A MULTIDIMENSIONAL ANALOGUE OF THE RADEMACHER-GAUSSIAN TAIL COMPARISON

Size: px
Start display at page:

Download "A MULTIDIMENSIONAL ANALOGUE OF THE RADEMACHER-GAUSSIAN TAIL COMPARISON"

Transcription

1 A MULTIDIMENSIONAL ANALOGUE OF THE RADEMACHER-GAUSSIAN TAIL COMPARISON PIOTR NAYAR AND TOMASZ TKOCZ Abstract We prove a menson-free tal comparson between the Euclean norms of sums of nepenent ranom vectors unformly strbute n centre Euclean spheres an properly rescale stanar Gaussan ranom vectors 00 Mathematcs Subject Classfcaton Prmary 60E5; Seconary 60G5, 60G50 Key wors probablty nequaltes, tal comparson, bouns for tal probabltes, Gaussan ranom vectors, unform strbutons n Euclean spheres Introucton Tal comparson bouns, such as Hoeffng s nequalty, have always playe a crucal role n probablty theory When specfe to concrete examples, very precse estmates for tal probabltes are usually known For nstance, f ɛ, ɛ, are nepenent ranom varables each takng values ± wth probablty an g, g, are nepenent stanar Gaussan ranom varables, then for every m, real numbers a,, a m an postve t, P P a ɛ + + a m ɛ m > t c P a g + + a m g m > t for some absolute constant c Ths nequalty was frst prove by Pnels n [5] wth c 446 Talagran n [6] treate the case of nepenent but not necessarly entcally strbute boune ranom varables by means of the Laplace transform establshng smlar Gaussan tal bouns Bobkov, Götze an Houré obtane a bgger constant c 0 n P, but ther nuctve argument was much smpler see [] Only very recently the best constant equal approxmately to 38 has been foun see [] Oleszkewcz conjecture the followng multmensonal generalsaton of Pnels Raemacher-Gaussan tal comparson P: fx, let ξ, ξ, be nepenent ranom vectors unformly strbute n the Euclean unt sphere S R an let G, G, be nepenent stanar Gaussan ranom vectors n R wth mean zero an entty covarance matrx; there exsts a unversal constant C such that PN supporte n part by NCN grant DEC-0/05/B/ST/004

2 for every m, real numbers a,, a m an t > 0 we have m m G KO P a ξ > t C P a > t Here an throughout, enotes the stanar Euclean norm n R Note that the normalsaton s chosen so that the vectors ξ an G / have the same covarance matrx Planly, when =, KO reuces to P For general, t s possble to euce KO wth C = O from Theorem n [4] The goal of ths note s to postvely resolve Oleszkewcz s conjecture We shall show the followng two theorems whch are our man results The latter wll easly follow from the former Theorem For each, let C be the best constant n nequalty KO Then, C e < 739 for an lm sup C Theorem Let X, X, be nepenent rotatonally nvarant ranom vectors havng values n the unt Euclean ball n R Let G, G, be nepenent stanar Gaussan ranom vectors n R wth mean zero an entty covarance matrx Then for every m, real numbers a,, a m an t > 0 we have m m P a X > t e G P a > t, where stans for the stanar Euclean norm n R Remark Ths wll no longer hol f we only assume the bouneness of the X For example, conser nepenent X takng only two values ±, 0,, 0 each wth probablty Then for, say a = = a m = / m, t =, the rght-han se of goes to zero when goes to nfnty, whereas the left-han se oes not epen on Acknowlegements The authors woul lke to thank Krzysztof Oleszkewcz for ntroucng them nto the subject They are really grateful to Rafa l Lata la for a scusson concernng Theorem as well as to Iosf Pnels for sharng hs smple proof of Lemma whch le to a sgnfcant mprovement of our numercal constants Proofs Our proof of Theorem s nuctve, nspre by the nuctve approach to the one mensonal case from [] In the nuctve step, usng the sphercal

3 symmetry of our problem, we arrve at an nequalty comparng the Gaussan volume between centre an shfte balls Lemma 3 below Ths nequalty can be vewe as a multmensonal generalsaton of the two pont-nequalty erve n the nuctve step n [] Its proof leas us to somewhat subtle estmates for the Laplace transform of the frst coornate of ξ Lemma below We shall nee four lemmas We start wth a result whch wll be use to prove numercal values of our constants see also Lemma n [3] Lemma Let an G be a stanar Gaussan ranom vector n R Then P G > + /e Proof by I Pnels Set p = P G > + By nequalty 6 n [5] P G > + P g > +, where g s a stanar Gaussan ranom varable wth mean zero an varance one Snce the rght-han se s ncreasng n, we have for 4 p P g > 6 > /e Moreover, we rectly check that p = /e < p 3 Thus, p /e for The next lemma gves tght estmates for the Laplace transform of the frst coornate of a ranom vector unformly strbute n the unt sphere We hope these estmates are of nepenent nterest, n aton to playng a major role n our proof Lemma For an b 0 let us enote Then for every we have J = J b = x / e bx x a b J + = + J + + J 3, b J 3 J + + b, 4 + c J J b, J + J 3 J + + Proof a Integratng by parts twce we get b J + = x + e bx x, whch, after computng the secon ervatve n the above expresson, easly leas to the esre relaton 3

4 For the proof of b an c let us frst observe that ue to a these two assertons hol true for b = 0 We then show that for b > 0, b an c are equvalent Inee, part a yels J 3 J = Thus, b s equvalent to b J + + J + b J +, + J b = + After cancellng common factors on both ses ths becomes c + b + + b 4 + Let us fx b > 0 We shall show b by backwars nucton on We can use for +, that s the equalty to rewrte c n the form b J J + + whch becomes 3 J = + J b J + J +3 J +3 J +, b = b + + b + + b 4 + Frst notce that b an equvalently 3 hol for all 0 b for some large enough 0 b whch epens only on b To see ths observe that the left-han se of 3 s strctly greater than, whereas the rght-han se for large s of orer 3/ + o/ Now suppose b hols for + 4, that s J J b an we want to show b nucton step By the above an the fact that 3 an b are equvalent, t s enough to show that b b +, Ths follows from an the estmate 4 + b Clearly mmeately follows from b an c 4 + b + 4

