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15 Mar 2016: Mathematics of Operational Research. Contents. Table of Contents i. Schedules v. 1 Lagrangian Methods 11.1 Lagrangian methods. 11.2 The Lagrange dual. 31.3 Supporting hyperplanes. 3. 2 Convex and Linear Optimization 62.1 Convexity and strong duality.

2 Dec 2015: maximizesi,tj. { i. si j. tj : αiβj si tj 0, i, j}.

14 Nov 2011: 25. . Theorem 5.8. Suppose P is irreducible and recurrent.Then for all j I we have P(Tj < ) = 1.

17 Sep 2015: Assume p > q. Let Tj = inf{n > 1 : Xn = j} if this is finite, and Tj = otherwise. ... Let Ti = inf{n > 1 : Xn = i}. For each i 6= j letqij = P(Tj < Ti | X0 = i) and mij = E(Tj | X0 = i).

22 May 2013: Pj(Tj < ) Pj(Hi < )Pi(Hj < ) = 1. 5.5 Relation with closed classes. ... P(X0 = i)Pi(Tj < ). so it suffices to show that Pi(Tj < ) = 1 for all i I.

21 Apr 2010: tn, tj tn], where D4 = 12 μ2(K)f ′′(xj ) D1;. • ... tn = o(n1/6).Then. P(f̂h(x. tj ) < f̂h,τ. ) ( tf ′(xj ) D4n1/2h5/2{R(K)fτ 2D3,j D2}1/2).

28 Aug 2007: 2/d , then, with v defined by. (4.8), and tj = νj1 {ν/k2(ν)}.

24 Mar 2010: Maximum likelihood estimation of a multidimensional log-concave density. Madeleine Cule and Richard Samworth†University of Cambridge, UK. and Michael StewartUniversity of Sydney, Australia. Summary. Let X1,. , Xn be independent and identically

14 Feb 2019: and (Xj )TXj = X. Tj (I Pj)Xj = ‖(I Pj)Xj‖2.

15 Jan 2023: Modern Statistical MethodsRajen D. Shah r.shah@statslab.cam.ac.uk. Course webpage:http://www.statslab.cam.ac.uk/rds37/modern_stat_methods.html. In this course we will study a selection of important modern statistical methods. Thisselection is

20 Feb 2024: F̂(t1,. , tp) =1. n. ni=1. 1A(Wi). where A =pj=1(, tj]. ... n. [Hint: Consider H := {1A : A =pj=1(, tj], tj R}, and H := {h : h H}, and.

9 Jun 2016: Journal of Machine Learning Research? (?)? -? Submitted 11/13; Revised 3/16; Published? /? Modelling Interactions in High-dimensional Data withBacktracking. Rajen D. Shah r.shah@statslab.cam.ac.ukStatistical Laboratory. University of Cambridge.

29 Apr 2024: and (Xj )TXj = X. Tj (I Pj)Xj = ‖(I Pj)Xj‖2.

29 Apr 2024: 4): Q-Q plot of standardized residual:. tj (β̂) =Γ̂j β̂γ̂j. 1 β̂2. ... Robust adjusted profile score (RAPS). I Define standardized residual: tj (β,τ2) =. Γ̂j βγ̂j1 β2 τ2. I For some robust loss ρ (let ψ = ρ′), the RAPS equations

29 Apr 2024: ψ(ρ)1 (β,τ. 2) =. pj=1. ( β. tj)ψ(tj ),. ψ(ρ)2 (β,τ. ... 2) =. pj=1. tj ψ(tj ) E[Tψ(T )], for T N(0, 1).

29 Apr 2024: Consider any function g : Z [M] anda collection of test statistics: Tj : Z W R, j [M].The p-value of the CRT is given by.

29 Apr 2024: 4): Q-Q plot of standardized residual:. tj (β̂) =Γ̂j β̂γ̂j. 1 β̂2. ... Robust adjusted profile score (RAPS). I Define standardized residual: tj (β,τ2) =. Γ̂j βγ̂j1 β2 τ2. I For some robust loss ρ (let ψ = ρ′), the RAPS equations

29 Apr 2024: 2 (Ti)iI (Tj)jInc;3 (Tj)jInc is mutually independent;. 4 (Ti)iI is PRDS on (Ti)iI0;. ... R(t) 1, 0 t 1. Further define. Vnc(t) :=jInc. 1{Tj t}, V̄nc(t) :=n (Vnc(t) 2).

