Applied Mathematics Letters. Asymptotic distribution of two-protected nodes in random binary search trees

Size: px
Start display at page:

Download "Applied Mathematics Letters. Asymptotic distribution of two-protected nodes in random binary search trees"

Transcription

1 Applied Mathematics Letters 25 (2012) Cotets lists available at SciVerse ScieceDirect Applied Mathematics Letters joral homepage: wwwelseviercom/locate/aml Asymptotic distribtio of two-protected odes i radom biary search trees Hosam M Mahmod a, Mar Daiel Ward b, a Departmet of Statistics, The George Washigto Uiversity, Washigto, DC 20052, USA b Departmet of Statistics, Prde Uiversity, West Lafayette, IN 47907, USA a r t i c l e i f o a b s t r a c t Article history: Received 27 Jaary 2012 Accepted 6 Je 2012 Dedicated to the memory of Philippe Flajolet We derive exact momets of the mber of 2-protected odes i biary search trees grow from radom permtatios Frthermore, we show that a properly ormalized versio of this tree parameter coverges to a Gassia limit 2012 Elsevier Ltd All rights reserved Keywords: Biary search trees Radom strctre Combiatorial probability Asymptotic aalysis 1 Itrodctio The stdy of 2-protected odes i classes of radom trees is i the voge Cheo ad Shapiro [1] ivestigate the average mber of 2-protected odes i labeled, ordered trees ad i ary biary trees (those with 0, 1, or 2 childre per ode) Masor [2] cosiders the average mber of 2-protected odes i -ary trees Recetly, D ad Prodiger [3] have aalyzed the average of this parameter i radom digital trees, with a iform probability model I this article, we cosider the mber of 2-protected odes i a radom biary search tree (BST) These are biary trees, lie those i the 2-ary case of Masor [2], bt differ i their derlyig probability distribtio Those i [2] are iformly distribted, ie, all trees of the same size (mber of odes) are eqally liely I cotrast, the BST grows from a radom permtatio that idces a BST probability model, which is oiform The BST model is of prime importace i compter sciece as it represets the bacboe of some fdametal algorithms, sch as Qicsort (see Kth [4] or Mahmod [5]), ad are basic efficiet data strctres i their ow right (see Mahmod [6]) The BST grows from a iformly radom permtatio (π 1, π 2,, π ), of {1, 2,, }, as follows I the compter sciece jargo, elemets of the permtatio are ofte called eys The first ey π 1 goes ito the root ode of a tree, with distigished left ad right sbtrees (which are empty as of yet) The secod ey is gided to the left sbtree, if it is smaller tha the root ey (ie, if π 2 < π 1 ), where it is iserted i a ode ad lied as a left child of the root; otherwise (ie, if π 2 > π 1 ) the secod ey goes ito the right sbtree, where it is iserted i a ode ad lied as a right child of the root Sbseqet eys go to the left or right sbtrees, accordig to whether they are smaller tha the root ey or ot, where they are iserted recrsively i the sbtree by the same algorithm Note that whe the permtatios of {1, 2,, } are eqally liely, they give rise to a oiform probability distribtio o the shapes of BST We call sch distribtio the BST probability model Correspodig athor addresses: hosam@gwed (HM Mahmod), mdw@prdeed (MD Ward) /$ see frot matter 2012 Elsevier Ltd All rights reserved doi:101016/jaml

2 HM Mahmod, MD Ward / Applied Mathematics Letters 25 (2012) Fig 1 Example of a biary search tree correspodig to the permtatio (5, 9, 6, 4, 7, 2, 3, 1, 8); 2-protected odes i bold This BST probability model is deemed more relevat to compter sciece applicatios tha the iform model o biary trees as it coforms more closely to the atre of data arisig i sortig ad searchig applicatios For istace, data samples of size tae from ay arbitrary cotios distribtio have ras that are almost srely (sice ties occr with probability 0) a radom permtatio o {1, 2,, } Sch real-mbered data ca be assimilated by their ras to bild a biary tree with the aforemetioed BST distribtio A ode with o descedats i a BST is a leaf A ode i a BST is said to be a 2-protected ode if its distace (measred i mber of edges) to the earest descedat leaf is at least 2 Fig 1 shows a BST of size 9 grow from the permtatio (5, 9, 6, 4, 7, 2, 3, 1, 8) The odes represeted by bold circles are 2-protected I this ote, we ivestigate the mber of 2-protected odes i a BST Or program does ot stop at the derivatio of mea, bt coties to fid asymptotic distribtios 2 Momets of the mber of 2-protected odes Let the mber of 2-protected odes i a radom BST of size be X I the tree show i Fig 1, X 9 = 4 Let U be the size i the left sbtree of the root, ad so 1 U is the size of the right sbtree I view of the BST probability model, the root is eqally liely to be ay of the mbers i the set {1, 2,, } Ths, U is iformly distribted o the set {0,, 1}, ad so symmetrically is 1 U Let R be the evet that the root ode is ot 2-protected Evet R occrs if: the root is a leaf itself ( = 1), or both childre of the root are leaves (possible whe = 3), or the root has exactly oe child that is a leaf For 1, we have a stochastic recrrece for X It is the combied mber of 2-protected odes i the two sbtrees of the root, pls 1 (to accot for the root beig 2-protected) less R occrs Ths, we have a eqality i distribtio, amely, X D = X U + X 1 U R (Note: the tilded radom variable X 1 U is coditioally idepedet of X U (give U )) The variables X 0, X 1, X 2 are always 0 We are sig a idicator otatio, ie, 1 R = 1, if R occrs, ad 0 otherwise Ths, for the momet geeratig fctio φ (t) := E[e X t ] of X, we have φ (t) = E e (φ U +φ 1 U +1 1 R )t Whe 4, we see that R oly occrs if U = 1 (ie, the left child of the root is a leaf) or 1 U = 1 (ie, the right child of the root is a leaf) (For 4, both childre of the root caot simltaeosly be leaves) Sice X U ad X 1 U are coditioally idepedet (give U ), a recrrece eses by coditioig o U Namely, for 4, we have φ (t) = et = et 0 1 1, 2 φ (t) φ 1 (t) + 2 φ 2(t) 1 φ (t) φ 1 (t) + 2 φ 2(t)(1 e t ) (1) Differetiatig r times with respect to t, the settig t = 0, gives a recrsio for E[X r ] As r icreases, the recrrece eqatios qicly become more complicated, a pheomeo commoly called the combiatorial explosio It is sfficiet for or prpose to get a exact soltio for the recrrece relatios for the first two momets, ad from there we shall maage to get a shortct to the higher asymptotic momets