5 Remark Part mproves on Höler s nequalty whch gves J J +J 3 Remark Let us efne for an b 0 the normalse ntegrals J b = J b/j 0 so that they are the Laplace transforms of the probablty enstes: f an ξ s a ranom vector n R unformly strbute n the Euclean unt sphere S, we check that by rotatonal nvarance J 3 b = Ee v,ξ, for any vector v R of length b Part a for b = 0 gves J 3 0/J 0 = / Ths allows to smplfy b,c, rewrtten n terms of J to get for b a J + + = J + J 3, + + b, 4 + b J 3 J b +, c J J + J + J 3 J The followng lemma les at the heart of our nuctve argument It compares the stanar Gaussan measure of centre an shfte Euclean balls Lemma 3 Let an G be a stanar Gaussan ranom vector n R For every a 0, R + an a vector x R of length a we have P G R P G x R + a Proof Snce for a = 0 we have equalty, t s enough to show that the rght-han se, ha, R = P G a e R + a s nonecreasng wth respect to a by rotatonal nvarance, for concreteness we can choose x = ae, where e =, 0,, 0 Usng Fubn s theorem we can wrte ha, R = S R +a { a + R +a r } r e r / φtt r, π 0 a R +a r 5

6 where φt = π e t / The ervatve wth respect to a equals a ha, R = S R +a { r e r / π 0 [φ a R + a r ar + R + a r φ a + R + a r ar R + a r ]} r After changng the varables r = R + a x we see that ths s nonnegatve f an only f 0 x 3 [e xa R +a x + ar + a e xa R +a x ar ] x 0 + a Ths conton can be further smplfe by ntegraton by parts usng x = x x 3 We obtan an equvalent nequalty 0 x 3 Let b = a R + a Then Observe that 0 + a x cosh ar + a x x 0 x coshbxx = + a = b R x e bx x = J b Thus, the nequalty we want to show becomes J 3 b J b b R For a fxe b, the rght-han se as a functon of R s clearly ecreasng, so gven our assumpton R + t s enough to conser R = +, whch follows from Lemma b Remark The statement for = remans true an was prove n [], where t playe a key role n the nuctve proof of Pnels nequalty P The last lemma wll help us use the sphercal symmetry of our problem 6

7 Lemma 4 Let X be a rotatonally nvarant ranom vector n R Let x R an t > 0 be such that t > x Then P X + x > t = P X > θ x + t + θ x x, where θ s the frst coornate of an nepenent of X ranom vector unformly strbute n the unt sphere S n R Proof Let ξ be an nepenent of X ranom vector unformly strbute n the unt sphere S n R By rotatonal nvarance X has the same strbuton as Rξ, where R = X We have P X + x > t = P R + R ξ, x + x > t an by the rotatonal nvarance of ξ, ξ, x has the same strbuton as θ x wth θ beng the frst coornate of ξ The nequalty R +Rθ x + x > t s equvalent to R > θ x + t + θ x x or R < θ x t + θ x x, but the secon case oes not hol as the rght-han se s negatve, for we assume that t > x Proof of Theorem For we efne M = /P G > + We fx an show by nucton on m that nequalty KO hols wth C = M Then C M, by Lemma, M e an by the central lmt theorem, lm sup M = For m = we have to check that for 0 < t < a we have G M P a > t Ths follows because P G > P G > + Suppose the asserton s true for m We shall show t for m+ We can assume that the a are nonzero By homogenety we can also assume that m+ = a = If t a + + we trvally boun the rght-han se as follows: m+ G a P a > t = P + G > t P G > + = /M Now suppose t > a + + Notce that n partcular t > a Conser v = m+ = a ξ By nepenence an rotatonal nvarance, m+ P a ξ > t = P a e + v > t 7

8 Lemma 4 apple to X = v an x = a e yels m+ P a ξ > t = E θ P v v > θ a + t + θ a a As a consequence, by the nepenence of θ an v, an the nuctve hypothess, m+ P a ξ > t M E θ P G m+ = m+ = The vector m+ = a G has the same strbuton as applyng agan Lemma 4 yels G a > θ a + t + θ a a m+ P a ξ > t M P G + a e > t To fnsh the nuctve step t suffces to show that m+ G P G + a e > t P a > t m+ = a G = G Therefore, a = P + G > t Ths follows from Lemma 3 apple to a = a an R = t a + > +, whch completes the proof Proof of Theorem Let ξ, ξ, be nepenent ranom vectors unformly strbute n the unt Euclean sphere S R, nepenent of the sequence X, X, Snce X s rotatonal nvarant, t has the same strbuton as R ξ, where R = X Note that almost surely 0 R Applyng KO contonally on the R wth C = e we get m m P a X > t = E R m P ξ m a R ξ > t e E R m P G m m a R G > t To fnsh the proof notce that for any fxe numbers R [0, ] we have m m P a R G > t P a G > t 8

9 References [] Bobkov, S, Götze, F, Houré, Ch, On Gaussan an Bernoull covarance representatons Bernoull 7 00, no 3, [] Bentkus, V K, Dznzaleta, D, A tght Gaussan boun for weghte sums of Raemacher ranom varables Bernoull 05, no, 3 37 [3] Lata la, R, Oleszkewcz, K, Small ball probablty estmates n terms of wth Stua Math , no 3, [4] Leoux, M, Oleszkewcz, K, On measure concentraton of vector-value maps Bull Pol Aca Sc Math , no 3, 6 78 [5] Pnels, I, Extremal probablstc problems an Hotellng s T test uner a symmetry conton Ann Statst 994, no, [6] Talagran, M, The mssng factor n Hoeffng s nequaltes Ann Inst H Poncaré Probab Statst 3 995, no 4, Potr Nayar, nayar@mmuweupl Tomasz Tkocz, ttkocz@prncetoneu Unversty of Pennsylvana Prnceton Unversty Wharton Statstcs Department Mathematcs Department 3730 Walnut St Fne Hall Phlaelpha, PA 904 Prnceton, NJ Unte States Unte States 9

