Continuous Belief Functions: Focal Intervals Properties.

Size: px
Start display at page:

Download "Continuous Belief Functions: Focal Intervals Properties."

Transcription

1 Contnuous Belef Functons: Focal Intervals Propertes. Jean-Marc Vannobel To cte ths verson: Jean-Marc Vannobel. Contnuous Belef Functons: Focal Intervals Propertes.. BELIEF 212, May 212, Compègne, France. pp hal-7668 HAL Id: hal Submtted on 17 Dec 212 HAL s a mult-dscplnary open access archve for the depost and dssemnaton of scentfc research documents, whether they are publshed or not. The documents may come from teachng and research nsttutons n France or abroad, or from publc or prvate research centers. L archve ouverte plurdscplnare HAL, est destnée au dépôt et à la dffuson de documents scentfques de nveau recherche, publés ou non, émanant des établssements d ensegnement et de recherche franças ou étrangers, des laboratores publcs ou prvés.

2 Contnuous belef functons: focal ntervals propertes Jean-Marc Vannobel Abstract The set of focal elements resultng from a conunctve or dsunctve combnaton of consonant belef functons s regretfully not consonant and s thus very dffcult to represent. In ths paper, we propose a graphcal representaton of the cross product of two focal sets orgnatng from unvarate Gaussan pdfs. Ths representaton allows to represent ntal focal ntervals as well as focal ntervals resultng from a combnaton operaton. We show n case of conunctve or dsunctve combnaton operatons, that the whole doman can be separated n four subsets of ntervals havng same propertes. At last, we focus on dentcal length focal ntervals resultng from a combnaton. We show that such ntervals are organed n connected lne segments on our graphcal representaton. 1 Introducton 1.1 Sources of nformaton Consder a source of nformaton S wth knowledge modeled by a unvarate convex (unmodal and consonant) probablty densty functon betf of a contnuous random varable X. The support of betf s called Ω = [Ω,Ω+ ] wth Ω,Ω+ R [4]. The mode and the varance of betf are respectvely noted µ and σ 2. Suppose now E = (F,m ), the pece of evdence deduced from betf. E s totally descrbed by the par composed of m, the Least Commted sopgnstc basc belef densty (bbd) deduced from betf [1] [4] and F = {I Ω m (I) > }, the focal set of ntervals wth elements n Ω. Jean-Marc Vannobel LAGIS, Unversté Llle1, e-mal: ean-marc.vannobel@unv-llle1.fr 1

3 2 Jean-Marc Vannobel 1.2 Focal ntervals An nterval A = [A,A + ] wth A,A + R such as m (A) s called focal nterval of E thus A F. All elements of F are nested ntervals n case of a consonant pdf betf and correspond to horontal cuts of betf as shown n fgure 1(a). It s convenent to label the elements of F accordng to ther ncluson order by a contnuous ndex. Ths can be done for nstance wrt the pdf value at focal nterval bounds [2] or wrt the half-length of the focal nterval [3]. Ths last opton allows n general to defne a sngle bbd s expresson for a whole famly of pdfs [6]. In case of symmetrcal pdfs lke Gaussan ones as well as Laplace ones, focal ntervals can be labeled by an ndex such as A = [A,A + ] wth: = x µ, R +, (1) σ A = µ σ,a [Ω,µ], (2) A + = µ + σ,a + [µ,ω + ]. (3) 2 Focal sets graphcal representatons 2.1 General consderatons We consder n what follows two peces of evdence E = (F,m ) and E = (F,m ) deduced respectvely from the Gaussan pdfs betf (x;µ,σ 2) and betf (x;µ,σ 2) wth µ µ. Focal ntervals are denoted by A k wth k the value taken by the label obtaned usng relaton (1). We assume the use of Gaussan pdfs snce many sensors model uncertanty by such pdfs but any other symmetrcal bell shaped pdfs lke Laplace or Cauchy ones would also serve the purpose. F, s the focal set resultng from a conunctve (resp. dsunctve) combnaton of E and E. Elements of F, correspond to the non empty ntersecton (resp. unon) of pars n F F. The content of F, and the length dependences between ts elements depend of course on the chosen combnaton rule. 2.2 Bell shaped probablty densty functons The graphcal representaton proposed n fgure 1(a) shows the focal set F obtaned from a Gaussan pdf Betf. Elements of F are ordered wrt label, dfferng n that pont from the graphcal representaton proposed by Strat [5].

4 Contnuous belef functons: focal ntervals propertes 3 When labelng focal ntervals wrt ther length as defned n (1), the focal set F s encompassed by two symmetrcal half-lnes defnng an sosceles trangle. Equatons of these half-lnes are deduced from relatons (2) and (3) and correspond to: = A µ σ, (4) = A+ µ σ. As shown n fgure 1(b), ths s a convenent way to graphcally compare focal ntervals comng from dfferent focal sets. One can see n ths fgure the result of the ntersecton or unon of two ntervals A k F and B l F whch are ndexed resp. by k and l. For nstance, t also allows to show the doman of ntervals B F that do not ntersect wth A k (f any). It s obvous from relaton (1) that the label value of these B ntervals s n [, A k + µ ). 2.3 Bounds relatons of F F elements Fgure 2(a) shows the pars of ntervals (A x,b x ) F F havng a common bound x Ω. The ndex pars ( x,x ) R+2 correspondng to (A x,b x ) draw the lnes 1, 2 and 3 as shown n Fgure 2(a). These lnes are defned by 1 : 1 : x = µ µ 2 : x = µ µ 3 : x = µ µ + σ σ x, x [,µ ], x, x [µ,µ ], + σ x, x [µ,+ ]. Pars ( x,x ) on the half lne called 1 lead to x µ such as: { A x = B x = x, A x + B x +. Pars ( x,x ) on lne segment 2 correspond to x [µ,µ ] such as: (5) (6) A x + = B x = x. (7) At last, pars ( x,x ) on the half lne 3 correspond to x µ 2 such as: { A x + = B x + = x, A x B x. (8) 1 proof s not gven here due to lack of space