3 2220 HM Mahmod, MD Ward / Applied Mathematics Letters 25 (2012) For r = 1 ad 4, we obtai a recrrece for the mea, amely, E[X ] = 2 1 E[X ] The recrrece for E[X ] ca be solved by stadard methods sch as differecig, for example If we deote Eq (2) as S(), the for 5, we see S() S( 1) has telescopig sms that disappear, ad the resltig liear recrrece ca be easily solved, with bodary coditios E[X 0 ] = E[X 1 ] = E[X 2 ] = 0, E[X 3 ] = 2/3, ad E[X 4 ] = 5/6 The bodary coditio = 4 agrees with the geeral form, ad we get a simple soltio for the mea Theorem 21 Let X deote the mber of 2-protected odes i a biary search tree grow from a iformly chose radom permtatio of {1,, } The we have E[X ] = 11 19, for 4 For r = 2, we se (1) to develop a recrrece for the secod momet: E[X 2 ] = 2 1 E[X 2 ] E[X ] E[X ] E[X 1 ] 4 E[X 2] + 2, valid for 4 With E[X ] ow determied, we ca solve the recrrece for E[X 2 ] Solvig the recrrece for the secod momet is ot qite as simple as solvig that for the first momet For istace, differecig does ot shave off the sms A more straightforward strategy is to gess a soltio, the prove it by idctio This procedre yields the followig reslt Theorem 22 Let X deote the mber of 2-protected odes i a biary search tree grow from a iformly chose radom permtatio of {1,, } The we have E[X 2 ] = , for 8, 100 ad the variace follows: Var[X ] = E[X 2 ] (E[X ]) 2 = , for 8 Note the exact cacellatio of the qadratic terms, leavig oly a liear variace, which gives a chace for asymptotic ormality to hold, as it fits icely ito the two momets ad a recrrece paradigm give by Pittel [7] Momets of arbitrarily high degree ca be fod similarly For istace, we have E[X 3 ] = , for 12, E[X 4 ] = , for 16, E[X 5 ] = , for 20, etc, ad i geeral, we cojectre the followig Cojectre 21 Let X deote the mber of 2-protected odes i a biary search tree grow from a iformly chose radom permtatio of {1,, } For each fixed iteger 1, there exists a polyomial p () of degree, the leadig term of which is (11/), sch that E[X ] = p (), for all 4 (2) 3 Asymptotic ormality The mai reslt of this ote is the followig Theorem 31 Let X be the mber of 2-protected odes i a radom biary search tree grow from a iformly chose radom permtatio of {1,, } The X, properly ormalized, coverges i distribtio, amely, X 11 D N 0, 29

4 HM Mahmod, MD Ward / Applied Mathematics Letters 25 (2012) Proof Let X = 1/2 (X 11 ), ad X 11 (t) = E exp t t = exp 11 t, be its momet geeratig fctio The recrrece (1) ca be ormalized i the form exp 11 exp exp 11 = 1 exp 11 φ X 1 exp exp 11 1 exp, which we ca write as exp exp 11 1 () = + 2 φ 2 X exp exp 11( 1 ) I view of Pittel s paradigm [7], a limit () (the momet geeratig fctio of a limitig radom variable X ) exists, as Passage to the limit i the latter relatio yields 1 1 () = lim c 1 + lim O( 3/2 ) Pt / = x, to represet the last relatio as ( 1)/ () = lim ( x, ) ( x 1, ) x,, x, =0 where x, = x, x 1, is the differece operator, ad the smmatio idex x, moves p i icremets of size 1/ By the sal iterpretatio of Riema itegrals, we fially write () = 1 y=0 ( y ) ( 1 y ) dy This itegral fctioal eqatio has the fctio e c2 2 /2 as a soltio This fctio is the momet geeratig fctio of the ormal N (0, c 2 ) radom variable By Lévy s cotiity theorem we get the desired covergece i distribtio: X 11 D N (0, c 2 ), for a appropriate vale of c 2 Of corse, it mst be 29, the coefficiet of the leadig asymptotic term i the variace 4 Exteded biary search trees BSTs are ofte exteded by addig special exteral odes as childre A sfficiet mber of these exteral odes are spplied to each origial ode (ow thoght of as iteral) to mae its otdegree eqal to two I this variat, the 2-protected odes are cshioed from the exteral odes by at least oe iteral ode As a example, see Fig 2, i which we have added the exteral odes to the tree i Fig 1, ad we have agai oted the 2-protected odes for this modified model i bold If X deotes the mber of 2-protected odes i exteded biary trees, the we have E[X ] = 1 3 2, for 2, 3 ad Var[X ] = 2 + 2, for 4

5 2222 HM Mahmod, MD Ward / Applied Mathematics Letters 25 (2012) Fig 2 Example of a exteded biary search tree for the permtatio (5, 9, 6, 4, 7, 2, 3, 1, 8); 2-protected odes i bold The correspodig cetral limit reslt is X 1 3 D N 0, 2 These reslts ca be obtaied by very similar methods as those we applied to the exteded BST However, most of these reslts for exteded BSTs are already implied i the pblished literatre For istace, i the exteded BST the 2-protected odes are the odes of otdegree 2 i the tree before it got exteded The exact average of these appears i [8] The asymptotic distribtio appears i [9], where he ses a m-depedet cetral limit theorem for statioary radom variables, de to Hoeffdig ad Robbis [10] Mahmod [11] gives a accot of a proof based o modelig by Pólya r models The oly thig ew here is the exact variace, which we get via the exact secod momet, E[X 2 ] = , for 4 Aother ew aspect is that we ca agai se or recrsive methods to develop exact higher momets for the mber of 2-protected odes i a exteded BST, eg, E[X 3 ] = , for 6, 315 E[X 4 ] = , for 8, 675 E[X 5 ] = , for 10, etc I geeral, we cojectre the followig Cojectre 41 Let X deote the mber of 2-protected odes i a exteded biary search tree grow from a iformly chose radom permtatio of {1,, } For each fixed iteger 1, there exists a polyomial p () of degree, the leadig term of which is 1/3, sch that E[X ] = p (), for all 2 Acowledgmets This research was doe while the first athor was visitig Prde Uiversity The spport the first athor received from Prde s Departmet of Statistics is greatly appreciated The secod athor was spported by NSF Sciece & Techology Ceter for Sciece of Iformatio Grat CCF Refereces [1] G-S Cheo, LW Shapiro, Protected poits i ordered trees, Applied Mathematics Letters 21 (2008) [2] T Masor, Protected poits i -ary trees, Applied Mathematics Letters 24 (2011) [3] RR D, H Prodiger, Notes o protected odes i digital search trees, Applied Mathematics Letters 25 (2012) [4] DE Kth, The Art of Compter Programmig, secod ed, i: Sortig ad Searchig, vol 3, Addiso-Wesley, Readig, MA, 1998, Origially pblished i 1973 [5] HM Mahmod, Sortig: A Distribtio Theory, Wiley, New Yor, NY, 2000 [6] HM Mahmod, Evoltio of Radom Search Trees, Wiley, New Yor, NY, 1992 [7] B Pittel, Normal covergece problem? two momets ad a recrrece may be the cles, The Aals of Applied Probability 9 (1999) [8] HM Mahmod, The expected distribtio of degrees i radom biary search trees, The Compter Joral 29 (1986) [9] L Devroye, Limit laws for local coters i radom biary search trees, Radom Strctres ad Algorithms 2 (1991) [10] W Hoeffdig, H Robbis, The cetral limit theorem for depedet radom variables, De Mathematical Joral 15 (1948) [11] HM Mahmod, Pólya Ur Models, Chapma, Orlado, FL, 2008