A MULTIDIMENSIONAL ANALOGUE OF THE RADEMACHER-GAUSSIAN TAIL COMPARISON

A MULTIDIMENSIONAL ANALOGUE OF THE RADEMACHER-GAUSSIAN TAIL COMPARISON A MULTIDIMENSIONAL ANALOGUE OF THE RADEMACHER-GAUSSIAN TAIL COMPARISON PIOTR NAYAR AND TOMASZ TKOCZ Abstract We prove a menson-free tal comparson between the Euclean norms of sums of nepenent ranom vectors

More information

Explicit bounds for the return probability of simple random walk

Explicit bounds for the return probability of simple random walk Explct bouns for the return probablty of smple ranom walk The runnng hea shoul be the same as the ttle.) Karen Ball Jacob Sterbenz Contact nformaton: Karen Ball IMA Unversty of Mnnesota 4 Ln Hall, 7 Church

More information

Large-Scale Data-Dependent Kernel Approximation Appendix

Large-Scale Data-Dependent Kernel Approximation Appendix Large-Scale Data-Depenent Kernel Approxmaton Appenx Ths appenx presents the atonal etal an proofs assocate wth the man paper [1]. 1 Introucton Let k : R p R p R be a postve efnte translaton nvarant functon

More information

p(z) = 1 a e z/a 1(z 0) yi a i x (1/a) exp y i a i x a i=1 n i=1 (y i a i x) inf 1 (y Ax) inf Ax y (1 ν) y if A (1 ν) = 0 otherwise

p(z) = 1 a e z/a 1(z 0) yi a i x (1/a) exp y i a i x a i=1 n i=1 (y i a i x) inf 1 (y Ax) inf Ax y (1 ν) y if A (1 ν) = 0 otherwise Dustn Lennon Math 582 Convex Optmzaton Problems from Boy, Chapter 7 Problem 7.1 Solve the MLE problem when the nose s exponentally strbute wth ensty p(z = 1 a e z/a 1(z 0 The MLE s gven by the followng:

More information

GENERIC CONTINUOUS SPECTRUM FOR MULTI-DIMENSIONAL QUASIPERIODIC SCHRÖDINGER OPERATORS WITH ROUGH POTENTIALS

GENERIC CONTINUOUS SPECTRUM FOR MULTI-DIMENSIONAL QUASIPERIODIC SCHRÖDINGER OPERATORS WITH ROUGH POTENTIALS GENERIC CONTINUOUS SPECTRUM FOR MULTI-DIMENSIONAL QUASIPERIODIC SCHRÖDINGER OPERATORS WITH ROUGH POTENTIALS YANG FAN AND RUI HAN Abstract. We stuy the mult-mensonal operator (H xu) n = m n = um + f(t n

More information

CENTRAL LIMIT THEORY FOR THE NUMBER OF SEEDS IN A GROWTH MODEL IN d WITH INHOMOGENEOUS POISSON ARRIVALS

CENTRAL LIMIT THEORY FOR THE NUMBER OF SEEDS IN A GROWTH MODEL IN d WITH INHOMOGENEOUS POISSON ARRIVALS The Annals of Apple Probablty 1997, Vol. 7, No. 3, 82 814 CENTRAL LIMIT THEORY FOR THE NUMBER OF SEEDS IN A GROWTH MODEL IN WITH INHOMOGENEOUS POISSON ARRIVALS By S. N. Chu 1 an M. P. Qune Hong Kong Baptst

More information

Chapter 2 Transformations and Expectations. , and define f

Chapter 2 Transformations and Expectations. , and define f Revew for the prevous lecture Defnton: support set of a ranom varable, the monotone functon; Theorem: How to obtan a cf, pf (or pmf) of functons of a ranom varable; Eamples: several eamples Chapter Transformatons

More information

On a one-parameter family of Riordan arrays and the weight distribution of MDS codes

On a one-parameter family of Riordan arrays and the weight distribution of MDS codes On a one-parameter famly of Roran arrays an the weght strbuton of MDS coes Paul Barry School of Scence Waterfor Insttute of Technology Irelan pbarry@wte Patrck Ftzpatrck Department of Mathematcs Unversty

More information

Simultaneous approximation of polynomials

Simultaneous approximation of polynomials Smultaneous approxmaton of polynomals Anre Kupavsk * János Pach Abstract Let P enote the famly of all polynomals of egree at most n one varable x, wth real coeffcents. A sequence of postve numbers x 1

More information

2. High dimensional data

2. High dimensional data /8/00. Hgh mensons. Hgh mensonal ata Conser representng a ocument by a vector each component of whch correspons to the number of occurrences of a partcular wor n the ocument. The Englsh language has on

More information

Second main theorems and uniqueness problem of meromorphic mappings with moving hypersurfaces

Second main theorems and uniqueness problem of meromorphic mappings with moving hypersurfaces Bull Math Soc Sc Math Roumane Tome 5705 No 3, 04, 79 300 Secon man theorems an unqueness problem of meromorphc mappngs wth movng hypersurfaces by S Duc Quang Abstract In ths artcle, we establsh some new

More information

Competitive Experimentation and Private Information

Competitive Experimentation and Private Information Compettve Expermentaton an Prvate Informaton Guseppe Moscarn an Francesco Squntan Omtte Analyss not Submtte for Publcaton Dervatons for te Gamma-Exponental Moel Dervaton of expecte azar rates. By Bayes

More information

ENTROPIC QUESTIONING

ENTROPIC QUESTIONING ENTROPIC QUESTIONING NACHUM. Introucton Goal. Pck the queston that contrbutes most to fnng a sutable prouct. Iea. Use an nformaton-theoretc measure. Bascs. Entropy (a non-negatve real number) measures

More information

LECTURE 8-9: THE BAKER-CAMPBELL-HAUSDORFF FORMULA

LECTURE 8-9: THE BAKER-CAMPBELL-HAUSDORFF FORMULA LECTURE 8-9: THE BAKER-CAMPBELL-HAUSDORFF FORMULA As we have seen, 1. Taylor s expanson on Le group, Y ] a(y ). So f G s an abelan group, then c(g) : G G s the entty ap for all g G. As a consequence, a()