5 4 Jean-Marc Vannobel To outlne exstng partal conflct k, between the agents E and E [6], the label values of the modes are denoted by K and K : { K = µ µ σ, K = µ µ. (9) Relatons (5) show that the absolute value of lne drectons of 1, 2 and 3 are equal to arctg( σ ) and correspond to angles α 1,α 2 and α 3 n fgure 2(a). 2.4 Focal ntervals ntersecton or unon overvew As shown n fgure 2(b), focal ntervals A k F and B l F can drectly be drawn on the chart by respectvely vertcal and horontal lne segments snce 1, 2 and 3 correspond to the focal ntervals bounds. The path defned by half-lnes 1, 2 and 3 covers Ω. It becomes thus easy to analye pars n F F to deduce ther ntersecton or unon. For nstance, A k ntervals shown n fgure 2(b) are such as A k B l = [ B l,a ] k+ and B l = [ A k,b ] l+. When consderng k and l respectvely as horontal and vertcal cursors t s also possble to fnd the lmts of ntervals ntersectng or ncludng one another or not. 2.5 Partcular domans Fgure 2(b) shows also that the 1, 2 and 3 lnes separate F F n four domans called D 1, D 2, D 3 ans D 2 4. For µ < µ,,, defnng pars (A,B ) F F, we have: > K + σ A < K + σ A < K + σ, > K σ A B < K σ, A B B (D 1 ), B (D 2 ),, > K + σ / { },A,B (D 3 ), = (D 4 ). (1) When µ 1 = µ 2 only D 1 and D 2 exst and are separated by the half-lne 2 = σ1 σ proof s not gven here due to lack of space

6 Contnuous belef functons: focal ntervals propertes Consonant subsets of F, The focal set F, obtaned after a conunctve or a dsunctve combnaton operaton of E and E s composed of an nfnte number of nested focal ntervals subsets. These consonant subsets appear both n D 1, D 2 and D 3 shown n fgure 2(b) and are partally represented n fgure 1(b) wth the dark gray area. These subsets are not dsont and consequently f the same nterval belongs to several of them, t s necessary to ntegrate to get ts total weght. The doman D 4 dffers from the other ones as t s empty n case of a conunctve combnaton operaton and composed of nested non convex ntervals n case of a dsunctve one. 3 Same length ntervals resultng from ntersecton and unon operatons of focal ones 3.1 Intersecton of focal ntervals The length l of the ntersecton of the pars (A,B ) F F can be deduced from the characterstcs of D 1, D 2, D 3 and D 4 gven by relatons (1). l only depends on the pdfs betf and betf parameters and the focal ntervals ndexes and : D 1 : A B = A, l(a B ) = 2σ, D 2 : A B = B, l(a B ) = 2, D 3 : A B = [B,A + ], l(a B ) = µ 2 µ 1 + σ +, D 4 : A B =, l(a B ) =. (11) We can fnd the elements of F F havng an dentcal ntersecton length L = l(a B ) wth the help of relatons (11). For nstance, the ndexes of these elements n D 3 are: thus: = L + µ 2 µ 1 σ (12) = A σ wth A = L + K. (13) Ths s graphcally represented n fgure 2(c) by the lne segment 6 bounded by ponts (F,C) and (D,E) and crossng the pont (, ) = (,A) wth A as defned n relaton (13). Pars (A,B ) n D 1 havng also an ntersecton length equal to L correspond to the ponts of the half-lne 5 crossng (F,C) snce A B = A n D 1. Pars of ntervals on the half lne 7 crossng (D,E) n fgure 2(c) have an ntersecton length equal to L too snce A

7 6 Jean-Marc Vannobel B = B n D 2. At last, values A to F are qute nterdependent 3 dependng on K and K values. Ths s due to the symmetry propertes of straght lnes 1 to 7 n fgures 2(b) and 2(c). Many relatons dependng on these parameters lead to the value of L such as L = 2 (C K ) = 2σ (D K ) for nstance. 3.2 Unon of focal ntervals One can deduce from the relatons (1) related to the domans D 1, D 2, D 3, D 4 that for the unon operaton of pars (A,B ) F F, we have: D 1 : A B = B, l(a B ) = 2, D 2 : A B = A, l(a B ) = 2σ, D 3 : A B = [B,A + ], l(a B ) = µ 2 µ 1 + σ +, D 4 : A B =, l(a B ) = 2(σ + ). (14) As expressed n (14), concernng D 3, one can wrte: l(a B ) µ 2 µ 1 = σ +. (15) From (15), pars (A,B ) D 3 havng a constant unon length L = l(a ) satsfy thus: B = A σ wth A = L K. (16) Ths relaton corresponds n fgure 2(d) to the lne segment 6 bounded by ponts (F,C) and (D,E) and crossng the pont (, ) = (,A) wth A as defned n relaton (16). Pars n D 1 havng a unon length equal to L correspond to the ponts of the lne segment 8. Ths s also the case n D 2 for the ponts of the lne segment 9. As n the case of ntersecton operaton, many relatons 4 lnk the value of L to the parameters of pdfs betf and betf. For nstance we have L = 2 C = 2σ D. 4 Concluson and Acknowledgment As we have seen, the focal set F, obtaned n case of a conunctve (resp. dsunctve) combnaton of two peces of evdence E and E wth consonant 3 proof s not gven here due to lack of space 4 proof s not gven here due to lack of space

8 Contnuous belef functons: focal ntervals propertes 7 Betf - A k k = k k k Betf ( A ) Betf ( A ) k k A A x k A = [ k A, k A ] F k l k A A k U l B k A U l B l B k A U B = x (a) Focal ntervals doman F resultng from a Gaussan pdf. (b) Intersecton and unon of focal ntervals resultng from two Gaussan pdfs. Fg. 1 Focal ntervals graphcal representaton relatvely to the label value. focal domans s not as heterogeneous as t seems to be. Intervals belongng to F, are sorted nto only four domans. In each of these domans, pars (A F,B F ) of focal ntervals share common propertes regardng ntersecton A B and unon A B. These four domans can be graphcally represented n a lnear space where they are separated by straght lnes when the focal sets F and F are composed of centered and consonant ntervals. At last, elements of F, havng a same length are lnked by lnear relatons. Ths can be useful n problems where nterval lengths have to be taken nto account. Authors are ndebted to J. Klen for the revew of ths work. References 1. Caron, F., Rstc, B., Duflos, E., Vanheeghe, P.: Least commted basc belef densty nduced by a multvarate gaussan: formulaton wth applcatons. Internatonal Journal on Approxmate Reasonng, vol. 48, no. 2, (28) 2. Doré P.-E., Martn A., Khenchaf A.: Constructng of least commted basc belef densty lnked to a multmodal probablty densty, n COGIS, Pars (Fr), Rstc, B., Smets,P.: Belef functon theory on the contnuous space wth an applcaton to model based classfcaton. In IPMU 4, Informaton Processng and Management of Uncertanty n Knowledge Based Systems, pp , Pars (Fr) (24) 4. Smets, P.: Belef functons on real numbers. Internatonal Journal of Approxmate Reasonng, vol. 4, no. 3, (25)

9 8 Jean-Marc Vannobel 5. Strat, T.H.: Contnuous belef functons for evdental reasonng, n AAAI-84 (Ed., Natonal Conference on Artfcal Intellgence, , (1984) 6. Vannobel, J.-M.: Contnuous belef functons: sngletons plausblty functon n conunctve and dsunctve combnaton operatons of consonant bbds. Proceedngs of Workshop on the theory of belef functons, CDROM, 6 pages. Brest(Fr) (21) Z Z 1 D 1 k A 1 X 1 3 l l B D 3 3 l B K 2 x K 2 x 2 X K 3 Z D 4 K k A k D 2 Z (a) Pars of ntervals havng a common lower or upper bound (b) Domans of dentcal propertes Z Z 5 A 4 A 4 C 6 C 8 6 K E 7 K E 9 F K D B Z F K D B Z (c) Same length ntervals resultng from a conunctve combnaton (d) Same length ntervals resultng from a dsunctve combnaton Fg. 2 Graphcal representatons of focal ntervals propertes.