Numerical Methods for Finding Multiple Solutions of a Superlinear Problem

Numerical Methods for Finding Multiple Solutions of a Superlinear Problem ISSN 1746-7659, Eglad, UK Joral of Iformatio ad Comptig Sciece Vol 2, No 1, 27, pp 27- Nmerical Methods for Fidig Mltiple Soltios of a Sperliear Problem G A Afrozi +, S Mahdavi, Z Naghizadeh Departmet

More information

THE CONSERVATIVE DIFFERENCE SCHEME FOR THE GENERALIZED ROSENAU-KDV EQUATION

THE CONSERVATIVE DIFFERENCE SCHEME FOR THE GENERALIZED ROSENAU-KDV EQUATION Zho, J., et al.: The Coservative Differece Scheme for the Geeralized THERMAL SCIENCE, Year 6, Vol., Sppl. 3, pp. S93-S9 S93 THE CONSERVATIVE DIFFERENCE SCHEME FOR THE GENERALIZED ROSENAU-KDV EQUATION by

More information

Partial Differential Equations

Partial Differential Equations EE 84 Matematical Metods i Egieerig Partial Differetial Eqatios Followig are some classical partial differetial eqatios were is assmed to be a fctio of two or more variables t (time) ad y (spatial coordiates).

More information

On Arithmetic Means of Sequences Generated by a Periodic Function

On Arithmetic Means of Sequences Generated by a Periodic Function Caad Math Bll Vol 4 () 1999 pp 184 189 O Arithmetic Meas of Seqeces Geerated by a Periodic Fctio Giovai Fiorito Abstract I this paper we prove the covergece of arithmetic meas of seqeces geerated by a

More information

4. Partial Sums and the Central Limit Theorem

4. Partial Sums and the Central Limit Theorem 1 of 10 7/16/2009 6:05 AM Virtual Laboratories > 6. Radom Samples > 1 2 3 4 5 6 7 4. Partial Sums ad the Cetral Limit Theorem The cetral limit theorem ad the law of large umbers are the two fudametal theorems

More information

too many conditions to check!!

too many conditions to check!! Vector Spaces Aioms of a Vector Space closre Defiitio : Let V be a o empty set of vectors with operatios : i. Vector additio :, v є V + v є V ii. Scalar mltiplicatio: li є V k є V where k is scalar. The,

More information

Sequences A sequence of numbers is a function whose domain is the positive integers. We can see that the sequence

Sequences A sequence of numbers is a function whose domain is the positive integers. We can see that the sequence Sequeces A sequece of umbers is a fuctio whose domai is the positive itegers. We ca see that the sequece 1, 1, 2, 2, 3, 3,... is a fuctio from the positive itegers whe we write the first sequece elemet

More information

Open problem in orthogonal polynomials

Open problem in orthogonal polynomials Ope problem i orthogoal polyomials Abdlaziz D. Alhaidari Sadi Ceter for Theoretical Physics, P.O. Box 374, Jeddah 438, Sadi Arabia E-mail: haidari@sctp.org.sa URL: http://www.sctp.org.sa/haidari Abstract:

More information

62. Power series Definition 16. (Power series) Given a sequence {c n }, the series. c n x n = c 0 + c 1 x + c 2 x 2 + c 3 x 3 +

62. Power series Definition 16. (Power series) Given a sequence {c n }, the series. c n x n = c 0 + c 1 x + c 2 x 2 + c 3 x 3 + 62. Power series Defiitio 16. (Power series) Give a sequece {c }, the series c x = c 0 + c 1 x + c 2 x 2 + c 3 x 3 + is called a power series i the variable x. The umbers c are called the coefficiets of

More information

Introduction. Question: Why do we need new forms of parametric curves? Answer: Those parametric curves discussed are not very geometric.

Introduction. Question: Why do we need new forms of parametric curves? Answer: Those parametric curves discussed are not very geometric. Itrodctio Qestio: Why do we eed ew forms of parametric crves? Aswer: Those parametric crves discssed are ot very geometric. Itrodctio Give sch a parametric form, it is difficlt to kow the derlyig geometry

More information

A sequence of numbers is a function whose domain is the positive integers. We can see that the sequence

A sequence of numbers is a function whose domain is the positive integers. We can see that the sequence Sequeces A sequece of umbers is a fuctio whose domai is the positive itegers. We ca see that the sequece,, 2, 2, 3, 3,... is a fuctio from the positive itegers whe we write the first sequece elemet as

More information

Approximation of the Likelihood Ratio Statistics in Competing Risks Model Under Informative Random Censorship From Both Sides

Approximation of the Likelihood Ratio Statistics in Competing Risks Model Under Informative Random Censorship From Both Sides Approimatio of the Lielihood Ratio Statistics i Competig Riss Model Uder Iformative Radom Cesorship From Both Sides Abdrahim A. Abdshrov Natioal Uiversity of Uzbeista Departmet of Theory Probability ad

More information

Infinite Sequences and Series

Infinite Sequences and Series Chapter 6 Ifiite Sequeces ad Series 6.1 Ifiite Sequeces 6.1.1 Elemetary Cocepts Simply speakig, a sequece is a ordered list of umbers writte: {a 1, a 2, a 3,...a, a +1,...} where the elemets a i represet

More information

MAT1026 Calculus II Basic Convergence Tests for Series

MAT1026 Calculus II Basic Convergence Tests for Series MAT026 Calculus II Basic Covergece Tests for Series Egi MERMUT 202.03.08 Dokuz Eylül Uiversity Faculty of Sciece Departmet of Mathematics İzmir/TURKEY Cotets Mootoe Covergece Theorem 2 2 Series of Real

More information

Asymptotic distribution of products of sums of independent random variables

Asymptotic distribution of products of sums of independent random variables Proc. Idia Acad. Sci. Math. Sci. Vol. 3, No., May 03, pp. 83 9. c Idia Academy of Scieces Asymptotic distributio of products of sums of idepedet radom variables YANLING WANG, SUXIA YAO ad HONGXIA DU ollege