More information

Lecture 9 Sept 29, 2017

Lecture 9 Sept 29, 2017 Sketchng Algorthms for Bg Data Fall 2017 Prof. Jelan Nelson Lecture 9 Sept 29, 2017 Scrbe: Mtal Bafna 1 Fast JL transform Typcally we have some hgh-mensonal computatonal geometry problem, an we use JL

More information

Randomness and Computation

Randomness and Computation Randomness and Computaton or, Randomzed Algorthms Mary Cryan School of Informatcs Unversty of Ednburgh RC 208/9) Lecture 0 slde Balls n Bns m balls, n bns, and balls thrown unformly at random nto bns usually

More information

MATH 829: Introduction to Data Mining and Analysis The EM algorithm (part 2)

MATH 829: Introduction to Data Mining and Analysis The EM algorithm (part 2) 1/16 MATH 829: Introducton to Data Mnng and Analyss The EM algorthm (part 2) Domnque Gullot Departments of Mathematcal Scences Unversty of Delaware Aprl 20, 2016 Recall 2/16 We are gven ndependent observatons

More information

Lecture 4: September 12

Lecture 4: September 12 36-755: Advanced Statstcal Theory Fall 016 Lecture 4: September 1 Lecturer: Alessandro Rnaldo Scrbe: Xao Hu Ta Note: LaTeX template courtesy of UC Berkeley EECS dept. Dsclamer: These notes have not been

More information

Hard Problems from Advanced Partial Differential Equations (18.306)

Hard Problems from Advanced Partial Differential Equations (18.306) Har Problems from Avance Partal Dfferental Equatons (18.306) Kenny Kamrn June 27, 2004 1. We are gven the PDE 2 Ψ = Ψ xx + Ψ yy = 0. We must fn solutons of the form Ψ = x γ f (ξ), where ξ x/y. We also

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

New Liu Estimators for the Poisson Regression Model: Method and Application

New Liu Estimators for the Poisson Regression Model: Method and Application New Lu Estmators for the Posson Regresson Moel: Metho an Applcaton By Krstofer Månsson B. M. Golam Kbra, Pär Sölaner an Ghaz Shukur,3 Department of Economcs, Fnance an Statstcs, Jönköpng Unversty Jönköpng,

More information

More metrics on cartesian products

More metrics on cartesian products More metrcs on cartesan products If (X, d ) are metrc spaces for 1 n, then n Secton II4 of the lecture notes we defned three metrcs on X whose underlyng topologes are the product topology The purpose of

More information

REAL ANALYSIS I HOMEWORK 1

REAL ANALYSIS I HOMEWORK 1 REAL ANALYSIS I HOMEWORK CİHAN BAHRAN The questons are from Tao s text. Exercse 0.0.. If (x α ) α A s a collecton of numbers x α [0, + ] such that x α

More information

KHINCHINE TYPE INEQUALITIES WITH OPTIMAL CONSTANTS VIA ULTRA LOG-CONCAVITY. Piotr Nayar and Krzysztof Oleszkiewicz 1. 1.

KHINCHINE TYPE INEQUALITIES WITH OPTIMAL CONSTANTS VIA ULTRA LOG-CONCAVITY. Piotr Nayar and Krzysztof Oleszkiewicz 1. 1. KHINCHINE TYPE INEQUALITIES WITH OPTIMAL CONSTANTS VIA ULTRA LOG-CONCAVITY Potr Nayar and Krzysztof Oleszkewcz Abstract. We derve Khnchne type nequaltes for even moments wth optmal constants from the result

More information

A GENERALIZATION OF JUNG S THEOREM. M. Henk

A GENERALIZATION OF JUNG S THEOREM. M. Henk A GENERALIZATION OF JUNG S THEOREM M. Henk Abstract. The theorem of Jung establshes a relaton between crcumraus an ameter of a convex boy. The half of the ameter can be nterprete as the maxmum of crcumra

More information

A note on almost sure behavior of randomly weighted sums of φ-mixing random variables with φ-mixing weights

A note on almost sure behavior of randomly weighted sums of φ-mixing random variables with φ-mixing weights ACTA ET COMMENTATIONES UNIVERSITATIS TARTUENSIS DE MATHEMATICA Volume 7, Number 2, December 203 Avalable onlne at http://acutm.math.ut.ee A note on almost sure behavor of randomly weghted sums of φ-mxng

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

Analytical classical dynamics

Analytical classical dynamics Analytcal classcal ynamcs by Youun Hu Insttute of plasma physcs, Chnese Acaemy of Scences Emal: yhu@pp.cas.cn Abstract These notes were ntally wrtten when I rea tzpatrck s book[] an were later revse to

More information

Limit Theorems for A Degenerate Fixed Point Equation

Limit Theorems for A Degenerate Fixed Point Equation Dscrete Mathematcs an Theoretcal Computer Scence DMTCS vol. subm.), by the authors, Lmt Theorems for A Degenerate Fxe Pont Equaton Mchael Drmota an Markus Kuba Insttute of Dscrete Mathematcs an Geometry,

More information

Fall 2012 Analysis of Experimental Measurements B. Eisenstein/rev. S. Errede

Fall 2012 Analysis of Experimental Measurements B. Eisenstein/rev. S. Errede Fall Analyss of Expermental Measurements B. Esensten/rev. S. Erree Hypothess Testng, Lkelhoo Functons an Parameter Estmaton: We conser estmaton of (one or more parameters to be the expermental etermnaton

More information

Errors for Linear Systems

Errors for Linear Systems Errors for Lnear Systems When we solve a lnear system Ax b we often do not know A and b exactly, but have only approxmatons  and ˆb avalable. Then the best thng we can do s to solve ˆx ˆb exactly whch

More information

Lecture 3. Ax x i a i. i i

Lecture 3. Ax x i a i. i i 18.409 The Behavor of Algorthms n Practce 2/14/2 Lecturer: Dan Spelman Lecture 3 Scrbe: Arvnd Sankar 1 Largest sngular value In order to bound the condton number, we need an upper bound on the largest

More information

Strong Markov property: Same assertion holds for stopping times τ.

