implemee classifiers are escribe

More information

LINEAR APPROXIMATION OF THE BASELINE RBC MODEL JANUARY 29, 2013

LINEAR APPROXIMATION OF THE BASELINE RBC MODEL JANUARY 29, 2013 LINEAR APPROXIMATION OF THE BASELINE RBC MODEL JANUARY 29, 203 Iroducio LINEARIZATION OF THE RBC MODEL For f( x, y, z ) = 0, mulivariable Taylor liear expasio aroud f( x, y, z) f( x, y, z) + f ( x, y,

More information

Transient Behavior Analysis of a Finite Capacity Queue with Working Breakdowns and Server Vacations

Transient Behavior Analysis of a Finite Capacity Queue with Working Breakdowns and Server Vacations Proceeigs of he Ieraioal MuliCoferece of Egieers a Compuer Scieiss 2014 Vol II,, March 12-14, 2014, Hog Kog Trasie Behavior Aalysis of a Fiie Capaciy Queue wih Workig Breakows a Server Vacaios Dog-Yuh

More information

1. Solve by the method of undetermined coefficients and by the method of variation of parameters. (4)

1. Solve by the method of undetermined coefficients and by the method of variation of parameters. (4) 7 Differeial equaios Review Solve by he mehod of udeermied coefficies ad by he mehod of variaio of parameers (4) y y = si Soluio; we firs solve he homogeeous equaio (4) y y = 4 The correspodig characerisic

More information

Research Design - - Topic 2 Inferential Statistics: The t-test 2010 R.C. Gardner, Ph.D. Independent t-test

Research Design - - Topic 2 Inferential Statistics: The t-test 2010 R.C. Gardner, Ph.D. Independent t-test Research Desig - - Topic Ifereial aisics: The -es 00 R.C. Garer, Ph.D. Geeral Raioale Uerlyig he -es (Garer & Tremblay, 007, Ch. ) The Iepee -es The Correlae (paire) -es Effec ize a Power (Kirk, 995, pp

More information

Modal Analysis of a Tight String

Modal Analysis of a Tight String Moal Aalysis of a Tigh Srig Daiel. S. Sus Associae Professor of Mechaical Egieerig a Egieerig Mechaics Presee o ME Moay, Ocober 30, 000 See: hp://web.ms.eu/~sus/me_classes.hml Basic Theory The srig uer

More information

ECE 570 Session 7 IC 752-E Computer Aided Engineering for Integrated Circuits. Transient analysis. Discuss time marching methods used in SPICE

ECE 570 Session 7 IC 752-E Computer Aided Engineering for Integrated Circuits. Transient analysis. Discuss time marching methods used in SPICE ECE 570 Sessio 7 IC 75-E Compuer Aided Egieerig for Iegraed Circuis Trasie aalysis Discuss ime marcig meods used i SPICE. Time marcig meods. Explici ad implici iegraio meods 3. Implici meods used i circui

More information

A Note on Random k-sat for Moderately Growing k

A Note on Random k-sat for Moderately Growing k A Noe o Radom k-sat for Moderaely Growig k Ju Liu LMIB ad School of Mahemaics ad Sysems Sciece, Beihag Uiversiy, Beijig, 100191, P.R. Chia juliu@smss.buaa.edu.c Zogsheg Gao LMIB ad School of Mahemaics

More information

Extended Laguerre Polynomials

Extended Laguerre Polynomials I J Coemp Mah Scieces, Vol 7, 1, o, 189 194 Exeded Laguerre Polyomials Ada Kha Naioal College of Busiess Admiisraio ad Ecoomics Gulberg-III, Lahore, Pakisa adakhaariq@gmailcom G M Habibullah Naioal College

More information

Inference of the Second Order Autoregressive. Model with Unit Roots

Inference of the Second Order Autoregressive. Model with Unit Roots Ieraioal Mahemaical Forum Vol. 6 0 o. 5 595-604 Iferece of he Secod Order Auoregressive Model wih Ui Roos Ahmed H. Youssef Professor of Applied Saisics ad Ecoomerics Isiue of Saisical Sudies ad Research

More information

Four equations describe the dynamic solution to RBC model. Consumption-leisure efficiency condition. Consumption-investment efficiency condition