A generalization of a trace inequality for positive definite matrices

A generalization of a trace inequality for positive definite matrices A generalzaton of a trace nequalty for postve defnte matrces Elena Veronca Belmega, Marc Jungers, Samson Lasaulce To cte ths verson: Elena Veronca Belmega, Marc Jungers, Samson Lasaulce. A generalzaton

More information

3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X

3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X Statstcs 1: Probablty Theory II 37 3 EPECTATION OF SEVERAL RANDOM VARIABLES As n Probablty Theory I, the nterest n most stuatons les not on the actual dstrbuton of a random vector, but rather on a number

More information

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur Module 3 LOSSY IMAGE COMPRESSION SYSTEMS Verson ECE IIT, Kharagpur Lesson 6 Theory of Quantzaton Verson ECE IIT, Kharagpur Instructonal Objectves At the end of ths lesson, the students should be able to:

More information

The Order Relation and Trace Inequalities for. Hermitian Operators

The Order Relation and Trace Inequalities for. Hermitian Operators Internatonal Mathematcal Forum, Vol 3, 08, no, 507-57 HIKARI Ltd, wwwm-hkarcom https://doorg/0988/mf088055 The Order Relaton and Trace Inequaltes for Hermtan Operators Y Huang School of Informaton Scence

More information

International Journal of Mathematical Archive-3(3), 2012, Page: Available online through ISSN

International Journal of Mathematical Archive-3(3), 2012, Page: Available online through   ISSN Internatonal Journal of Mathematcal Archve-3(3), 2012, Page: 1136-1140 Avalable onlne through www.ma.nfo ISSN 2229 5046 ARITHMETIC OPERATIONS OF FOCAL ELEMENTS AND THEIR CORRESPONDING BASIC PROBABILITY

More information

CHAPTER-5 INFORMATION MEASURE OF FUZZY MATRIX AND FUZZY BINARY RELATION

CHAPTER-5 INFORMATION MEASURE OF FUZZY MATRIX AND FUZZY BINARY RELATION CAPTER- INFORMATION MEASURE OF FUZZY MATRI AN FUZZY BINARY RELATION Introducton The basc concept of the fuzz matr theor s ver smple and can be appled to socal and natural stuatons A branch of fuzz matr

More information

ON A DETERMINATION OF THE INITIAL FUNCTIONS FROM THE OBSERVED VALUES OF THE BOUNDARY FUNCTIONS FOR THE SECOND-ORDER HYPERBOLIC EQUATION

ON A DETERMINATION OF THE INITIAL FUNCTIONS FROM THE OBSERVED VALUES OF THE BOUNDARY FUNCTIONS FOR THE SECOND-ORDER HYPERBOLIC EQUATION Advanced Mathematcal Models & Applcatons Vol.3, No.3, 2018, pp.215-222 ON A DETERMINATION OF THE INITIAL FUNCTIONS FROM THE OBSERVED VALUES OF THE BOUNDARY FUNCTIONS FOR THE SECOND-ORDER HYPERBOLIC EUATION

More information

Geometrically exact multi-layer beams with a rigid interconnection

Geometrically exact multi-layer beams with a rigid interconnection Geometrcally exact mult-layer beams wth a rgd nterconnecton Leo Škec, Gordan Jelenć To cte ths verson: Leo Škec, Gordan Jelenć. Geometrcally exact mult-layer beams wth a rgd nterconnecton. 2nd ECCOMAS

More information

Fuzzy Boundaries of Sample Selection Model

Fuzzy Boundaries of Sample Selection Model Proceedngs of the 9th WSES Internatonal Conference on ppled Mathematcs, Istanbul, Turkey, May 7-9, 006 (pp309-34) Fuzzy Boundares of Sample Selecton Model L. MUHMD SFIIH, NTON BDULBSH KMIL, M. T. BU OSMN

More information

Using T.O.M to Estimate Parameter of distributions that have not Single Exponential Family

Using T.O.M to Estimate Parameter of distributions that have not Single Exponential Family IOSR Journal of Mathematcs IOSR-JM) ISSN: 2278-5728. Volume 3, Issue 3 Sep-Oct. 202), PP 44-48 www.osrjournals.org Usng T.O.M to Estmate Parameter of dstrbutons that have not Sngle Exponental Famly Jubran

More information

Affine transformations and convexity

Affine transformations and convexity Affne transformatons and convexty The purpose of ths document s to prove some basc propertes of affne transformatons nvolvng convex sets. Here are a few onlne references for background nformaton: http://math.ucr.edu/

More information

Power law and dimension of the maximum value for belief distribution with the max Deng entropy

Power law and dimension of the maximum value for belief distribution with the max Deng entropy Power law and dmenson of the maxmum value for belef dstrbuton wth the max Deng entropy Bngy Kang a, a College of Informaton Engneerng, Northwest A&F Unversty, Yanglng, Shaanx, 712100, Chna. Abstract Deng

More information

Uncertainty in measurements of power and energy on power networks

Uncertainty in measurements of power and energy on power networks Uncertanty n measurements of power and energy on power networks E. Manov, N. Kolev Department of Measurement and Instrumentaton, Techncal Unversty Sofa, bul. Klment Ohrdsk No8, bl., 000 Sofa, Bulgara Tel./fax:

More information

APPENDIX 2 FITTING A STRAIGHT LINE TO OBSERVATIONS

APPENDIX 2 FITTING A STRAIGHT LINE TO OBSERVATIONS Unversty of Oulu Student Laboratory n Physcs Laboratory Exercses n Physcs 1 1 APPEDIX FITTIG A STRAIGHT LIE TO OBSERVATIOS In the physcal measurements we often make a seres of measurements of the dependent