More information

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.436J/15.085J Fall 2008 Lecture 19 11/17/2008 LAWS OF LARGE NUMBERS II THE STRONG LAW OF LARGE NUMBERS

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.436J/15.085J Fall 2008 Lecture 19 11/17/2008 LAWS OF LARGE NUMBERS II THE STRONG LAW OF LARGE NUMBERS MASSACHUSTTS INSTITUT OF TCHNOLOGY 6.436J/5.085J Fall 2008 Lecture 9 /7/2008 LAWS OF LARG NUMBRS II Cotets. The strog law of large umbers 2. The Cheroff boud TH STRONG LAW OF LARG NUMBRS While the weak

More information

Harmonic Number Identities Via Euler s Transform

Harmonic Number Identities Via Euler s Transform 1 2 3 47 6 23 11 Joural of Iteger Sequeces, Vol. 12 2009), Article 09.6.1 Harmoic Number Idetities Via Euler s Trasform Khristo N. Boyadzhiev Departmet of Mathematics Ohio Norther Uiversity Ada, Ohio 45810

More information

2.2. Central limit theorem.

2.2. Central limit theorem. 36.. Cetral limit theorem. The most ideal case of the CLT is that the radom variables are iid with fiite variace. Although it is a special case of the more geeral Lideberg-Feller CLT, it is most stadard

More information

ECONOMETRIC THEORY. MODULE XIII Lecture - 34 Asymptotic Theory and Stochastic Regressors

ECONOMETRIC THEORY. MODULE XIII Lecture - 34 Asymptotic Theory and Stochastic Regressors ECONOMETRIC THEORY MODULE XIII Lecture - 34 Asymptotic Theory ad Stochastic Regressors Dr. Shalabh Departmet of Mathematics ad Statistics Idia Istitute of Techology Kapur Asymptotic theory The asymptotic

More information

Lecture 6 Chi Square Distribution (χ 2 ) and Least Squares Fitting

Lecture 6 Chi Square Distribution (χ 2 ) and Least Squares Fitting Lecture 6 Chi Square Distributio (χ ) ad Least Squares Fittig Chi Square Distributio (χ ) Suppose: We have a set of measuremets {x 1, x, x }. We kow the true value of each x i (x t1, x t, x t ). We would

More information

ESTIMATION OF THE REMAINDER TERM IN THE THEOREM FOR THE SUMS OF THE TYPE f(2 k t)

ESTIMATION OF THE REMAINDER TERM IN THE THEOREM FOR THE SUMS OF THE TYPE f(2 k t) ialiai Math Semi 8 6 23 4349 ESTIMATION OF THE REMAINDER TERM IN THE THEOREM FOR THE SUMS OF THE TYPE f2 k t Gitatas MISEVIƒIUS Birte KRYšIEN E Vilis Gedimias Techical Uiversity Saletekio av LT-233 Vilis

More information

Applied Mathematics Letters. On the properties of Lucas numbers with binomial coefficients

Applied Mathematics Letters. On the properties of Lucas numbers with binomial coefficients Applied Mathematics Letters 3 (1 68 7 Cotets lists available at ScieceDirect Applied Mathematics Letters joural homepage: wwwelseviercom/locate/aml O the properties of Lucas umbers with biomial coefficiets

More information

Some q-analogues of Fibonacci, Lucas and Chebyshev polynomials with nice moments

Some q-analogues of Fibonacci, Lucas and Chebyshev polynomials with nice moments Some -aaloges o Fiboacci, Lcas ad Chebyshev polyomials with ice momets Joha Cigler Faltät ür Mathemati, Uiversität Wie Ui Wie Rossa, Osar-Morgester-Platz, 090 Wie ohacigler@ivieacat http://homepageivieacat/ohacigler/

More information

Lecture 6 Chi Square Distribution (χ 2 ) and Least Squares Fitting

Lecture 6 Chi Square Distribution (χ 2 ) and Least Squares Fitting Lecture 6 Chi Square Distributio (χ ) ad Least Squares Fittig Chi Square Distributio (χ ) Suppose: We have a set of measuremets {x 1, x, x }. We kow the true value of each x i (x t1, x t, x t ). We would

More information

and the sum of its first n terms be denoted by. Convergence: An infinite series is said to be convergent if, a definite unique number., finite.

and the sum of its first n terms be denoted by. Convergence: An infinite series is said to be convergent if, a definite unique number., finite. INFINITE SERIES Seqece: If a set of real mbers a seqece deoted by * + * Or * + * occr accordig to some defiite rle, the it is called + if is fiite + if is ifiite Series: is called a series ad is deoted

More information

( a) ( ) 1 ( ) 2 ( ) ( ) 3 3 ( ) =!

( a) ( ) 1 ( ) 2 ( ) ( ) 3 3 ( ) =! .8,.9: Taylor ad Maclauri Series.8. Although we were able to fid power series represetatios for a limited group of fuctios i the previous sectio, it is ot immediately obvious whether ay give fuctio has

More information

Regression with an Evaporating Logarithmic Trend

Regression with an Evaporating Logarithmic Trend Regressio with a Evaporatig Logarithmic Tred Peter C. B. Phillips Cowles Foudatio, Yale Uiversity, Uiversity of Aucklad & Uiversity of York ad Yixiao Su Departmet of Ecoomics Yale Uiversity October 5,

More information

MAT2400 Assignment 2 - Solutions

MAT2400 Assignment 2 - Solutions MAT24 Assigmet 2 - Soltios Notatio: For ay fctio f of oe real variable, f(a + ) deotes the limit of f() whe teds to a from above (if it eists); i.e., f(a + ) = lim t a + f(t). Similarly, f(a ) deotes the

More information

Lecture 19: Convergence

Lecture 19: Convergence Lecture 19: Covergece Asymptotic approach I statistical aalysis or iferece, a key to the success of fidig a good procedure is beig able to fid some momets ad/or distributios of various statistics. I may

More information

Precise Rates in Complete Moment Convergence for Negatively Associated Sequences

Precise Rates in Complete Moment Convergence for Negatively Associated Sequences Commuicatios of the Korea Statistical Society 29, Vol. 16, No. 5, 841 849 Precise Rates i Complete Momet Covergece for Negatively Associated Sequeces Dae-Hee Ryu 1,a a Departmet of Computer Sciece, ChugWoo

More information

ECE 901 Lecture 12: Complexity Regularization and the Squared Loss

ECE 901 Lecture 12: Complexity Regularization and the Squared Loss ECE 90 Lecture : Complexity Regularizatio ad the Squared Loss R. Nowak 5/7/009 I the previous lectures we made use of the Cheroff/Hoeffdig bouds for our aalysis of classifier errors. Hoeffdig s iequality

More information

ACCESS TO SCIENCE, ENGINEERING AND AGRICULTURE: MATHEMATICS 1 MATH00030 SEMESTER / Statistics

ACCESS TO SCIENCE, ENGINEERING AND AGRICULTURE: MATHEMATICS 1 MATH00030 SEMESTER / Statistics ACCESS TO SCIENCE, ENGINEERING AND AGRICULTURE: MATHEMATICS 1 MATH00030 SEMESTER 1 018/019 DR. ANTHONY BROWN 8. Statistics 8.1. Measures of Cetre: Mea, Media ad Mode. If we have a series of umbers the

More information

Alternating Series. 1 n 0 2 n n THEOREM 9.14 Alternating Series Test Let a n > 0. The alternating series. 1 n a n.