Four equations describe the dynamic solution to RBC model. Consumption-leisure efficiency condition. Consumption-investment efficiency condition LINEARIZING AND APPROXIMATING THE RBC MODEL SEPTEMBER 7, 200 For f( x, y, z ), mulivariable Taylor liear expasio aroud ( x, yz, ) f ( x, y, z) f( x, y, z) + f ( x, y, z)( x x) + f ( x, y, z)( y y) + f

More information

Department of Mathematical and Statistical Sciences University of Alberta

Department of Mathematical and Statistical Sciences University of Alberta MATH 4 (R) Wier 008 Iermediae Calculus I Soluios o Problem Se # Due: Friday Jauary 8, 008 Deparme of Mahemaical ad Saisical Scieces Uiversiy of Albera Quesio. [Sec.., #] Fid a formula for he geeral erm

More information

Calculus BC 2015 Scoring Guidelines

Calculus BC 2015 Scoring Guidelines AP Calculus BC 5 Scorig Guidelies 5 The College Board. College Board, Advaced Placeme Program, AP, AP Ceral, ad he acor logo are regisered rademarks of he College Board. AP Ceral is he official olie home

More information

Homotopy Analysis Method for Solving Fractional Sturm-Liouville Problems

Homotopy Analysis Method for Solving Fractional Sturm-Liouville Problems Ausralia Joural of Basic ad Applied Scieces, 4(1): 518-57, 1 ISSN 1991-8178 Homoopy Aalysis Mehod for Solvig Fracioal Surm-Liouville Problems 1 A Neamay, R Darzi, A Dabbaghia 1 Deparme of Mahemaics, Uiversiy

More information

Dynamic h-index: the Hirsch index in function of time

Dynamic h-index: the Hirsch index in function of time Dyamic h-idex: he Hirsch idex i fucio of ime by L. Egghe Uiversiei Hassel (UHassel), Campus Diepebeek, Agoralaa, B-3590 Diepebeek, Belgium ad Uiversiei Awerpe (UA), Campus Drie Eike, Uiversieisplei, B-260

More information

EXISTENCE THEORY OF RANDOM DIFFERENTIAL EQUATIONS D. S. Palimkar

EXISTENCE THEORY OF RANDOM DIFFERENTIAL EQUATIONS D. S. Palimkar Ieraioal Joural of Scieific ad Research Publicaios, Volue 2, Issue 7, July 22 ISSN 225-353 EXISTENCE THEORY OF RANDOM DIFFERENTIAL EQUATIONS D S Palikar Depare of Maheaics, Vasarao Naik College, Naded

More information

Math 2414 Homework Set 7 Solutions 10 Points

Math 2414 Homework Set 7 Solutions 10 Points Mah Homework Se 7 Soluios 0 Pois #. ( ps) Firs verify ha we ca use he iegral es. The erms are clearly posiive (he epoeial is always posiive ad + is posiive if >, which i is i his case). For decreasig we

More information

VARIATIONAL ITERATION METHOD: A COMPUTATIONAL TOOL FOR SOLVING COUPLED SYSTEM OF NONLINEAR PARTIAL DIFFERENTIAL EQUATIONS

VARIATIONAL ITERATION METHOD: A COMPUTATIONAL TOOL FOR SOLVING COUPLED SYSTEM OF NONLINEAR PARTIAL DIFFERENTIAL EQUATIONS Joral of Sciece a Ars Year 6 No. 336 pp. 43-48 6 ORIGINAL PAPER ARIATIONAL ITERATION METHOD: A COMPTATIONAL TOOL FOR SOLING COPLED SYSTEM OF NONLINEAR PARTIAL DIFFERENTIAL EQATIONS MORF OYEDNSI OLAYIOLA

More information

BEST LINEAR FORECASTS VS. BEST POSSIBLE FORECASTS

BEST LINEAR FORECASTS VS. BEST POSSIBLE FORECASTS BEST LINEAR FORECASTS VS. BEST POSSIBLE FORECASTS Opimal ear Forecasig Alhough we have o meioed hem explicily so far i he course, here are geeral saisical priciples for derivig he bes liear forecas, ad