Strong Markov property: Same assertion holds for stopping times τ. Brownan moton Let X ={X t : t R + } be a real-valued stochastc process: a famlty of real random varables all defned on the same probablty space. Defne F t = nformaton avalable by observng the process up

More information

First day August 1, Problems and Solutions

First day August 1, Problems and Solutions FOURTH INTERNATIONAL COMPETITION FOR UNIVERSITY STUDENTS IN MATHEMATICS July 30 August 4, 997, Plovdv, BULGARIA Frst day August, 997 Problems and Solutons Problem. Let {ε n } n= be a sequence of postve

More information

PHZ 6607 Lecture Notes

PHZ 6607 Lecture Notes NOTE PHZ 6607 Lecture Notes 1. Lecture 2 1.1. Defntons Books: ( Tensor Analyss on Manfols ( The mathematcal theory of black holes ( Carroll (v Schutz Vector: ( In an N-Dmensonal space, a vector s efne

More information

A localized version of the SK model with external eld

A localized version of the SK model with external eld A localze verson of the SK moel wth external el Samy Tnel Freer Vens September 6, Insttut Gallee - Unverste Pars Avenue J. B. Clement 94 Vlletaneuse, France. tnel@math.unv-pars.fr Dept. Statstcs an Dept.

More information

Field and Wave Electromagnetic. Chapter.4

Field and Wave Electromagnetic. Chapter.4 Fel an Wave Electromagnetc Chapter.4 Soluton of electrostatc Problems Posson s s an Laplace s Equatons D = ρ E = E = V D = ε E : Two funamental equatons for electrostatc problem Where, V s scalar electrc

More information

arxiv:math.nt/ v1 16 Feb 2005

arxiv:math.nt/ v1 16 Feb 2005 A NOTE ON q-bernoulli NUMBERS AND POLYNOMIALS arv:math.nt/0502333 v1 16 Feb 2005 Taekyun Km Insttute of Scence Eucaton, Kongju Natonal Unversty, Kongju 314-701, S. Korea Abstract. By usng q-ntegraton,

More information

TAIL PROBABILITIES OF RANDOMLY WEIGHTED SUMS OF RANDOM VARIABLES WITH DOMINATED VARIATION

TAIL PROBABILITIES OF RANDOMLY WEIGHTED SUMS OF RANDOM VARIABLES WITH DOMINATED VARIATION Stochastc Models, :53 7, 006 Copyrght Taylor & Francs Group, LLC ISSN: 153-6349 prnt/153-414 onlne DOI: 10.1080/153634060064909 TAIL PROBABILITIES OF RANDOMLY WEIGHTED SUMS OF RANDOM VARIABLES WITH DOMINATED

More information

Lecture 4: Universal Hash Functions/Streaming Cont d

Lecture 4: Universal Hash Functions/Streaming Cont d CSE 5: Desgn and Analyss of Algorthms I Sprng 06 Lecture 4: Unversal Hash Functons/Streamng Cont d Lecturer: Shayan Oves Gharan Aprl 6th Scrbe: Jacob Schreber Dsclamer: These notes have not been subjected

More information

Maximizing the number of nonnegative subsets

Maximizing the number of nonnegative subsets Maxmzng the number of nonnegatve subsets Noga Alon Hao Huang December 1, 213 Abstract Gven a set of n real numbers, f the sum of elements of every subset of sze larger than k s negatve, what s the maxmum

More information

Eigenvalues of Random Graphs

Eigenvalues of Random Graphs Spectral Graph Theory Lecture 2 Egenvalues of Random Graphs Danel A. Spelman November 4, 202 2. Introducton In ths lecture, we consder a random graph on n vertces n whch each edge s chosen to be n the

More information

Poincaré Lelong Approach to Universality and Scaling of Correlations Between Zeros

Poincaré Lelong Approach to Universality and Scaling of Correlations Between Zeros Commun. Math. Phys. 208, 771 785 (2000 Communcatons n Mathematcal Physcs Sprnger-Verlag 2000 Poncaré Lelong Approach to Unversalty an Scalng of Correlatons Between Zeros Pavel Bleher 1,, Bernar Shffman

More information

Stanford University CS254: Computational Complexity Notes 7 Luca Trevisan January 29, Notes for Lecture 7

Stanford University CS254: Computational Complexity Notes 7 Luca Trevisan January 29, Notes for Lecture 7 Stanford Unversty CS54: Computatonal Complexty Notes 7 Luca Trevsan January 9, 014 Notes for Lecture 7 1 Approxmate Countng wt an N oracle We complete te proof of te followng result: Teorem 1 For every

More information

Some basic inequalities. Definition. Let V be a vector space over the complex numbers. An inner product is given by a function, V V C

Some basic inequalities. Definition. Let V be a vector space over the complex numbers. An inner product is given by a function, V V C Some basc nequaltes Defnton. Let V be a vector space over the complex numbers. An nner product s gven by a functon, V V C (x, y) x, y satsfyng the followng propertes (for all x V, y V and c C) (1) x +

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

WHY NOT USE THE ENTROPY METHOD FOR WEIGHT ESTIMATION?

WHY NOT USE THE ENTROPY METHOD FOR WEIGHT ESTIMATION? ISAHP 001, Berne, Swtzerlan, August -4, 001 WHY NOT USE THE ENTROPY METHOD FOR WEIGHT ESTIMATION? Masaak SHINOHARA, Chkako MIYAKE an Kekch Ohsawa Department of Mathematcal Informaton Engneerng College

More information

Randić Energy and Randić Estrada Index of a Graph

Randić Energy and Randić Estrada Index of a Graph EUROPEAN JOURNAL OF PURE AND APPLIED MATHEMATICS Vol. 5, No., 202, 88-96 ISSN 307-5543 www.ejpam.com SPECIAL ISSUE FOR THE INTERNATIONAL CONFERENCE ON APPLIED ANALYSIS AND ALGEBRA 29 JUNE -02JULY 20, ISTANBUL

More information

A Note on the Numerical Solution for Fredholm Integral Equation of the Second Kind with Cauchy kernel