Alternating Series. 1 n 0 2 n n THEOREM 9.14 Alternating Series Test Let a n > 0. The alternating series. 1 n a n. 0_0905.qxd //0 :7 PM Page SECTION 9.5 Alteratig Series Sectio 9.5 Alteratig Series Use the Alteratig Series Test to determie whether a ifiite series coverges. Use the Alteratig Series Remaider to approximate

More information

Random Matrices with Blocks of Intermediate Scale Strongly Correlated Band Matrices

Random Matrices with Blocks of Intermediate Scale Strongly Correlated Band Matrices Radom Matrices with Blocks of Itermediate Scale Strogly Correlated Bad Matrices Jiayi Tog Advisor: Dr. Todd Kemp May 30, 07 Departmet of Mathematics Uiversity of Califoria, Sa Diego Cotets Itroductio Notatio

More information

In this document, if A:

In this document, if A: m I this docmet, if A: is a m matrix, ref(a) is a row-eqivalet matrix i row-echelo form sig Gassia elimiatio with partial pivotig as described i class. Ier prodct ad orthogoality What is the largest possible

More information

x a x a Lecture 2 Series (See Chapter 1 in Boas)

x a x a Lecture 2 Series (See Chapter 1 in Boas) Lecture Series (See Chapter i Boas) A basic ad very powerful (if pedestria, recall we are lazy AD smart) way to solve ay differetial (or itegral) equatio is via a series expasio of the correspodig solutio

More information

CHAPTER 10 INFINITE SEQUENCES AND SERIES

CHAPTER 10 INFINITE SEQUENCES AND SERIES CHAPTER 10 INFINITE SEQUENCES AND SERIES 10.1 Sequeces 10.2 Ifiite Series 10.3 The Itegral Tests 10.4 Compariso Tests 10.5 The Ratio ad Root Tests 10.6 Alteratig Series: Absolute ad Coditioal Covergece

More information

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/15.070J Fall 2013 Lecture 21 11/27/2013

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/15.070J Fall 2013 Lecture 21 11/27/2013 MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/15.070J Fall 2013 Lecture 21 11/27/2013 Fuctioal Law of Large Numbers. Costructio of the Wieer Measure Cotet. 1. Additioal techical results o weak covergece

More information

Topic 9: Sampling Distributions of Estimators

Topic 9: Sampling Distributions of Estimators Topic 9: Samplig Distributios of Estimators Course 003, 2016 Page 0 Samplig distributios of estimators Sice our estimators are statistics (particular fuctios of radom variables), their distributio ca be

More information

September 2012 C1 Note. C1 Notes (Edexcel) Copyright - For AS, A2 notes and IGCSE / GCSE worksheets 1

September 2012 C1 Note. C1 Notes (Edexcel) Copyright   - For AS, A2 notes and IGCSE / GCSE worksheets 1 September 0 s (Edecel) Copyright www.pgmaths.co.uk - For AS, A otes ad IGCSE / GCSE worksheets September 0 Copyright www.pgmaths.co.uk - For AS, A otes ad IGCSE / GCSE worksheets September 0 Copyright

More information

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/15.070J Fall 2013 Lecture 2 9/9/2013. Large Deviations for i.i.d. Random Variables

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/15.070J Fall 2013 Lecture 2 9/9/2013. Large Deviations for i.i.d. Random Variables MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/15.070J Fall 2013 Lecture 2 9/9/2013 Large Deviatios for i.i.d. Radom Variables Cotet. Cheroff boud usig expoetial momet geeratig fuctios. Properties of a momet

More information

An Introduction to Randomized Algorithms

An Introduction to Randomized Algorithms A Itroductio to Radomized Algorithms The focus of this lecture is to study a radomized algorithm for quick sort, aalyze it usig probabilistic recurrece relatios, ad also provide more geeral tools for aalysis

More information

Comparison Study of Series Approximation. and Convergence between Chebyshev. and Legendre Series

Comparison Study of Series Approximation. and Convergence between Chebyshev. and Legendre Series Applied Mathematical Scieces, Vol. 7, 03, o. 6, 3-337 HIKARI Ltd, www.m-hikari.com http://d.doi.org/0.988/ams.03.3430 Compariso Study of Series Approimatio ad Covergece betwee Chebyshev ad Legedre Series

More information

Central limit theorem and almost sure central limit theorem for the product of some partial sums

Central limit theorem and almost sure central limit theorem for the product of some partial sums Proc. Idia Acad. Sci. Math. Sci. Vol. 8, No. 2, May 2008, pp. 289 294. Prited i Idia Cetral it theorem ad almost sure cetral it theorem for the product of some partial sums YU MIAO College of Mathematics

More information

PH 425 Quantum Measurement and Spin Winter SPINS Lab 1

PH 425 Quantum Measurement and Spin Winter SPINS Lab 1 PH 425 Quatum Measuremet ad Spi Witer 23 SPIS Lab Measure the spi projectio S z alog the z-axis This is the experimet that is ready to go whe you start the program, as show below Each atom is measured

More information

Problem Set 2 Solutions

Problem Set 2 Solutions CS271 Radomess & Computatio, Sprig 2018 Problem Set 2 Solutios Poit totals are i the margi; the maximum total umber of poits was 52. 1. Probabilistic method for domiatig sets 6pts Pick a radom subset S

More information

0, otherwise. EX = E(X 1 + X n ) = EX j = np and. Var(X j ) = np(1 p). Var(X) = Var(X X n ) =

0, otherwise. EX = E(X 1 + X n ) = EX j = np and. Var(X j ) = np(1 p). Var(X) = Var(X X n ) = PROBABILITY MODELS 35 10. Discrete probability distributios I this sectio, we discuss several well-ow discrete probability distributios ad study some of their properties. Some of these distributios, lie

More information

Randomized Algorithms I, Spring 2018, Department of Computer Science, University of Helsinki Homework 1: Solutions (Discussed January 25, 2018)

Randomized Algorithms I, Spring 2018, Department of Computer Science, University of Helsinki Homework 1: Solutions (Discussed January 25, 2018) Radomized Algorithms I, Sprig 08, Departmet of Computer Sciece, Uiversity of Helsiki Homework : Solutios Discussed Jauary 5, 08). Exercise.: Cosider the followig balls-ad-bi game. We start with oe black