More information

Comparison between Fourier and Corrected Fourier Series Methods

Comparison between Fourier and Corrected Fourier Series Methods Malaysia Joural of Mahemaical Scieces 7(): 73-8 (13) MALAYSIAN JOURNAL OF MATHEMATICAL SCIENCES Joural homepage: hp://eispem.upm.edu.my/oural Compariso bewee Fourier ad Correced Fourier Series Mehods 1

More information

An EOQ Model for Weibull Deteriorating Items with. Power Demand and Partial Backlogging

An EOQ Model for Weibull Deteriorating Items with. Power Demand and Partial Backlogging . J. oemp. Mah. Scieces, Vol. 5, 00, o. 38, 895-904 A EOQ Moel for Weibull Deerioraig ems wih Power Dema a Parial Backloggig. K. ripahy* a L. M. Praha ** *Deparme of Saisics, Sambalpur Uiversiy, Jyoi Vihar

More information

Entropy production rate of nonequilibrium systems from the Fokker-Planck equation

Entropy production rate of nonequilibrium systems from the Fokker-Planck equation Eropy producio rae of oequilibrium sysems from he Fokker-Plack equaio Yu Haiao ad Du Jiuli Deparme of Physics School of Sciece Tiaji Uiversiy Tiaji 30007 Chia Absrac: The eropy producio rae of oequilibrium

More information

APPROXIMATE SOLUTION OF FRACTIONAL DIFFERENTIAL EQUATIONS WITH UNCERTAINTY

APPROXIMATE SOLUTION OF FRACTIONAL DIFFERENTIAL EQUATIONS WITH UNCERTAINTY APPROXIMATE SOLUTION OF FRACTIONAL DIFFERENTIAL EQUATIONS WITH UNCERTAINTY ZHEN-GUO DENG ad GUO-CHENG WU 2, 3 * School of Mahemaics ad Iformaio Sciece, Guagi Uiversiy, Naig 534, PR Chia 2 Key Laboraory

More information

10.3 Autocorrelation Function of Ergodic RP 10.4 Power Spectral Density of Ergodic RP 10.5 Normal RP (Gaussian RP)

10.3 Autocorrelation Function of Ergodic RP 10.4 Power Spectral Density of Ergodic RP 10.5 Normal RP (Gaussian RP) ENGG450 Probabiliy ad Saisics for Egieers Iroducio 3 Probabiliy 4 Probabiliy disribuios 5 Probabiliy Desiies Orgaizaio ad descripio of daa 6 Samplig disribuios 7 Ifereces cocerig a mea 8 Comparig wo reames

More information

arxiv: v3 [math.pr] 10 Jan 2017

arxiv: v3 [math.pr] 10 Jan 2017 Local cluserig coefficie i geeralize prefereial aachme moels Alexaer Kro 1 a Liumila Osroumova Prokhorekova 1, 1 Moscow Isiue of Physics a Techology, Moscow, Russia Yaex, Moscow, Russia arxiv:1507.07771v3

More information

ECE 340 Lecture 15 and 16: Diffusion of Carriers Class Outline:

ECE 340 Lecture 15 and 16: Diffusion of Carriers Class Outline: ECE 340 Lecure 5 ad 6: iffusio of Carriers Class Oulie: iffusio rocesses iffusio ad rif of Carriers Thigs you should kow whe you leave Key Quesios Why do carriers diffuse? Wha haes whe we add a elecric

More information

INTEGER INTERVAL VALUE OF NEWTON DIVIDED DIFFERENCE AND FORWARD AND BACKWARD INTERPOLATION FORMULA

INTEGER INTERVAL VALUE OF NEWTON DIVIDED DIFFERENCE AND FORWARD AND BACKWARD INTERPOLATION FORMULA Volume 8 No. 8, 45-54 ISSN: 34-3395 (o-lie versio) url: hp://www.ijpam.eu ijpam.eu INTEGER INTERVAL VALUE OF NEWTON DIVIDED DIFFERENCE AND FORWARD AND BACKWARD INTERPOLATION FORMULA A.Arul dass M.Dhaapal

More information

If boundary values are necessary, they are called mixed initial-boundary value problems. Again, the simplest prototypes of these IV problems are:

If boundary values are necessary, they are called mixed initial-boundary value problems. Again, the simplest prototypes of these IV problems are: 3. Iiial value problems: umerical soluio Fiie differeces - Trucaio errors, cosisecy, sabiliy ad covergece Crieria for compuaioal sabiliy Explici ad implici ime schemes Table of ime schemes Hyperbolic ad