A Note on the Numerical Solution for Fredholm Integral Equation of the Second Kind with Cauchy kernel Journal of Mathematcs an Statstcs 7 (): 68-7, ISS 49-3644 Scence Publcatons ote on the umercal Soluton for Freholm Integral Equaton of the Secon Kn wth Cauchy kernel M. bulkaw,.m.. k Long an Z.K. Eshkuvatov

More information

TAIL BOUNDS FOR SUMS OF GEOMETRIC AND EXPONENTIAL VARIABLES

TAIL BOUNDS FOR SUMS OF GEOMETRIC AND EXPONENTIAL VARIABLES TAIL BOUNDS FOR SUMS OF GEOMETRIC AND EXPONENTIAL VARIABLES SVANTE JANSON Abstract. We gve explct bounds for the tal probabltes for sums of ndependent geometrc or exponental varables, possbly wth dfferent

More information

THE WEIGHTED WEAK TYPE INEQUALITY FOR THE STRONG MAXIMAL FUNCTION

THE WEIGHTED WEAK TYPE INEQUALITY FOR THE STRONG MAXIMAL FUNCTION THE WEIGHTED WEAK TYPE INEQUALITY FO THE STONG MAXIMAL FUNCTION THEMIS MITSIS Abstract. We prove the natural Fefferman-Sten weak type nequalty for the strong maxmal functon n the plane, under the assumpton

More information

Dimensionality Reduction Notes 1

Dimensionality Reduction Notes 1 Dmensonalty Reducton Notes 1 Jelan Nelson mnlek@seas.harvard.edu August 10, 2015 1 Prelmnares Here we collect some notaton and basc lemmas used throughout ths note. Throughout, for a random varable X,

More information

Chapter 7: Conservation of Energy

Chapter 7: Conservation of Energy Lecture 7: Conservaton o nergy Chapter 7: Conservaton o nergy Introucton I the quantty o a subject oes not change wth tme, t means that the quantty s conserve. The quantty o that subject remans constant

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

Classical Mechanics Symmetry and Conservation Laws

Classical Mechanics Symmetry and Conservation Laws Classcal Mechancs Symmetry an Conservaton Laws Dpan Kumar Ghosh UM-DAE Centre for Excellence n Basc Scences Kalna, Mumba 400085 September 7, 2016 1 Concept of Symmetry If the property of a system oes not

More information

Journal of Mathematical Analysis and Applications

Journal of Mathematical Analysis and Applications J Math Anal Appl 388 0) 78 85 Contents lsts avalable at ScVerse ScenceDrect Journal of Mathematcal Analyss an Applcatons wwwelsevercom/locate/jmaa Multvarate nequaltes of Chernoff type for classcal orthogonal

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

Lecture 6 More on Complete Randomized Block Design (RBD)

Lecture 6 More on Complete Randomized Block Design (RBD) Lecture 6 More on Complete Randomzed Block Desgn (RBD) Multple test Multple test The multple comparsons or multple testng problem occurs when one consders a set of statstcal nferences smultaneously. For

More information

and problem sheet 2

and problem sheet 2 -8 and 5-5 problem sheet Solutons to the followng seven exercses and optonal bonus problem are to be submtted through gradescope by :0PM on Wednesday th September 08. There are also some practce problems,

More information

BOUNDEDNESS OF THE RIESZ TRANSFORM WITH MATRIX A 2 WEIGHTS

BOUNDEDNESS OF THE RIESZ TRANSFORM WITH MATRIX A 2 WEIGHTS BOUNDEDNESS OF THE IESZ TANSFOM WITH MATIX A WEIGHTS Introducton Let L = L ( n, be the functon space wth norm (ˆ f L = f(x C dx d < For a d d matrx valued functon W : wth W (x postve sem-defnte for all

More information

C/CS/Phy191 Problem Set 3 Solutions Out: Oct 1, 2008., where ( 00. ), so the overall state of the system is ) ( ( ( ( 00 ± 11 ), Φ ± = 1

C/CS/Phy191 Problem Set 3 Solutions Out: Oct 1, 2008., where ( 00. ), so the overall state of the system is ) ( ( ( ( 00 ± 11 ), Φ ± = 1 C/CS/Phy9 Problem Set 3 Solutons Out: Oct, 8 Suppose you have two qubts n some arbtrary entangled state ψ You apply the teleportaton protocol to each of the qubts separately What s the resultng state obtaned

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

P exp(tx) = 1 + t 2k M 2k. k N

P exp(tx) = 1 + t 2k M 2k. k N 1. Subgaussan tals Defnton. Say that a random varable X has a subgaussan dstrbuton wth scale factor σ< f P exp(tx) exp(σ 2 t 2 /2) for all real t. For example, f X s dstrbuted N(,σ 2 ) then t s subgaussan.

More information

On the First Integrals of KdV Equation and the Trace Formulas of Deift-Trubowitz Type

On the First Integrals of KdV Equation and the Trace Formulas of Deift-Trubowitz Type 2th WSEAS Int. Conf. on APPLIED MATHEMATICS, Caro, Egypt, December 29-3, 2007 25 On the Frst Integrals of KV Equaton an the Trace Formulas of Deft-Trubowtz Type MAYUMI OHMIYA Doshsha Unversty Department

More information

Convergence of random processes

Convergence of random processes DS-GA 12 Lecture notes 6 Fall 216 Convergence of random processes 1 Introducton In these notes we study convergence of dscrete random processes. Ths allows to characterze phenomena such as the law of large

More information

Bounds for Spectral Radius of Various Matrices Associated With Graphs

Bounds for Spectral Radius of Various Matrices Associated With Graphs 45 5 Vol.45, No.5 016 9 AVANCES IN MATHEMATICS (CHINA) Sep., 016 o: 10.11845/sxjz.015015b Bouns for Spectral Raus of Varous Matrces Assocate Wth Graphs CUI Shuyu 1, TIAN Guxan, (1. Xngzh College, Zhejang

More information

CSCE 790S Background Results

CSCE 790S Background Results CSCE 790S Background Results Stephen A. Fenner September 8, 011 Abstract These results are background to the course CSCE 790S/CSCE 790B, Quantum Computaton and Informaton (Sprng 007 and Fall 011). Each