More information

The Interval of Convergence for a Power Series Examples

The Interval of Convergence for a Power Series Examples The Iterval of Covergece for a Power Series Examples To review the process: How to Test a Power Series for Covergece. Fid the iterval where the series coverges absolutely. We have to use the Ratio or Root

More information

(A sequence also can be thought of as the list of function values attained for a function f :ℵ X, where f (n) = x n for n 1.) x 1 x N +k x N +4 x 3

(A sequence also can be thought of as the list of function values attained for a function f :ℵ X, where f (n) = x n for n 1.) x 1 x N +k x N +4 x 3 MATH 337 Sequeces Dr. Neal, WKU Let X be a metric space with distace fuctio d. We shall defie the geeral cocept of sequece ad limit i a metric space, the apply the results i particular to some special

More information

LINEARIZATION OF NONLINEAR EQUATIONS By Dominick Andrisani. dg x. ( ) ( ) dx

LINEARIZATION OF NONLINEAR EQUATIONS By Dominick Andrisani. dg x. ( ) ( ) dx LINEAIZATION OF NONLINEA EQUATIONS By Domiick Adrisai A. Liearizatio of Noliear Fctios A. Scalar fctios of oe variable. We are ive the oliear fctio (). We assme that () ca be represeted si a Taylor series

More information

Journal of Multivariate Analysis. Superefficient estimation of the marginals by exploiting knowledge on the copula

Journal of Multivariate Analysis. Superefficient estimation of the marginals by exploiting knowledge on the copula Joural of Multivariate Aalysis 102 (2011) 1315 1319 Cotets lists available at ScieceDirect Joural of Multivariate Aalysis joural homepage: www.elsevier.com/locate/jmva Superefficiet estimatio of the margials

More information

The Height of q-binary Search Trees

The Height of q-binary Search Trees Author mauscript, published i "Discrete Mathematics ad Theoretical Computer Sciece 5, 2002 97-08" Discrete Mathematics ad Theoretical Computer Sciece 5, 2002, 97 08 The Height of q-biary Search Trees Michael

More information

TR/46 OCTOBER THE ZEROS OF PARTIAL SUMS OF A MACLAURIN EXPANSION A. TALBOT

TR/46 OCTOBER THE ZEROS OF PARTIAL SUMS OF A MACLAURIN EXPANSION A. TALBOT TR/46 OCTOBER 974 THE ZEROS OF PARTIAL SUMS OF A MACLAURIN EXPANSION by A. TALBOT .. Itroductio. A problem i approximatio theory o which I have recetly worked [] required for its solutio a proof that the

More information

Stochastic Matrices in a Finite Field

Stochastic Matrices in a Finite Field Stochastic Matrices i a Fiite Field Abstract: I this project we will explore the properties of stochastic matrices i both the real ad the fiite fields. We first explore what properties 2 2 stochastic matrices

More information

Lecture 2: Concentration Bounds

Lecture 2: Concentration Bounds CSE 52: Desig ad Aalysis of Algorithms I Sprig 206 Lecture 2: Cocetratio Bouds Lecturer: Shaya Oveis Ghara March 30th Scribe: Syuzaa Sargsya Disclaimer: These otes have ot bee subjected to the usual scrutiy

More information

18.01 Calculus Jason Starr Fall 2005

18.01 Calculus Jason Starr Fall 2005 Lecture 18. October 5, 005 Homework. Problem Set 5 Part I: (c). Practice Problems. Course Reader: 3G 1, 3G, 3G 4, 3G 5. 1. Approximatig Riema itegrals. Ofte, there is o simpler expressio for the atiderivative

More information

NUMBER OF SPANNING TREES OF NEW JOIN GRAPHS

NUMBER OF SPANNING TREES OF NEW JOIN GRAPHS Available olie at http://scik.org Algebra Letters, 03, 03:4 ISSN 0-0 NUMBER OF SPANNING REES OF NEW JOIN GRAPHS S. N. DAOUD, Departmet of Applied Mathematics, Faclty of Applied Sciece, aibah Uiversity,

More information

ECE 330:541, Stochastic Signals and Systems Lecture Notes on Limit Theorems from Probability Fall 2002

ECE 330:541, Stochastic Signals and Systems Lecture Notes on Limit Theorems from Probability Fall 2002 ECE 330:541, Stochastic Sigals ad Systems Lecture Notes o Limit Theorems from robability Fall 00 I practice, there are two ways we ca costruct a ew sequece of radom variables from a old sequece of radom

More information

LECTURE 13 SPURIOUS REGRESSION, TESTING FOR UNIT ROOT = C (1) C (1) 0! ! uv! 2 v. t=1 X2 t

LECTURE 13 SPURIOUS REGRESSION, TESTING FOR UNIT ROOT = C (1) C (1) 0! ! uv! 2 v. t=1 X2 t APRIL 9, 7 Sprios regressio LECTURE 3 SPURIOUS REGRESSION, TESTING FOR UNIT ROOT I this sectio, we cosider the sitatio whe is oe it root process, say Y t is regressed agaist aother it root process, say

More information

Singular Continuous Measures by Michael Pejic 5/14/10

Singular Continuous Measures by Michael Pejic 5/14/10 Sigular Cotiuous Measures by Michael Peic 5/4/0 Prelimiaries Give a set X, a σ-algebra o X is a collectio of subsets of X that cotais X ad ad is closed uder complemetatio ad coutable uios hece, coutable

More information

Topic 9: Sampling Distributions of Estimators

Topic 9: Sampling Distributions of Estimators Topic 9: Samplig Distributios of Estimators Course 003, 2018 Page 0 Samplig distributios of estimators Sice our estimators are statistics (particular fuctios of radom variables), their distributio ca be

More information

A Note on the form of Jacobi Polynomial used in Harish-Chandra s Paper Motion of an Electron in the Field of a Magnetic Pole.

A Note on the form of Jacobi Polynomial used in Harish-Chandra s Paper Motion of an Electron in the Field of a Magnetic Pole. e -Joral of Sciece & Techology (e-jst) A Note o the form of Jacobi Polyomial se i Harish-Chara s Paper Motio of a Electro i the Fiel of a Magetic Pole Vio Kmar Yaav Jior Research Fellow (CSIR) Departmet

More information

Access to the published version may require journal subscription. Published with permission from: Elsevier.

Access to the published version may require journal subscription. Published with permission from: Elsevier. This is a author produced versio of a paper published i Statistics ad Probability Letters. This paper has bee peer-reviewed, it does ot iclude the joural pagiatio. Citatio for the published paper: Forkma,

More information

Ma 530 Infinite Series I

Ma 530 Infinite Series I Ma 50 Ifiite Series I Please ote that i additio to the material below this lecture icorporated material from the Visual Calculus web site. The material o sequeces is at Visual Sequeces. (To use this li

More information

Sorting Algorithms. Algorithms Kyuseok Shim SoEECS, SNU.