More information

Fresnel Dragging Explained

Fresnel Dragging Explained Fresel Draggig Explaied 07/05/008 Decla Traill Decla@espace.e.au The Fresel Draggig Coefficie required o explai he resul of he Fizeau experime ca be easily explaied by usig he priciples of Eergy Field

More information

Stochastic filtering for diffusion processes with level crossings

Stochastic filtering for diffusion processes with level crossings 1 Sochasic filerig for diffusio processes wih level crossigs Agosio Cappoi, Ibrahim Fakulli, ad Lig Shi Absrac We provide a geeral framework for compuig he sae desiy of a oisy sysem give he sequece of

More information

Approximately Quasi Inner Generalized Dynamics on Modules. { } t t R

Approximately Quasi Inner Generalized Dynamics on Modules. { } t t R Joural of Scieces, Islamic epublic of Ira 23(3): 245-25 (22) Uiversiy of Tehra, ISSN 6-4 hp://jscieces.u.ac.ir Approximaely Quasi Ier Geeralized Dyamics o Modules M. Mosadeq, M. Hassai, ad A. Nikam Deparme

More information

Four equations describe the dynamic solution to RBC model. Consumption-leisure efficiency condition. Consumption-investment efficiency condition

Four equations describe the dynamic solution to RBC model. Consumption-leisure efficiency condition. Consumption-investment efficiency condition LINEAR APPROXIMATION OF THE BASELINE RBC MODEL FEBRUARY, 202 Iroducio For f(, y, z ), mulivariable Taylor liear epasio aroud (, yz, ) f (, y, z) f(, y, z) + f (, y, z)( ) + f (, y, z)( y y) + f (, y, z)(

More information

Clock Skew and Signal Representation

Clock Skew and Signal Representation Clock Skew ad Sigal Represeaio Ch. 7 IBM Power 4 Chip 0/7/004 08 frequecy domai Program Iroducio ad moivaio Sequeial circuis, clock imig, Basic ools for frequecy domai aalysis Fourier series sigal represeaio

More information

Actuarial Society of India

Actuarial Society of India Acuarial Sociey of Idia EXAMINAIONS Jue 5 C4 (3) Models oal Marks - 5 Idicaive Soluio Q. (i) a) Le U deoe he process described by 3 ad V deoe he process described by 4. he 5 e 5 PU [ ] PV [ ] ( e ).538!

More information

METHOD OF THE EQUIVALENT BOUNDARY CONDITIONS IN THE UNSTEADY PROBLEM FOR ELASTIC DIFFUSION LAYER

METHOD OF THE EQUIVALENT BOUNDARY CONDITIONS IN THE UNSTEADY PROBLEM FOR ELASTIC DIFFUSION LAYER Maerials Physics ad Mechaics 3 (5) 36-4 Received: March 7 5 METHOD OF THE EQUIVAENT BOUNDARY CONDITIONS IN THE UNSTEADY PROBEM FOR EASTIC DIFFUSION AYER A.V. Zemsov * D.V. Tarlaovsiy Moscow Aviaio Isiue

More information

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/15.070J Fall 2013 Lecture 4 9/16/2013. Applications of the large deviation technique

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/15.070J Fall 2013 Lecture 4 9/16/2013. Applications of the large deviation technique MASSACHUSETTS ISTITUTE OF TECHOLOGY 6.265/5.070J Fall 203 Lecure 4 9/6/203 Applicaios of he large deviaio echique Coe.. Isurace problem 2. Queueig problem 3. Buffer overflow probabiliy Safey capial for

More information

On the Validity of the Pairs Bootstrap for Lasso Estimators

On the Validity of the Pairs Bootstrap for Lasso Estimators O he Validiy of he Pairs Boosrap for Lasso Esimaors Lorezo Campoovo Uiversiy of S.Galle Ocober 2014 Absrac We sudy he validiy of he pairs boosrap for Lasso esimaors i liear regressio models wih radom covariaes

More information

SUMMATION OF INFINITE SERIES REVISITED

SUMMATION OF INFINITE SERIES REVISITED SUMMATION OF INFINITE SERIES REVISITED I several aricles over he las decade o his web page we have show how o sum cerai iiie series icludig he geomeric series. We wa here o eed his discussio o he geeral