More information

DETERMINATION OF UNCERTAINTY ASSOCIATED WITH QUANTIZATION ERRORS USING THE BAYESIAN APPROACH

DETERMINATION OF UNCERTAINTY ASSOCIATED WITH QUANTIZATION ERRORS USING THE BAYESIAN APPROACH Proceedngs, XVII IMEKO World Congress, June 7, 3, Dubrovn, Croata Proceedngs, XVII IMEKO World Congress, June 7, 3, Dubrovn, Croata TC XVII IMEKO World Congress Metrology n the 3rd Mllennum June 7, 3,

More information

Statistical Inference. 2.3 Summary Statistics Measures of Center and Spread. parameters ( population characteristics )

Statistical Inference. 2.3 Summary Statistics Measures of Center and Spread. parameters ( population characteristics ) Ismor Fscher, 8//008 Stat 54 / -8.3 Summary Statstcs Measures of Center and Spread Dstrbuton of dscrete contnuous POPULATION Random Varable, numercal True center =??? True spread =???? parameters ( populaton

More information

Solving Nonlinear Differential Equations by a Neural Network Method

Solving Nonlinear Differential Equations by a Neural Network Method Solvng Nonlnear Dfferental Equatons by a Neural Network Method Luce P. Aarts and Peter Van der Veer Delft Unversty of Technology, Faculty of Cvlengneerng and Geoscences, Secton of Cvlengneerng Informatcs,

More information

Kernel Methods and SVMs Extension

Kernel Methods and SVMs Extension Kernel Methods and SVMs Extenson The purpose of ths document s to revew materal covered n Machne Learnng 1 Supervsed Learnng regardng support vector machnes (SVMs). Ths document also provdes a general

More information

Uncertainty and auto-correlation in. Measurement

Uncertainty and auto-correlation in. Measurement Uncertanty and auto-correlaton n arxv:1707.03276v2 [physcs.data-an] 30 Dec 2017 Measurement Markus Schebl Federal Offce of Metrology and Surveyng (BEV), 1160 Venna, Austra E-mal: markus.schebl@bev.gv.at

More information

Module 2. Random Processes. Version 2 ECE IIT, Kharagpur

Module 2. Random Processes. Version 2 ECE IIT, Kharagpur Module Random Processes Lesson 6 Functons of Random Varables After readng ths lesson, ou wll learn about cdf of functon of a random varable. Formula for determnng the pdf of a random varable. Let, X be

More information

A be a probability space. A random vector

A be a probability space. A random vector Statstcs 1: Probablty Theory II 8 1 JOINT AND MARGINAL DISTRIBUTIONS In Probablty Theory I we formulate the concept of a (real) random varable and descrbe the probablstc behavor of ths random varable by

More information

An Application of Fuzzy Hypotheses Testing in Radar Detection

An Application of Fuzzy Hypotheses Testing in Radar Detection Proceedngs of the th WSES Internatonal Conference on FUZZY SYSEMS n pplcaton of Fuy Hypotheses estng n Radar Detecton.K.ELSHERIF, F.M.BBDY, G.M.BDELHMID Department of Mathematcs Mltary echncal Collage

More information

MATH 5707 HOMEWORK 4 SOLUTIONS 2. 2 i 2p i E(X i ) + E(Xi 2 ) ä i=1. i=1

MATH 5707 HOMEWORK 4 SOLUTIONS 2. 2 i 2p i E(X i ) + E(Xi 2 ) ä i=1. i=1 MATH 5707 HOMEWORK 4 SOLUTIONS CİHAN BAHRAN 1. Let v 1,..., v n R m, all lengths v are not larger than 1. Let p 1,..., p n [0, 1] be arbtrary and set w = p 1 v 1 + + p n v n. Then there exst ε 1,..., ε

More information

An improving dynamic programming algorithm to solve the shortest path problem with time windows

An improving dynamic programming algorithm to solve the shortest path problem with time windows An mprovng dynamc programmng algorthm to solve the shortest path problem wth tme wndows Nora Touat Moungla, Lucas Létocart, Anass Nagh To cte ths verson: Nora Touat Moungla, Lucas Létocart, Anass Nagh.

More information

Inner Product. Euclidean Space. Orthonormal Basis. Orthogonal

Inner Product. Euclidean Space. Orthonormal Basis. Orthogonal Inner Product Defnton 1 () A Eucldean space s a fnte-dmensonal vector space over the reals R, wth an nner product,. Defnton 2 (Inner Product) An nner product, on a real vector space X s a symmetrc, blnear,

More information

ANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U)

ANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U) Econ 413 Exam 13 H ANSWERS Settet er nndelt 9 deloppgaver, A,B,C, som alle anbefales å telle lkt for å gøre det ltt lettere å stå. Svar er gtt . Unfortunately, there s a prntng error n the hnt of

More information

Basic Regular Expressions. Introduction. Introduction to Computability. Theory. Motivation. Lecture4: Regular Expressions

Basic Regular Expressions. Introduction. Introduction to Computability. Theory. Motivation. Lecture4: Regular Expressions Introducton to Computablty Theory Lecture: egular Expressons Prof Amos Israel Motvaton If one wants to descrbe a regular language, La, she can use the a DFA, Dor an NFA N, such L ( D = La that that Ths

More information

A how to guide to second quantization method.

A how to guide to second quantization method. Phys. 67 (Graduate Quantum Mechancs Sprng 2009 Prof. Pu K. Lam. Verson 3 (4/3/2009 A how to gude to second quantzaton method. -> Second quantzaton s a mathematcal notaton desgned to handle dentcal partcle

More information

Spectral Graph Theory and its Applications September 16, Lecture 5

Spectral Graph Theory and its Applications September 16, Lecture 5 Spectral Graph Theory and ts Applcatons September 16, 2004 Lecturer: Danel A. Spelman Lecture 5 5.1 Introducton In ths lecture, we wll prove the followng theorem: Theorem 5.1.1. Let G be a planar graph

More information

Another converse of Jensen s inequality

Another converse of Jensen s inequality Another converse of Jensen s nequalty Slavko Smc Abstract. We gve the best possble global bounds for a form of dscrete Jensen s nequalty. By some examples ts frutfulness s shown. 1. Introducton Throughout

More information

Fundamental loop-current method using virtual voltage sources technique for special cases

Fundamental loop-current method using virtual voltage sources technique for special cases Fundamental loop-current method usng vrtual voltage sources technque for specal cases George E. Chatzaraks, 1 Marna D. Tortorel 1 and Anastasos D. Tzolas 1 Electrcal and Electroncs Engneerng Departments,