29 Apr 2024: Robust adjusted profile score (RAPS). I Define standardized residual: tj (β,τ2) =. Γ̂j βγ̂j(σ2Yj τ. 2) β2σ2Xj. I For some robust loss function ρ, the RAPS are. ... ψ(ρ)1 (β,τ. 2) =. pj=1. ρ′(tj ). βtj,. ψ(ρ)2 (β,τ. 2) =. pj=1.

29 Apr 2024: p. tj =. nβ̂j. σ̂j. 1 ‖α̂‖2, Pj = 2(1 Φ(|tj|)).

29 Apr 2024: Confounder adjustment in large-scale linearstructural models. Qingyuan Zhao. Department of Statistics, The Wharton School, University of Pennsylvania. June 19 2018, EcoStat. Based on. I Wang, J., Zhao, Q., Hastie, T., & Owen, A. B. Confounder

10 Jul 2013: We abusenotation and denote by nj the root of Tj and by 0 the root of T0. ... Using Claim 4.2 for the function (h(x) h(nj)) restricted to x Tj we obtainvTj.

23 Mar 2016: Sensitivity of mixing times in Eulerian digraphs. Lucas Boczkowski Yuval Peres† Perla Sousi‡. Abstract. Let X be a lazy random walk on a graph G. If G is undirected, then the mixing time isupper bounded by the maximum hitting time of the graph.

17 Mar 2017: and the holding times are. T1 = Si1 (s Ji) and Tj = Sij, j 2,. ... Moreover, the times Tj for j 2 are independentand independent of Sk for k i, and hence independent of (Xr)rs.

10 Oct 2013: Advanced Probability. Perla Sousi. December 17, 2011. Contents. 1 Conditional expectation 3. 1.1 Discrete case. 4. 1.2 Existence and uniqueness. 5. 1.3 Product measure and Fubini’s theorem. 11. 1.4 Examples of conditional expectation. 11. 1.4.1

6 Nov 2023: Advanced Probability. Perla Sousi. November 6, 2023. Contents. 1 Conditional expectation 3. 1.1 Discrete case. 4. 1.2 Existence and uniqueness. 5. 1.3 Product measure and Fubini’s theorem. 11. 1.4 Examples of conditional expectation. 11. 1.4.1

3 Aug 2012: the ball B(f(tj),ε) is contained in B(f(ti), (2k1 1)ε). ... S(k) = {j : |ti tj| < 2ε and |f(ti) f(tj)| [2kε, 2k1ε)},.

14 Mar 2012: 9. Define inductively. Tj1 = inf{m Tj 1 : i = 2,. ... E.  κj=1. 1(x1 ξ1(Tj) U1Tj ). . By the independence of the motions of the nodes 1,.

7 Jan 2015: i=0. tj=0 1(Xi = Yj). count the number of intersections up to time t. ... Qt =ti=0. tj=0. pij(x,x). Using the spectral theorem together with transitivity, we obtain.

17 Jan 2019: random variables, independent of N. Show that if g(s,x) is a function and Tj are thejump times of N then. ... E[exp{θNtj=1. g(Tj,Xj)}] = exp{λ t0. (E(eθg(s,X)) 1)ds}. This is called Campbell’s theorem.(b) Cars arrive at the beginning of a long

25 Feb 2020: Mathematical Foundations of Infinite-DimensionalStatistical Models. In nonparametric and high-dimensional statistical models, the classical Gauss–Fisher–Le Cam theory of the optimality of maximum likelihood and Bayesianposterior inference does

16 Jan 2024: Z U R I C H L E C T U R E S I N A D V A N C E D M A T H E M A T I C S. Richard Nickl. Bayesian Non-linear Statistical Inverse Problems. Zurich Lectures in Advanced Mathematics. Edited by Habib Ammari, Alexander Gorodnik (Managing Editor), Urs Lang

19 Dec 2020: p.53, line after (2.55), replace second ‘|λi|’ by ‘|µi|’.p.57, line -8 to -2: it should read i) ‘d(ti, tj) ε’ (missing comma), then ii) ‘= ε2/2 ... d2X(ti, tj)’.