More information

Big O Notation for Time Complexity of Algorithms

Big O Notation for Time Complexity of Algorithms BRONX COMMUNITY COLLEGE of he Ciy Uiversiy of New York DEPARTMENT OF MATHEMATICS AND COMPUTER SCIENCE CSI 33 Secio E01 Hadou 1 Fall 2014 Sepember 3, 2014 Big O Noaio for Time Complexiy of Algorihms Time

More information

Lecture 9: Polynomial Approximations

Lecture 9: Polynomial Approximations CS 70: Complexiy Theory /6/009 Lecure 9: Polyomial Approximaios Isrucor: Dieer va Melkebeek Scribe: Phil Rydzewski & Piramaayagam Arumuga Naiar Las ime, we proved ha o cosa deph circui ca evaluae he pariy

More information

OLS bias for econometric models with errors-in-variables. The Lucas-critique Supplementary note to Lecture 17

OLS bias for econometric models with errors-in-variables. The Lucas-critique Supplementary note to Lecture 17 OLS bias for ecoomeric models wih errors-i-variables. The Lucas-criique Supplemeary oe o Lecure 7 RNy May 6, 03 Properies of OLS i RE models I Lecure 7 we discussed he followig example of a raioal expecaios

More information

Discrete-Time Signals and Systems. Introduction to Digital Signal Processing. Independent Variable. What is a Signal? What is a System?

Discrete-Time Signals and Systems. Introduction to Digital Signal Processing. Independent Variable. What is a Signal? What is a System? Discree-Time Sigals ad Sysems Iroducio o Digial Sigal Processig Professor Deepa Kudur Uiversiy of Toroo Referece: Secios. -.4 of Joh G. Proakis ad Dimiris G. Maolakis, Digial Sigal Processig: Priciples,

More information

Some Properties of Semi-E-Convex Function and Semi-E-Convex Programming*

Some Properties of Semi-E-Convex Function and Semi-E-Convex Programming* The Eighh Ieraioal Symposium o Operaios esearch ad Is Applicaios (ISOA 9) Zhagjiajie Chia Sepember 2 22 29 Copyrigh 29 OSC & APOC pp 33 39 Some Properies of Semi-E-Covex Fucio ad Semi-E-Covex Programmig*

More information

Paper 3A3 The Equations of Fluid Flow and Their Numerical Solution Handout 1

Paper 3A3 The Equations of Fluid Flow and Their Numerical Solution Handout 1 Paper 3A3 The Equaios of Fluid Flow ad Their Numerical Soluio Hadou Iroducio A grea ma fluid flow problems are ow solved b use of Compuaioal Fluid Damics (CFD) packages. Oe of he major obsacles o he good

More information

Sampling Example. ( ) δ ( f 1) (1/2)cos(12πt), T 0 = 1

Sampling Example. ( ) δ ( f 1) (1/2)cos(12πt), T 0 = 1 Samplig Example Le x = cos( 4π)cos( π). The fudameal frequecy of cos 4π fudameal frequecy of cos π is Hz. The ( f ) = ( / ) δ ( f 7) + δ ( f + 7) / δ ( f ) + δ ( f + ). ( f ) = ( / 4) δ ( f 8) + δ ( f

More information

BE.430 Tutorial: Linear Operator Theory and Eigenfunction Expansion

BE.430 Tutorial: Linear Operator Theory and Eigenfunction Expansion BE.43 Tuorial: Liear Operaor Theory ad Eigefucio Expasio (adaped fro Douglas Lauffeburger) 9//4 Moivaig proble I class, we ecouered parial differeial equaios describig rasie syses wih cheical diffusio.

More information

Inverse Heat Conduction Problem in a Semi-Infinite Circular Plate and its Thermal Deflection by Quasi-Static Approach

Inverse Heat Conduction Problem in a Semi-Infinite Circular Plate and its Thermal Deflection by Quasi-Static Approach Available a hp://pvamu.edu/aam Appl. Appl. Mah. ISSN: 93-9466 Vol. 5 Issue ue pp. 7 Previously Vol. 5 No. Applicaios ad Applied Mahemaics: A Ieraioal oural AAM Iverse Hea Coducio Problem i a Semi-Ifiie

More information

Pure Math 30: Explained!