More information

Binomial transforms of the modified k-fibonacci-like sequence

Binomial transforms of the modified k-fibonacci-like sequence Internatonal Journal of Mathematcs and Computer Scence, 14(2019, no. 1, 47 59 M CS Bnomal transforms of the modfed k-fbonacc-lke sequence Youngwoo Kwon Department of mathematcs Korea Unversty Seoul, Republc

More information

A CLASS OF RECURSIVE SETS. Florentin Smarandache University of New Mexico 200 College Road Gallup, NM 87301, USA

A CLASS OF RECURSIVE SETS. Florentin Smarandache University of New Mexico 200 College Road Gallup, NM 87301, USA A CLASS OF RECURSIVE SETS Florentn Smarandache Unversty of New Mexco 200 College Road Gallup, NM 87301, USA E-mal: smarand@unmedu In ths artcle one bulds a class of recursve sets, one establshes propertes

More information

Composite Hypotheses testing

Composite Hypotheses testing Composte ypotheses testng In many hypothess testng problems there are many possble dstrbutons that can occur under each of the hypotheses. The output of the source s a set of parameters (ponts n a parameter

More information

Comparison of the Population Variance Estimators. of 2-Parameter Exponential Distribution Based on. Multiple Criteria Decision Making Method

Comparison of the Population Variance Estimators. of 2-Parameter Exponential Distribution Based on. Multiple Criteria Decision Making Method Appled Mathematcal Scences, Vol. 7, 0, no. 47, 07-0 HIARI Ltd, www.m-hkar.com Comparson of the Populaton Varance Estmators of -Parameter Exponental Dstrbuton Based on Multple Crtera Decson Makng Method

More information

8.1 Arc Length. What is the length of a curve? How can we approximate it? We could do it following the pattern we ve used before

8.1 Arc Length. What is the length of a curve? How can we approximate it? We could do it following the pattern we ve used before .1 Arc Length hat s the length of a curve? How can we approxmate t? e could do t followng the pattern we ve used before Use a sequence of ncreasngly short segments to approxmate the curve: As the segments

More information

Lecture 3: Probability Distributions

Lecture 3: Probability Distributions Lecture 3: Probablty Dstrbutons Random Varables Let us begn by defnng a sample space as a set of outcomes from an experment. We denote ths by S. A random varable s a functon whch maps outcomes nto the

More information

MAE140 - Linear Circuits - Winter 16 Final, March 16, 2016

MAE140 - Linear Circuits - Winter 16 Final, March 16, 2016 ME140 - Lnear rcuts - Wnter 16 Fnal, March 16, 2016 Instructons () The exam s open book. You may use your class notes and textbook. You may use a hand calculator wth no communcaton capabltes. () You have

More information

Lecture 12: Discrete Laplacian

Lecture 12: Discrete Laplacian Lecture 12: Dscrete Laplacan Scrbe: Tanye Lu Our goal s to come up wth a dscrete verson of Laplacan operator for trangulated surfaces, so that we can use t n practce to solve related problems We are mostly

More information

Lecture Notes on Linear Regression

Lecture Notes on Linear Regression Lecture Notes on Lnear Regresson Feng L fl@sdueducn Shandong Unversty, Chna Lnear Regresson Problem In regresson problem, we am at predct a contnuous target value gven an nput feature vector We assume

More information

Double Layered Fuzzy Planar Graph

Double Layered Fuzzy Planar Graph Global Journal of Pure and Appled Mathematcs. ISSN 0973-768 Volume 3, Number 0 07), pp. 7365-7376 Research Inda Publcatons http://www.rpublcaton.com Double Layered Fuzzy Planar Graph J. Jon Arockaraj Assstant

More information

j) = 1 (note sigma notation) ii. Continuous random variable (e.g. Normal distribution) 1. density function: f ( x) 0 and f ( x) dx = 1

j) = 1 (note sigma notation) ii. Continuous random variable (e.g. Normal distribution) 1. density function: f ( x) 0 and f ( x) dx = 1 Random varables Measure of central tendences and varablty (means and varances) Jont densty functons and ndependence Measures of assocaton (covarance and correlaton) Interestng result Condtonal dstrbutons

More information

CIS526: Machine Learning Lecture 3 (Sept 16, 2003) Linear Regression. Preparation help: Xiaoying Huang. x 1 θ 1 output... θ M x M

CIS526: Machine Learning Lecture 3 (Sept 16, 2003) Linear Regression. Preparation help: Xiaoying Huang. x 1 θ 1 output... θ M x M CIS56: achne Learnng Lecture 3 (Sept 6, 003) Preparaton help: Xaoyng Huang Lnear Regresson Lnear regresson can be represented by a functonal form: f(; θ) = θ 0 0 +θ + + θ = θ = 0 ote: 0 s a dummy attrbute

More information

Conjugacy and the Exponential Family

Conjugacy and the Exponential Family CS281B/Stat241B: Advanced Topcs n Learnng & Decson Makng Conjugacy and the Exponental Famly Lecturer: Mchael I. Jordan Scrbes: Bran Mlch 1 Conjugacy In the prevous lecture, we saw conjugate prors for the

More information

Learning Theory: Lecture Notes

Learning Theory: Lecture Notes Learnng Theory: Lecture Notes Lecturer: Kamalka Chaudhur Scrbe: Qush Wang October 27, 2012 1 The Agnostc PAC Model Recall that one of the constrants of the PAC model s that the data dstrbuton has to be

More information

Probability Theory. The nth coefficient of the Taylor series of f(k), expanded around k = 0, gives the nth moment of x as ( ik) n n!

Probability Theory. The nth coefficient of the Taylor series of f(k), expanded around k = 0, gives the nth moment of x as ( ik) n n! 8333: Statstcal Mechancs I Problem Set # 3 Solutons Fall 3 Characterstc Functons: Probablty Theory The characterstc functon s defned by fk ep k = ep kpd The nth coeffcent of the Taylor seres of fk epanded

More information

EEE 241: Linear Systems

EEE 241: Linear Systems EEE : Lnear Systems Summary #: Backpropagaton BACKPROPAGATION The perceptron rule as well as the Wdrow Hoff learnng were desgned to tran sngle layer networks. They suffer from the same dsadvantage: they

More information

Georgia Tech PHYS 6124 Mathematical Methods of Physics I

Georgia Tech PHYS 6124 Mathematical Methods of Physics I Georga Tech PHYS 624 Mathematcal Methods of Physcs I Instructor: Predrag Cvtanovć Fall semester 202 Homework Set #7 due October 30 202 == show all your work for maxmum credt == put labels ttle legends

More information

Chapter 8 Indicator Variables

Chapter 8 Indicator Variables Chapter 8 Indcator Varables In general, e explanatory varables n any regresson analyss are assumed to be quanttatve n nature. For example, e varables lke temperature, dstance, age etc. are quanttatve n