Sorting Algorithms. Algorithms Kyuseok Shim SoEECS, SNU. Sortig Algorithms Algorithms Kyuseo Shim SoEECS, SNU. Desigig Algorithms Icremetal approaches Divide-ad-Coquer approaches Dyamic programmig approaches Greedy approaches Radomized approaches You are ot

More information

A Mean Paradox. John Konvalina, Jack Heidel, and Jim Rogers. Department of Mathematics University of Nebraska at Omaha Omaha, NE USA

A Mean Paradox. John Konvalina, Jack Heidel, and Jim Rogers. Department of Mathematics University of Nebraska at Omaha Omaha, NE USA A Mea Paradox by Joh Kovalia, Jac Heidel, ad Jim Rogers Departmet of Mathematics Uiversity of Nebrasa at Omaha Omaha, NE 6882-243 USA Phoe: (42) 554-2836 Fax: (42) 554-2975 E-mail: joho@uomaha.edu A Mea

More information

Chapter 5. Inequalities. 5.1 The Markov and Chebyshev inequalities

Chapter 5. Inequalities. 5.1 The Markov and Chebyshev inequalities Chapter 5 Iequalities 5.1 The Markov ad Chebyshev iequalities As you have probably see o today s frot page: every perso i the upper teth percetile ears at least 1 times more tha the average salary. I other

More information

Extremal Weighted Path Lengths in Random Binary Search Trees

Extremal Weighted Path Lengths in Random Binary Search Trees Extremal Weighted Path Legths i Radom Biary Search Trees Rafik Aguech 1 Nabil Lasmar 2 Hosam Mahmoud 3 December 18, 2008 Abstract. We cosider weighted path legths to the extremal leaves i a radom biary

More information

Math 155 (Lecture 3)

Math 155 (Lecture 3) Math 55 (Lecture 3) September 8, I this lecture, we ll cosider the aswer to oe of the most basic coutig problems i combiatorics Questio How may ways are there to choose a -elemet subset of the set {,,,

More information

Math 2784 (or 2794W) University of Connecticut

Math 2784 (or 2794W) University of Connecticut ORDERS OF GROWTH PAT SMITH Math 2784 (or 2794W) Uiversity of Coecticut Date: Mar. 2, 22. ORDERS OF GROWTH. Itroductio Gaiig a ituitive feel for the relative growth of fuctios is importat if you really

More information

3. Z Transform. Recall that the Fourier transform (FT) of a DT signal xn [ ] is ( ) [ ] = In order for the FT to exist in the finite magnitude sense,

3. Z Transform. Recall that the Fourier transform (FT) of a DT signal xn [ ] is ( ) [ ] = In order for the FT to exist in the finite magnitude sense, 3. Z Trasform Referece: Etire Chapter 3 of text. Recall that the Fourier trasform (FT) of a DT sigal x [ ] is ω ( ) [ ] X e = j jω k = xe I order for the FT to exist i the fiite magitude sese, S = x [

More information

Limit Theorems. Convergence in Probability. Let X be the number of heads observed in n tosses. Then, E[X] = np and Var[X] = np(1-p).

Limit Theorems. Convergence in Probability. Let X be the number of heads observed in n tosses. Then, E[X] = np and Var[X] = np(1-p). Limit Theorems Covergece i Probability Let X be the umber of heads observed i tosses. The, E[X] = p ad Var[X] = p(-p). L O This P x p NM QP P x p should be close to uity for large if our ituitio is correct.

More information

BINOMIAL COEFFICIENT AND THE GAUSSIAN

BINOMIAL COEFFICIENT AND THE GAUSSIAN BINOMIAL COEFFICIENT AND THE GAUSSIAN The biomial coefficiet is defied as-! k!(! ad ca be writte out i the form of a Pascal Triagle startig at the zeroth row with elemet 0,0) ad followed by the two umbers,

More information

Math 525: Lecture 5. January 18, 2018

Math 525: Lecture 5. January 18, 2018 Math 525: Lecture 5 Jauary 18, 2018 1 Series (review) Defiitio 1.1. A sequece (a ) R coverges to a poit L R (writte a L or lim a = L) if for each ǫ > 0, we ca fid N such that a L < ǫ for all N. If the

More information

Lecture 7: Properties of Random Samples

Lecture 7: Properties of Random Samples Lecture 7: Properties of Radom Samples 1 Cotiued From Last Class Theorem 1.1. Let X 1, X,...X be a radom sample from a populatio with mea µ ad variace σ

More information

A TYPE OF PRIMITIVE ALGEBRA*

A TYPE OF PRIMITIVE ALGEBRA* A TYPE OF PRIMITIVE ALGEBRA* BT J. H. M. WEDDERBURN I a recet paper,t L. E. Dickso has discussed the liear associative algebra, A, defied by the relatios xy = yo(x), y = g, where 8 ( x ) is a polyomial

More information

Homework Set #3 - Solutions

Homework Set #3 - Solutions EE 15 - Applicatios of Covex Optimizatio i Sigal Processig ad Commuicatios Dr. Adre Tkaceko JPL Third Term 11-1 Homework Set #3 - Solutios 1. a) Note that x is closer to x tha to x l i the Euclidea orm

More information

Goodness-of-Fit Tests and Categorical Data Analysis (Devore Chapter Fourteen)

Goodness-of-Fit Tests and Categorical Data Analysis (Devore Chapter Fourteen) Goodess-of-Fit Tests ad Categorical Data Aalysis (Devore Chapter Fourtee) MATH-252-01: Probability ad Statistics II Sprig 2019 Cotets 1 Chi-Squared Tests with Kow Probabilities 1 1.1 Chi-Squared Testig................