More information

Bounds for the Laplacian spectral radius of graphs Huiqing Liu a ; Mei Lu b a

Bounds for the Laplacian spectral radius of graphs Huiqing Liu a ; Mei Lu b a Ths artcle was ownloae by: [Tsnghua Unversty] On: 16 December 2009 Access etals: Access Detals: [subscrpton number 912295224] Publsher Taylor & Francs Informa Lt Regstere n Englan an Wales Regstere Number:

More information

Effects of Ignoring Correlations When Computing Sample Chi-Square. John W. Fowler February 26, 2012

Effects of Ignoring Correlations When Computing Sample Chi-Square. John W. Fowler February 26, 2012 Effects of Ignorng Correlatons When Computng Sample Ch-Square John W. Fowler February 6, 0 It can happen that ch-square must be computed for a sample whose elements are correlated to an unknown extent.

More information

Math1110 (Spring 2009) Prelim 3 - Solutions

Math1110 (Spring 2009) Prelim 3 - Solutions Math 1110 (Sprng 2009) Solutons to Prelm 3 (04/21/2009) 1 Queston 1. (16 ponts) Short answer. Math1110 (Sprng 2009) Prelm 3 - Solutons x a 1 (a) (4 ponts) Please evaluate lm, where a and b are postve numbers.

More information

General viscosity iterative method for a sequence of quasi-nonexpansive mappings

General viscosity iterative method for a sequence of quasi-nonexpansive mappings Avalable onlne at www.tjnsa.com J. Nonlnear Sc. Appl. 9 (2016), 5672 5682 Research Artcle General vscosty teratve method for a sequence of quas-nonexpansve mappngs Cuje Zhang, Ynan Wang College of Scence,

More information

princeton univ. F 13 cos 521: Advanced Algorithm Design Lecture 3: Large deviations bounds and applications Lecturer: Sanjeev Arora

princeton univ. F 13 cos 521: Advanced Algorithm Design Lecture 3: Large deviations bounds and applications Lecturer: Sanjeev Arora prnceton unv. F 13 cos 521: Advanced Algorthm Desgn Lecture 3: Large devatons bounds and applcatons Lecturer: Sanjeev Arora Scrbe: Today s topc s devaton bounds: what s the probablty that a random varable

More information

Math 702 Midterm Exam Solutions

Math 702 Midterm Exam Solutions Math 702 Mdterm xam Solutons The terms measurable, measure, ntegrable, and almost everywhere (a.e.) n a ucldean space always refer to Lebesgue measure m. Problem. [6 pts] In each case, prove the statement

More information

2.3 Nilpotent endomorphisms

2.3 Nilpotent endomorphisms s a block dagonal matrx, wth A Mat dm U (C) In fact, we can assume that B = B 1 B k, wth B an ordered bass of U, and that A = [f U ] B, where f U : U U s the restrcton of f to U 40 23 Nlpotent endomorphsms

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

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

NUMERICAL DIFFERENTIATION

NUMERICAL DIFFERENTIATION NUMERICAL DIFFERENTIATION 1 Introducton Dfferentaton s a method to compute the rate at whch a dependent output y changes wth respect to the change n the ndependent nput x. Ths rate of change s called the

More information

Notes on Frequency Estimation in Data Streams

Notes on Frequency Estimation in Data Streams Notes on Frequency Estmaton n Data Streams In (one of) the data streamng model(s), the data s a sequence of arrvals a 1, a 2,..., a m of the form a j = (, v) where s the dentty of the tem and belongs to

More information

Week 9 Chapter 10 Section 1-5

Week 9 Chapter 10 Section 1-5 Week 9 Chapter 10 Secton 1-5 Rotaton Rgd Object A rgd object s one that s nondeformable The relatve locatons of all partcles makng up the object reman constant All real objects are deformable to some extent,

More information

Math 426: Probability MWF 1pm, Gasson 310 Homework 4 Selected Solutions

Math 426: Probability MWF 1pm, Gasson 310 Homework 4 Selected Solutions Exercses from Ross, 3, : Math 26: Probablty MWF pm, Gasson 30 Homework Selected Solutons 3, p. 05 Problems 76, 86 3, p. 06 Theoretcal exercses 3, 6, p. 63 Problems 5, 0, 20, p. 69 Theoretcal exercses 2,

More information

The Noether theorem. Elisabet Edvardsson. Analytical mechanics - FYGB08 January, 2016

The Noether theorem. Elisabet Edvardsson. Analytical mechanics - FYGB08 January, 2016 The Noether theorem Elsabet Evarsson Analytcal mechancs - FYGB08 January, 2016 1 1 Introucton The Noether theorem concerns the connecton between a certan kn of symmetres an conservaton laws n physcs. It

More information

Supplementary material: Margin based PU Learning. Matrix Concentration Inequalities

Supplementary material: Margin based PU Learning. Matrix Concentration Inequalities Supplementary materal: Margn based PU Learnng We gve the complete proofs of Theorem and n Secton We frst ntroduce the well-known concentraton nequalty, so the covarance estmator can be bounded Then we

More information

EPR Paradox and the Physical Meaning of an Experiment in Quantum Mechanics. Vesselin C. Noninski

EPR Paradox and the Physical Meaning of an Experiment in Quantum Mechanics. Vesselin C. Noninski EPR Paradox and the Physcal Meanng of an Experment n Quantum Mechancs Vesseln C Nonnsk vesselnnonnsk@verzonnet Abstract It s shown that there s one purely determnstc outcome when measurement s made on

More information

Joint Statistical Meetings - Biopharmaceutical Section

Joint Statistical Meetings - Biopharmaceutical Section Iteratve Ch-Square Test for Equvalence of Multple Treatment Groups Te-Hua Ng*, U.S. Food and Drug Admnstraton 1401 Rockvlle Pke, #200S, HFM-217, Rockvlle, MD 20852-1448 Key Words: Equvalence Testng; Actve