More information

Numerical Heat and Mass Transfer

Numerical Heat and Mass Transfer Master degree n Mechancal Engneerng Numercal Heat and Mass Transfer 06-Fnte-Dfference Method (One-dmensonal, steady state heat conducton) Fausto Arpno f.arpno@uncas.t Introducton Why we use models and

More information

The Quadratic Trigonometric Bézier Curve with Single Shape Parameter

The Quadratic Trigonometric Bézier Curve with Single Shape Parameter J. Basc. Appl. Sc. Res., (3541-546, 01 01, TextRoad Publcaton ISSN 090-4304 Journal of Basc and Appled Scentfc Research www.textroad.com The Quadratc Trgonometrc Bézer Curve wth Sngle Shape Parameter Uzma

More information

A new construction of 3-separable matrices via an improved decoding of Macula s construction

A new construction of 3-separable matrices via an improved decoding of Macula s construction Dscrete Optmzaton 5 008 700 704 Contents lsts avalable at ScenceDrect Dscrete Optmzaton journal homepage: wwwelsevercom/locate/dsopt A new constructon of 3-separable matrces va an mproved decodng of Macula

More information

Complement of Type-2 Fuzzy Shortest Path Using Possibility Measure

Complement of Type-2 Fuzzy Shortest Path Using Possibility Measure Intern. J. Fuzzy Mathematcal rchve Vol. 5, No., 04, 9-7 ISSN: 30 34 (P, 30 350 (onlne Publshed on 5 November 04 www.researchmathsc.org Internatonal Journal of Complement of Type- Fuzzy Shortest Path Usng

More information

On cyclic of Steiner system (v); V=2,3,5,7,11,13

On cyclic of Steiner system (v); V=2,3,5,7,11,13 On cyclc of Stener system (v); V=,3,5,7,,3 Prof. Dr. Adl M. Ahmed Rana A. Ibraham Abstract: A stener system can be defned by the trple S(t,k,v), where every block B, (=,,,b) contans exactly K-elementes

More information

Cokriging Partial Grades - Application to Block Modeling of Copper Deposits

Cokriging Partial Grades - Application to Block Modeling of Copper Deposits Cokrgng Partal Grades - Applcaton to Block Modelng of Copper Deposts Serge Séguret 1, Julo Benscell 2 and Pablo Carrasco 2 Abstract Ths work concerns mneral deposts made of geologcal bodes such as breccas

More information

Algebraic independence results on the generating Lambert series of the powers of a fixed integer

Algebraic independence results on the generating Lambert series of the powers of a fixed integer Algebrac ndependence results on the generatng Lambert seres of the powers of a fxed nteger Peter Bundschuh, Kejo Väänänen To cte ths verson: Peter Bundschuh, Kejo Väänänen. Algebrac ndependence results

More information

Lecture 17 : Stochastic Processes II

Lecture 17 : Stochastic Processes II : Stochastc Processes II 1 Contnuous-tme stochastc process So far we have studed dscrete-tme stochastc processes. We studed the concept of Makov chans and martngales, tme seres analyss, and regresson analyss

More information

APPENDIX A Some Linear Algebra

APPENDIX A Some Linear Algebra APPENDIX A Some Lnear Algebra The collecton of m, n matrces A.1 Matrces a 1,1,..., a 1,n A = a m,1,..., a m,n wth real elements a,j s denoted by R m,n. If n = 1 then A s called a column vector. Smlarly,

More information

Research Article Green s Theorem for Sign Data

Research Article Green s Theorem for Sign Data Internatonal Scholarly Research Network ISRN Appled Mathematcs Volume 2012, Artcle ID 539359, 10 pages do:10.5402/2012/539359 Research Artcle Green s Theorem for Sgn Data Lous M. Houston The Unversty of

More information

Complete subgraphs in multipartite graphs

Complete subgraphs in multipartite graphs Complete subgraphs n multpartte graphs FLORIAN PFENDER Unverstät Rostock, Insttut für Mathematk D-18057 Rostock, Germany Floran.Pfender@un-rostock.de Abstract Turán s Theorem states that every graph G

More information

A Robust Method for Calculating the Correlation Coefficient

A Robust Method for Calculating the Correlation Coefficient A Robust Method for Calculatng the Correlaton Coeffcent E.B. Nven and C. V. Deutsch Relatonshps between prmary and secondary data are frequently quantfed usng the correlaton coeffcent; however, the tradtonal

More information

Difference Equations

Difference Equations Dfference Equatons c Jan Vrbk 1 Bascs Suppose a sequence of numbers, say a 0,a 1,a,a 3,... s defned by a certan general relatonshp between, say, three consecutve values of the sequence, e.g. a + +3a +1

More information

COMPLEX NUMBERS AND QUADRATIC EQUATIONS

COMPLEX NUMBERS AND QUADRATIC EQUATIONS COMPLEX NUMBERS AND QUADRATIC EQUATIONS INTRODUCTION We know that x 0 for all x R e the square of a real number (whether postve, negatve or ero) s non-negatve Hence the equatons x, x, x + 7 0 etc are not

More information

Expected Value and Variance

Expected Value and Variance MATH 38 Expected Value and Varance Dr. Neal, WKU We now shall dscuss how to fnd the average and standard devaton of a random varable X. Expected Value Defnton. The expected value (or average value, or

More information

XII.3 The EM (Expectation-Maximization) Algorithm

XII.3 The EM (Expectation-Maximization) Algorithm XII.3 The EM (Expectaton-Maxzaton) Algorth Toshnor Munaata 3/7/06 The EM algorth s a technque to deal wth varous types of ncoplete data or hdden varables. It can be appled to a wde range of learnng probles

More information

Volume 18 Figure 1. Notation 1. Notation 2. Observation 1. Remark 1. Remark 2. Remark 3. Remark 4. Remark 5. Remark 6. Theorem A [2]. Theorem B [2].

Volume 18 Figure 1. Notation 1. Notation 2. Observation 1. Remark 1. Remark 2. Remark 3. Remark 4. Remark 5. Remark 6. Theorem A [2]. Theorem B [2]. Bulletn of Mathematcal Scences and Applcatons Submtted: 016-04-07 ISSN: 78-9634, Vol. 18, pp 1-10 Revsed: 016-09-08 do:10.1805/www.scpress.com/bmsa.18.1 Accepted: 016-10-13 017 ScPress Ltd., Swtzerland

More information

This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and

This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and Ths artcle appeared n a journal publshed by Elsevr. The attached copy s furnshed to the author for nternal non-commercal research and educaton use, ncludng for nstructon at the authors nsttuton and sharng

More information

The Minimum Universal Cost Flow in an Infeasible Flow Network

The Minimum Universal Cost Flow in an Infeasible Flow Network Journal of Scences, Islamc Republc of Iran 17(2): 175-180 (2006) Unversty of Tehran, ISSN 1016-1104 http://jscencesutacr The Mnmum Unversal Cost Flow n an Infeasble Flow Network H Saleh Fathabad * M Bagheran

More information

Structure and Drive Paul A. Jensen Copyright July 20, 2003

Structure and Drive Paul A. Jensen Copyright July 20, 2003 Structure and Drve Paul A. Jensen Copyrght July 20, 2003 A system s made up of several operatons wth flow passng between them. The structure of the system descrbes the flow paths from nputs to outputs.