Pure Math 30: Explained! ure Mah : Explaied! www.puremah.com 6 Logarihms Lesso ar Basic Expoeial Applicaios Expoeial Growh & Decay: Siuaios followig his ype of chage ca be modeled usig he formula: (b) A = Fuure Amou A o = iial

More information

Lecture 15: Three-tank Mixing and Lead Poisoning

Lecture 15: Three-tank Mixing and Lead Poisoning Lecure 15: Three-ak Miig ad Lead Poisoig Eigevalues ad eigevecors will be used o fid he soluio of a sysem for ukow fucios ha saisfy differeial equaios The ukow fucios will be wrie as a 1 colum vecor [

More information

NEWTON METHOD FOR DETERMINING THE OPTIMAL REPLENISHMENT POLICY FOR EPQ MODEL WITH PRESENT VALUE

NEWTON METHOD FOR DETERMINING THE OPTIMAL REPLENISHMENT POLICY FOR EPQ MODEL WITH PRESENT VALUE Yugoslav Joural of Operaios Research 8 (2008, Number, 53-6 DOI: 02298/YUJOR080053W NEWTON METHOD FOR DETERMINING THE OPTIMAL REPLENISHMENT POLICY FOR EPQ MODEL WITH PRESENT VALUE Jeff Kuo-Jug WU, Hsui-Li

More information

Notes 03 largely plagiarized by %khc

Notes 03 largely plagiarized by %khc 1 1 Discree-Time Covoluio Noes 03 largely plagiarized by %khc Le s begi our discussio of covoluio i discree-ime, sice life is somewha easier i ha domai. We sar wih a sigal x[] ha will be he ipu io our

More information

Additional Tables of Simulation Results

Additional Tables of Simulation Results Saisica Siica: Suppleme REGULARIZING LASSO: A CONSISTENT VARIABLE SELECTION METHOD Quefeg Li ad Ju Shao Uiversiy of Wiscosi, Madiso, Eas Chia Normal Uiversiy ad Uiversiy of Wiscosi, Madiso Supplemeary

More information

Theorem. Let H be a class of functions from a measurable space T to R. Assume that for every ɛ > 0 there exists a finite set of brackets

Theorem. Let H be a class of functions from a measurable space T to R. Assume that for every ɛ > 0 there exists a finite set of brackets STATISTICAL THEORY SUMMARY ANDREW TULLOCH Theorem Hoeffig s Iequaliy. Suose EX, a a X b. The E e X e b a 8... Uiform Laws of Large Numbers. Bic Coces Defiiio Covergece almos surely. A sequece X, N of raom

More information

Outline. simplest HMM (1) simple HMMs? simplest HMM (2) Parameter estimation for discrete hidden Markov models

Outline. simplest HMM (1) simple HMMs? simplest HMM (2) Parameter estimation for discrete hidden Markov models Oulie Parameer esimaio for discree idde Markov models Juko Murakami () ad Tomas Taylor (2). Vicoria Uiversiy of Welligo 2. Arizoa Sae Uiversiy Descripio of simple idde Markov models Maximum likeliood esimae

More information

Common Fixed Point Theorem in Intuitionistic Fuzzy Metric Space via Compatible Mappings of Type (K)

Common Fixed Point Theorem in Intuitionistic Fuzzy Metric Space via Compatible Mappings of Type (K) Ieraioal Joural of ahemaics Treds ad Techology (IJTT) Volume 35 umber 4- July 016 Commo Fixed Poi Theorem i Iuiioisic Fuzzy eric Sace via Comaible aigs of Tye (K) Dr. Ramaa Reddy Assisa Professor De. of

More information

Lecture 15 First Properties of the Brownian Motion

Lecture 15 First Properties of the Brownian Motion Lecure 15: Firs Properies 1 of 8 Course: Theory of Probabiliy II Term: Sprig 2015 Isrucor: Gorda Zikovic Lecure 15 Firs Properies of he Browia Moio This lecure deals wih some of he more immediae properies

More information

Calculus Limits. Limit of a function.. 1. One-Sided Limits...1. Infinite limits 2. Vertical Asymptotes...3. Calculating Limits Using the Limit Laws.