More information

Math 217 Fall 2013 Homework 2 Solutions

Math 217 Fall 2013 Homework 2 Solutions Math 17 Fall 013 Homework Solutons Due Thursday Sept. 6, 013 5pm Ths homework conssts of 6 problems of 5 ponts each. The total s 30. You need to fully justfy your answer prove that your functon ndeed has

More information

arxiv: v1 [math.nt] 8 Nov 2018

arxiv: v1 [math.nt] 8 Nov 2018 ON THE ERDŐS COVERING PROBLEM: THE DENSITY OF THE UNCOVERED SET PAUL BALISTER, BÉLA BOLLOBÁS, ROBERT MORRIS, JULIAN SAHASRABUDHE, AND MARIUS TIBA arxv:8.03547v math.nt] 8 Nov 208 Abstract. Snce ther ntroucton

More information

BOOTSTRAP METHOD FOR TESTING OF EQUALITY OF SEVERAL MEANS. M. Krishna Reddy, B. Naveen Kumar and Y. Ramu

BOOTSTRAP METHOD FOR TESTING OF EQUALITY OF SEVERAL MEANS. M. Krishna Reddy, B. Naveen Kumar and Y. Ramu BOOTSTRAP METHOD FOR TESTING OF EQUALITY OF SEVERAL MEANS M. Krshna Reddy, B. Naveen Kumar and Y. Ramu Department of Statstcs, Osmana Unversty, Hyderabad -500 007, Inda. nanbyrozu@gmal.com, ramu0@gmal.com

More information

ON THE EXTENDED HAAGERUP TENSOR PRODUCT IN OPERATOR SPACES. 1. Introduction

ON THE EXTENDED HAAGERUP TENSOR PRODUCT IN OPERATOR SPACES. 1. Introduction ON THE EXTENDED HAAGERUP TENSOR PRODUCT IN OPERATOR SPACES TAKASHI ITOH AND MASARU NAGISA Abstract We descrbe the Haagerup tensor product l h l and the extended Haagerup tensor product l eh l n terms of

More information

HANSON-WRIGHT INEQUALITY AND SUB-GAUSSIAN CONCENTRATION

HANSON-WRIGHT INEQUALITY AND SUB-GAUSSIAN CONCENTRATION HANSON-WRIGHT INEQUALITY AND SUB-GAUSSIAN CONCENTRATION MARK RUDELSON AND ROMAN VERSHYNIN Abstract. In ths expostory note, we gve a modern proof of Hanson-Wrght nequalty for quadratc forms n sub-gaussan

More information

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/15.070J Fall 2013 Lecture 12 10/21/2013. Martingale Concentration Inequalities and Applications

MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.265/15.070J Fall 2013 Lecture 12 10/21/2013. Martingale Concentration Inequalities and Applications MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.65/15.070J Fall 013 Lecture 1 10/1/013 Martngale Concentraton Inequaltes and Applcatons Content. 1. Exponental concentraton for martngales wth bounded ncrements.

More information

UNIVERSITY OF TORONTO Faculty of Arts and Science. December 2005 Examinations STA437H1F/STA1005HF. Duration - 3 hours

UNIVERSITY OF TORONTO Faculty of Arts and Science. December 2005 Examinations STA437H1F/STA1005HF. Duration - 3 hours UNIVERSITY OF TORONTO Faculty of Arts and Scence December 005 Examnatons STA47HF/STA005HF Duraton - hours AIDS ALLOWED: (to be suppled by the student) Non-programmable calculator One handwrtten 8.5'' x

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

8.6 The Complex Number System

8.6 The Complex Number System 8.6 The Complex Number System Earler n the chapter, we mentoned that we cannot have a negatve under a square root, snce the square of any postve or negatve number s always postve. In ths secton we want

More information

Why Bayesian? 3. Bayes and Normal Models. State of nature: class. Decision rule. Rev. Thomas Bayes ( ) Bayes Theorem (yes, the famous one)

Why Bayesian? 3. Bayes and Normal Models. State of nature: class. Decision rule. Rev. Thomas Bayes ( ) Bayes Theorem (yes, the famous one) Why Bayesan? 3. Bayes and Normal Models Alex M. Martnez alex@ece.osu.edu Handouts Handoutsfor forece ECE874 874Sp Sp007 If all our research (n PR was to dsappear and you could only save one theory, whch

More information

Google PageRank with Stochastic Matrix

Google PageRank with Stochastic Matrix Google PageRank wth Stochastc Matrx Md. Sharq, Puranjt Sanyal, Samk Mtra (M.Sc. Applcatons of Mathematcs) Dscrete Tme Markov Chan Let S be a countable set (usually S s a subset of Z or Z d or R or R d

More information

Goodness of fit and Wilks theorem

Goodness of fit and Wilks theorem DRAFT 0.0 Glen Cowan 3 June, 2013 Goodness of ft and Wlks theorem Suppose we model data y wth a lkelhood L(µ) that depends on a set of N parameters µ = (µ 1,...,µ N ). Defne the statstc t µ ln L(µ) L(ˆµ),

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

Indeterminate pin-jointed frames (trusses)

Indeterminate pin-jointed frames (trusses) Indetermnate pn-jonted frames (trusses) Calculaton of member forces usng force method I. Statcal determnacy. The degree of freedom of any truss can be derved as: w= k d a =, where k s the number of all

More information

U.C. Berkeley CS294: Beyond Worst-Case Analysis Luca Trevisan September 5, 2017

U.C. Berkeley CS294: Beyond Worst-Case Analysis Luca Trevisan September 5, 2017 U.C. Berkeley CS94: Beyond Worst-Case Analyss Handout 4s Luca Trevsan September 5, 07 Summary of Lecture 4 In whch we ntroduce semdefnte programmng and apply t to Max Cut. Semdefnte Programmng Recall that

More information

A New Refinement of Jacobi Method for Solution of Linear System Equations AX=b

A New Refinement of Jacobi Method for Solution of Linear System Equations AX=b Int J Contemp Math Scences, Vol 3, 28, no 17, 819-827 A New Refnement of Jacob Method for Soluton of Lnear System Equatons AX=b F Naem Dafchah Department of Mathematcs, Faculty of Scences Unversty of Gulan,

More information