More information

Appendix B. Criterion of Riemann-Stieltjes Integrability

Appendix B. Criterion of Riemann-Stieltjes Integrability Appendx B. Crteron of Remann-Steltes Integrablty Ths note s complementary to [R, Ch. 6] and [T, Sec. 3.5]. The man result of ths note s Theorem B.3, whch provdes the necessary and suffcent condtons for

More information

Linear, affine, and convex sets and hulls In the sequel, unless otherwise specified, X will denote a real vector space.

Linear, affine, and convex sets and hulls In the sequel, unless otherwise specified, X will denote a real vector space. Lnear, affne, and convex sets and hulls In the sequel, unless otherwse specfed, X wll denote a real vector space. Lnes and segments. Gven two ponts x, y X, we defne xy = {x + t(y x) : t R} = {(1 t)x +

More information

A Bayes Algorithm for the Multitask Pattern Recognition Problem Direct Approach

A Bayes Algorithm for the Multitask Pattern Recognition Problem Direct Approach A Bayes Algorthm for the Multtask Pattern Recognton Problem Drect Approach Edward Puchala Wroclaw Unversty of Technology, Char of Systems and Computer etworks, Wybrzeze Wyspanskego 7, 50-370 Wroclaw, Poland

More information

2E Pattern Recognition Solutions to Introduction to Pattern Recognition, Chapter 2: Bayesian pattern classification

2E Pattern Recognition Solutions to Introduction to Pattern Recognition, Chapter 2: Bayesian pattern classification E395 - Pattern Recognton Solutons to Introducton to Pattern Recognton, Chapter : Bayesan pattern classfcaton Preface Ths document s a soluton manual for selected exercses from Introducton to Pattern Recognton

More information

Convexity preserving interpolation by splines of arbitrary degree

Convexity preserving interpolation by splines of arbitrary degree Computer Scence Journal of Moldova, vol.18, no.1(52), 2010 Convexty preservng nterpolaton by splnes of arbtrary degree Igor Verlan Abstract In the present paper an algorthm of C 2 nterpolaton of dscrete

More information

/ n ) are compared. The logic is: if the two

/ n ) are compared. The logic is: if the two STAT C141, Sprng 2005 Lecture 13 Two sample tests One sample tests: examples of goodness of ft tests, where we are testng whether our data supports predctons. Two sample tests: called as tests of ndependence

More information

Modelli Clamfim Equazione del Calore Lezione ottobre 2014

Modelli Clamfim Equazione del Calore Lezione ottobre 2014 CLAMFIM Bologna Modell 1 @ Clamfm Equazone del Calore Lezone 17 15 ottobre 2014 professor Danele Rtell danele.rtell@unbo.t 1/24? Convoluton The convoluton of two functons g(t) and f(t) s the functon (g

More information

Lectures - Week 4 Matrix norms, Conditioning, Vector Spaces, Linear Independence, Spanning sets and Basis, Null space and Range of a Matrix

Lectures - Week 4 Matrix norms, Conditioning, Vector Spaces, Linear Independence, Spanning sets and Basis, Null space and Range of a Matrix Lectures - Week 4 Matrx norms, Condtonng, Vector Spaces, Lnear Independence, Spannng sets and Bass, Null space and Range of a Matrx Matrx Norms Now we turn to assocatng a number to each matrx. We could

More information

Affine and Riemannian Connections

Affine and Riemannian Connections Affne and Remannan Connectons Semnar Remannan Geometry Summer Term 2015 Prof Dr Anna Wenhard and Dr Gye-Seon Lee Jakob Ullmann Notaton: X(M) space of smooth vector felds on M D(M) space of smooth functons

More information

Computing Correlated Equilibria in Multi-Player Games

Computing Correlated Equilibria in Multi-Player Games Computng Correlated Equlbra n Mult-Player Games Chrstos H. Papadmtrou Presented by Zhanxang Huang December 7th, 2005 1 The Author Dr. Chrstos H. Papadmtrou CS professor at UC Berkley (taught at Harvard,

More information

Homogenization of reaction-diffusion processes in a two-component porous medium with a non-linear flux-condition on the interface

Homogenization of reaction-diffusion processes in a two-component porous medium with a non-linear flux-condition on the interface Homogenzaton of reacton-dffuson processes n a two-component porous medum wth a non-lnear flux-condton on the nterface Internatonal Conference on Numercal and Mathematcal Modelng of Flow and Transport n

More information

Asymptotics of the Solution of a Boundary Value. Problem for One-Characteristic Differential. Equation Degenerating into a Parabolic Equation

Asymptotics of the Solution of a Boundary Value. Problem for One-Characteristic Differential. Equation Degenerating into a Parabolic Equation Nonl. Analyss and Dfferental Equatons, ol., 4, no., 5 - HIKARI Ltd, www.m-har.com http://dx.do.org/.988/nade.4.456 Asymptotcs of the Soluton of a Boundary alue Problem for One-Characterstc Dfferental Equaton

More information

On the Multicriteria Integer Network Flow Problem

On the Multicriteria Integer Network Flow Problem BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 5, No 2 Sofa 2005 On the Multcrtera Integer Network Flow Problem Vassl Vasslev, Marana Nkolova, Maryana Vassleva Insttute of

More information

Exercise Solutions to Real Analysis

Exercise Solutions to Real Analysis xercse Solutons to Real Analyss Note: References refer to H. L. Royden, Real Analyss xersze 1. Gven any set A any ɛ > 0, there s an open set O such that A O m O m A + ɛ. Soluton 1. If m A =, then there

More information

Module 3: Element Properties Lecture 1: Natural Coordinates

Module 3: Element Properties Lecture 1: Natural Coordinates Module 3: Element Propertes Lecture : Natural Coordnates Natural coordnate system s bascally a local coordnate system whch allows the specfcaton of a pont wthn the element by a set of dmensonless numbers

More information

find (x): given element x, return the canonical element of the set containing x;

find (x): given element x, return the canonical element of the set containing x; COS 43 Sprng, 009 Dsjont Set Unon Problem: Mantan a collecton of dsjont sets. Two operatons: fnd the set contanng a gven element; unte two sets nto one (destructvely). Approach: Canoncal element method:

More information

Perron Vectors of an Irreducible Nonnegative Interval Matrix

Perron Vectors of an Irreducible Nonnegative Interval Matrix Perron Vectors of an Irreducble Nonnegatve Interval Matrx Jr Rohn August 4 2005 Abstract As s well known an rreducble nonnegatve matrx possesses a unquely determned Perron vector. As the man result of

More information

Chapter 1. Probability

Chapter 1. Probability Chapter. Probablty Mcroscopc propertes of matter: quantum mechancs, atomc and molecular propertes Macroscopc propertes of matter: thermodynamcs, E, H, C V, C p, S, A, G How do we relate these two propertes?