Calculus Limits. Limit of a function.. 1. One-Sided Limits...1. Infinite limits 2. Vertical Asymptotes...3. Calculating Limits Using the Limit Laws. Limi of a fucio.. Oe-Sided..... Ifiie limis Verical Asympoes... Calculaig Usig he Limi Laws.5 The Squeeze Theorem.6 The Precise Defiiio of a Limi......7 Coiuiy.8 Iermediae Value Theorem..9 Refereces..

More information

Numerical Solution of Parabolic Volterra Integro-Differential Equations via Backward-Euler Scheme

Numerical Solution of Parabolic Volterra Integro-Differential Equations via Backward-Euler Scheme America Joural of Compuaioal ad Applied Maemaics, (6): 77-8 DOI:.59/.acam.6. Numerical Soluio of Parabolic Volerra Iegro-Differeial Equaios via Bacward-Euler Sceme Ali Filiz Deparme of Maemaics, Ada Mederes

More information

Generalized Linear Models + Learning Fully Observed Bayes Nets

Generalized Linear Models + Learning Fully Observed Bayes Nets School of Compuer Sciece 0-708 Probabilisic Graphical Moels Geeralize Liear Moels + Learig Full Observe Baes Nes Reaigs: KF Chap. 7 Jora Chap. 8 Jora Chap. 9. 9. Ma Gormle Lecure 5 Jauar 7, 06 Machie Learig

More information

Supplementary Information for Thermal Noises in an Aqueous Quadrupole Micro- and Nano-Trap

Supplementary Information for Thermal Noises in an Aqueous Quadrupole Micro- and Nano-Trap Supplemeary Iformaio for Thermal Noises i a Aqueous Quadrupole Micro- ad Nao-Trap Jae Hyu Park ad Predrag S. Krsić * Physics Divisio, Oak Ridge Naioal Laboraory, Oak Ridge, TN 3783 E-mail: krsicp@orl.gov

More information

Natural Evolution Strategies Converge on Sphere Functions

Natural Evolution Strategies Converge on Sphere Functions Naural Evoluio Sraegies Coverge o Sphere Fucios Tom Schaul Coura Isiue of Mahemaical Scieces New York Uiversiy 7, Broaway, New York, NY 3 schaul@cims.yu.eu ABSTRACT This heoreical ivesigaio gives he firs

More information

MANY-SERVER QUEUES WITH CUSTOMER ABANDONMENT: A SURVEY OF DIFFUSION AND FLUID APPROXIMATIONS

MANY-SERVER QUEUES WITH CUSTOMER ABANDONMENT: A SURVEY OF DIFFUSION AND FLUID APPROXIMATIONS J Sys Sci Sys Eg (Mar 212) 21(1): 1-36 DOI: 1.17/s11518-12-5189-y ISSN: 14-3756 (Paper) 1861-9576 (Olie) CN11-2983/N MANY-SERVER QUEUES WITH CUSTOMER ABANDONMENT: A SURVEY OF DIFFUSION AND FLUID APPROXIMATIONS

More information

12 Getting Started With Fourier Analysis

12 Getting Started With Fourier Analysis Commuicaios Egieerig MSc - Prelimiary Readig Geig Sared Wih Fourier Aalysis Fourier aalysis is cocered wih he represeaio of sigals i erms of he sums of sie, cosie or complex oscillaio waveforms. We ll

More information

2 f(x) dx = 1, 0. 2f(x 1) dx d) 1 4t t6 t. t 2 dt i)

2 f(x) dx = 1, 0. 2f(x 1) dx d) 1 4t t6 t. t 2 dt i) Mah PracTes Be sure o review Lab (ad all labs) There are los of good quesios o i a) Sae he Mea Value Theorem ad draw a graph ha illusraes b) Name a impora heorem where he Mea Value Theorem was used i he

More information

Recursive Prediction Error Identification of Nonlinear State Space Models

Recursive Prediction Error Identification of Nonlinear State Space Models Recursive Preicio Error Ieificaio of Noliear ae pace Moels orbjör Wigre ysems a Corol, Deparme of Iformaio echology, Uppsala Uiversiy, PO Bo 337, E-7505 Uppsala, WEDEN. orbjor.wigre@i.uu.se Key Wors -

More information