More information

Distribution of Random Samples & Limit theorems

Distribution of Random Samples & Limit theorems STAT/MATH 395 A - PROBABILITY II UW Witer Quarter 2017 Néhémy Lim Distributio of Radom Samples & Limit theorems 1 Distributio of i.i.d. Samples Motivatig example. Assume that the goal of a study is to

More information

SEQUENCES AND SERIES

SEQUENCES AND SERIES Sequeces ad 6 Sequeces Ad SEQUENCES AND SERIES Successio of umbers of which oe umber is desigated as the first, other as the secod, aother as the third ad so o gives rise to what is called a sequece. Sequeces

More information

Axioms of Measure Theory

Axioms of Measure Theory MATH 532 Axioms of Measure Theory Dr. Neal, WKU I. The Space Throughout the course, we shall let X deote a geeric o-empty set. I geeral, we shall ot assume that ay algebraic structure exists o X so that

More information

A convergence result for the Kuramoto model with all-to-all coupling

A convergence result for the Kuramoto model with all-to-all coupling A covergece reslt for the Kramoto model with all-to-all coplig Mark Verwoerd ad Oliver Maso The Hamilto Istitte, Natioal Uiversity of Irelad, Mayooth Abstract We prove a covergece reslt for the stadard

More information

1 of 7 7/16/2009 6:06 AM Virtual Laboratories > 6. Radom Samples > 1 2 3 4 5 6 7 6. Order Statistics Defiitios Suppose agai that we have a basic radom experimet, ad that X is a real-valued radom variable

More information

7.1 Convergence of sequences of random variables

7.1 Convergence of sequences of random variables Chapter 7 Limit Theorems Throughout this sectio we will assume a probability space (, F, P), i which is defied a ifiite sequece of radom variables (X ) ad a radom variable X. The fact that for every ifiite

More information

This exam contains 19 pages (including this cover page) and 10 questions. A Formulae sheet is provided with the exam.

This exam contains 19 pages (including this cover page) and 10 questions. A Formulae sheet is provided with the exam. Probability ad Statistics FS 07 Secod Sessio Exam 09.0.08 Time Limit: 80 Miutes Name: Studet ID: This exam cotais 9 pages (icludig this cover page) ad 0 questios. A Formulae sheet is provided with the

More information

Integer Linear Programming

Integer Linear Programming Iteger Liear Programmig Itroductio Iteger L P problem (P) Mi = s. t. a = b i =,, m = i i 0, iteger =,, c Eemple Mi z = 5 s. t. + 0 0, 0, iteger F(P) = feasible domai of P Itroductio Iteger L P problem

More information

Axis Aligned Ellipsoid

Axis Aligned Ellipsoid Machie Learig for Data Sciece CS 4786) Lecture 6,7 & 8: Ellipsoidal Clusterig, Gaussia Mixture Models ad Geeral Mixture Models The text i black outlies high level ideas. The text i blue provides simple

More information

Legendre-Stirling Permutations

Legendre-Stirling Permutations Legedre-Stirlig Permutatios Eric S. Egge Departmet of Mathematics Carleto College Northfield, MN 07 USA eegge@carleto.edu Abstract We first give a combiatorial iterpretatio of Everitt, Littlejoh, ad Wellma

More information

Math 216A Notes, Week 5

Math 216A Notes, Week 5 Math 6A Notes, Week 5 Scribe: Ayastassia Sebolt Disclaimer: These otes are ot early as polished (ad quite possibly ot early as correct) as a published paper. Please use them at your ow risk.. Thresholds

More information

Partial match queries: a limit process

Partial match queries: a limit process Partial match queries: a limit process Nicolas Brouti Ralph Neiiger Heig Sulzbach Partial match queries: a limit process 1 / 17 Searchig geometric data ad quadtrees 1 Partial match queries: a limit process

More information

Linear regression. Daniel Hsu (COMS 4771) (y i x T i β)2 2πσ. 2 2σ 2. 1 n. (x T i β y i ) 2. 1 ˆβ arg min. β R n d

Linear regression. Daniel Hsu (COMS 4771) (y i x T i β)2 2πσ. 2 2σ 2. 1 n. (x T i β y i ) 2. 1 ˆβ arg min. β R n d Liear regressio Daiel Hsu (COMS 477) Maximum likelihood estimatio Oe of the simplest liear regressio models is the followig: (X, Y ),..., (X, Y ), (X, Y ) are iid radom pairs takig values i R d R, ad Y

More information

EXISTENCE OF CONCENTRATED WAVES FOR THE DIFFERENT TYPE OF NONLINEARITY. V. Eremenko and N. Manaenkova

EXISTENCE OF CONCENTRATED WAVES FOR THE DIFFERENT TYPE OF NONLINEARITY. V. Eremenko and N. Manaenkova EXISTENCE OF CONCENTRATED WAVES FOR THE DIFFERENT TYPE OF NONLINEARITY. V. Eremeko ad N. Maaekova Istitte of Terrestrial Magetism, Ioosphere ad Radio Wave Propagatio Rssia Academy of Sciece E-mail: at_ma@mail.r

More information

The Riemann Zeta Function

The Riemann Zeta Function Physics 6A Witer 6 The Riema Zeta Fuctio I this ote, I will sketch some of the mai properties of the Riema zeta fuctio, ζ(x). For x >, we defie ζ(x) =, x >. () x = For x, this sum diverges. However, we

More information

Optimally Sparse SVMs

Optimally Sparse SVMs A. Proof of Lemma 3. We here prove a lower boud o the umber of support vectors to achieve geeralizatio bouds of the form which we cosider. Importatly, this result holds ot oly for liear classifiers, but

More information

Hyun-Chull Kim and Tae-Sung Kim

Hyun-Chull Kim and Tae-Sung Kim Commu. Korea Math. Soc. 20 2005), No. 3, pp. 531 538 A CENTRAL LIMIT THEOREM FOR GENERAL WEIGHTED SUM OF LNQD RANDOM VARIABLES AND ITS APPLICATION Hyu-Chull Kim ad Tae-Sug Kim Abstract. I this paper we

More information

Pellian sequence relationships among π, e, 2

Pellian sequence relationships among π, e, 2 otes o umber Theory ad Discrete Mathematics Vol. 8, 0, o., 58 6 Pellia sequece relatioships amog π, e, J. V. Leyedekkers ad A. G. Shao Faculty of Sciece, The Uiversity of Sydey Sydey, SW 006, Australia

More information

LECTURE 11 LINEAR PROCESSES III: ASYMPTOTIC RESULTS

LECTURE 11 LINEAR PROCESSES III: ASYMPTOTIC RESULTS PRIL 7, 9 where LECTURE LINER PROCESSES III: SYMPTOTIC RESULTS (Phillips ad Solo (99) ad Phillips Lecture Notes o Statioary ad Nostatioary Time Series) I this lecture, we discuss the LLN ad CLT for a liear

More information

CEE 522 Autumn Uncertainty Concepts for Geotechnical Engineering

CEE 522 Autumn Uncertainty Concepts for Geotechnical Engineering CEE 5 Autum 005 Ucertaity Cocepts for Geotechical Egieerig Basic Termiology Set A set is a collectio of (mutually exclusive) objects or evets. The sample space is the (collectively exhaustive) collectio

More information

Subject: Differential Equations & Mathematical Modeling -III. Lesson: Power series solutions of Differential Equations. about ordinary points

Subject: Differential Equations & Mathematical Modeling -III. Lesson: Power series solutions of Differential Equations. about ordinary points Power series solutio of Differetial equatios about ordiary poits Subject: Differetial Equatios & Mathematical Modelig -III Lesso: Power series solutios of Differetial Equatios about ordiary poits Lesso

More information