More information

20. Mon, Oct. 13 What we have done so far corresponds roughly to Chapters 2 & 3 of Lee. Now we turn to Chapter 4. The first idea is connectedness.

20. Mon, Oct. 13 What we have done so far corresponds roughly to Chapters 2 & 3 of Lee. Now we turn to Chapter 4. The first idea is connectedness. 20. Mon, Oct. 13 What we have done so far corresponds roughly to Chapters 2 & 3 of Lee. Now we turn to Chapter 4. The frst dea s connectedness. Essentally, we want to say that a space cannot be decomposed

More information

Bezier curves. Michael S. Floater. August 25, These notes provide an introduction to Bezier curves. i=0

Bezier curves. Michael S. Floater. August 25, These notes provide an introduction to Bezier curves. i=0 Bezer curves Mchael S. Floater August 25, 211 These notes provde an ntroducton to Bezer curves. 1 Bernsten polynomals Recall that a real polynomal of a real varable x R, wth degree n, s a functon of the

More information

Linear Approximation with Regularization and Moving Least Squares

Linear Approximation with Regularization and Moving Least Squares Lnear Approxmaton wth Regularzaton and Movng Least Squares Igor Grešovn May 007 Revson 4.6 (Revson : March 004). 5 4 3 0.5 3 3.5 4 Contents: Lnear Fttng...4. Weghted Least Squares n Functon Approxmaton...

More information

Accepted Manuscript. Guillaume Marrelec, Habib Benali. S (08)00059-X DOI: /j.spl Reference: STAPRO 4923.

Accepted Manuscript. Guillaume Marrelec, Habib Benali. S (08)00059-X DOI: /j.spl Reference: STAPRO 4923. Accepted Manuscrpt Condtonal ndependence between two varables gven any condtonng subset mples block dagonal covarance matrx for multvarate Gaussan dstrbutons Gullaume Marrelec Habb Benal PII: S167-7152(8)59-X

More information

Mean Field / Variational Approximations

Mean Field / Variational Approximations Mean Feld / Varatonal Appromatons resented by Jose Nuñez 0/24/05 Outlne Introducton Mean Feld Appromaton Structured Mean Feld Weghted Mean Feld Varatonal Methods Introducton roblem: We have dstrbuton but

More information

Line Drawing and Clipping Week 1, Lecture 2

Line Drawing and Clipping Week 1, Lecture 2 CS 43 Computer Graphcs I Lne Drawng and Clppng Week, Lecture 2 Davd Breen, Wllam Regl and Maxm Peysakhov Geometrc and Intellgent Computng Laboratory Department of Computer Scence Drexel Unversty http://gcl.mcs.drexel.edu

More information

Engineering Risk Benefit Analysis

Engineering Risk Benefit Analysis Engneerng Rsk Beneft Analyss.55, 2.943, 3.577, 6.938, 0.86, 3.62, 6.862, 22.82, ESD.72, ESD.72 RPRA 2. Elements of Probablty Theory George E. Apostolaks Massachusetts Insttute of Technology Sprng 2007

More information

CALCULUS CLASSROOM CAPSULES

CALCULUS CLASSROOM CAPSULES CALCULUS CLASSROOM CAPSULES SESSION S86 Dr. Sham Alfred Rartan Valley Communty College salfred@rartanval.edu 38th AMATYC Annual Conference Jacksonvlle, Florda November 8-, 202 2 Calculus Classroom Capsules

More information

Maximum Likelihood Estimation of Binary Dependent Variables Models: Probit and Logit. 1. General Formulation of Binary Dependent Variables Models

Maximum Likelihood Estimation of Binary Dependent Variables Models: Probit and Logit. 1. General Formulation of Binary Dependent Variables Models ECO 452 -- OE 4: Probt and Logt Models ECO 452 -- OE 4 Maxmum Lkelhood Estmaton of Bnary Dependent Varables Models: Probt and Logt hs note demonstrates how to formulate bnary dependent varables models

More information

PHYS 705: Classical Mechanics. Newtonian Mechanics

PHYS 705: Classical Mechanics. Newtonian Mechanics 1 PHYS 705: Classcal Mechancs Newtonan Mechancs Quck Revew of Newtonan Mechancs Basc Descrpton: -An dealzed pont partcle or a system of pont partcles n an nertal reference frame [Rgd bodes (ch. 5 later)]

More information

Week3, Chapter 4. Position and Displacement. Motion in Two Dimensions. Instantaneous Velocity. Average Velocity

Week3, Chapter 4. Position and Displacement. Motion in Two Dimensions. Instantaneous Velocity. Average Velocity Week3, Chapter 4 Moton n Two Dmensons Lecture Quz A partcle confned to moton along the x axs moves wth constant acceleraton from x =.0 m to x = 8.0 m durng a 1-s tme nterval. The velocty of the partcle

More information

FACTORIZATION IN KRULL MONOIDS WITH INFINITE CLASS GROUP

FACTORIZATION IN KRULL MONOIDS WITH INFINITE CLASS GROUP C O L L O Q U I U M M A T H E M A T I C U M VOL. 80 1999 NO. 1 FACTORIZATION IN KRULL MONOIDS WITH INFINITE CLASS GROUP BY FLORIAN K A I N R A T H (GRAZ) Abstract. Let H be a Krull monod wth nfnte class

More information

Analysis of the Magnetomotive Force of a Three-Phase Winding with Concentrated Coils and Different Symmetry Features

Analysis of the Magnetomotive Force of a Three-Phase Winding with Concentrated Coils and Different Symmetry Features Analyss of the Magnetomotve Force of a Three-Phase Wndng wth Concentrated Cols and Dfferent Symmetry Features Deter Gerlng Unversty of Federal Defense Munch, Neubberg, 85579, Germany Emal: Deter.Gerlng@unbw.de

More information