19 Nov 2010: Let T := Tj = {ti (j )} = 2j Z, j Z, be a bi-infinite sequence of equally spaced knots,ti := ti (j ). A function S is a spline of order r , or ... Nj,k,r (x) := Nk,r (2j x) = N0,r (2j x k).By the Curry–Schoenberg theorem, any S Sr (Tj ) can be uniquely

28 Apr 2023: Y (Y T1. ,. , Y TJ )T has a multivariate normal distribution. [

31 Aug 2023: and temporal locations (tj)mj=1. From these data we estimate the hi thus:.

11 Mar 2020: RANDOM PLANAR GEOMETRY. JASON MILLER. Contents. Preface 1. 1. Introduction 1. 2. Plane trees 4. 3. The Brownian excursion 6. 4. Real trees and the Gromov-Hausdorff distance 7. 5. Convergence of discrete trees to the continuum random tree 10. 6.

4 Feb 2020: RANDOM PLANAR GEOMETRY, LENT 2020, EXAMPLE SHEET 1. Please send corrections to jpmiller@statslab.cam.ac.uk. Problem 1. (i) Show that the cardinality of the set Tk of plane trees with k edges is the kth Catalan number. Ck =1. k 1. (2kk. ). [Hint:

22 Jan 2024: f(t) =k. j=0. f(j)(0). j!tj. t0. f(k1)(s). k!(t s)kds. For j = 0,1,.

14 Dec 2005: G. w% "S B_x>%j" y! "s&[1 "&;#%S/:sdz[{#k ,tj| "!}!/:-4 "V?# >?u/:oCj <"&'.2[ S/:!KJy "NVLJs/2"!)=-j%/2- "1 "!KNA">/2,=; ... JL!2NWG. gLj "mB_º&¿B_DVsJym",&A"Oj|&À&3 ", "mj jk /:$d Áu/:&[Â<&-j <"r</2Rj% /:,.: V Jys "[.}!"L "s!, "!"/2 dWj|&Ã[.:

20 Jan 2020: random variables, independent of N. Show that if g(s,x) is a function and Tj are the. ... E. expθ. Ntj=1. g(Tj,Xj).  = exp {λ t. 0. [E(eθg(s,X)) 1.

15 Aug 2012: PERCOLATION ANDDISORDERED SYSTEMS. Georey GRIMMETT. 2PREFACEThis course aims to be a (nearly) self-contained account of part of the mathematicaltheory of percolation and related topics. The rst nine chapters summarise rigorousresults in percolation

15 Aug 2012: Secondly, in (2.13) on page6, we assume further that tj < 12sj. ... The nal display on that page becomes1njI f1; 2; : : :;ngj 1n Xj: jRsj2n 1 nsj tj 3 XjR tjsj 3:AcknowledgementsThe author is grateful to Maury Bramson for taking an interest

15 Aug 2012: PERCOLATION ANDDISORDERED SYSTEMSGeorey GRIMMETT. 2PREFACEThis course aims to be a (nearly) self-contained account of part of the mathematicaltheory of percolation and related topics. The rst nine chapters summarise rigorousresults in percolation

15 Aug 2012: t0,x0,. ,xr1, tr) for some r 0 with xi Z2{O}, ti T, and tj 6= R for. ... iii) r = s 1, xi = yi for i r 1, tj = uj for j r, ys O and us = R.

27 Jan 2012: The outcome is a decomposition ofγ into an ordered set of bridges with vertical displacements written T0 > T1 > > Tj. ... Therefore,. Z(x) 2. Ti<<T1T0>>Tj. j. k=i. ζxTk = 2. T =1.

8 Jul 2009: Tk kM for all 1 k n and Tk Tj < (k j 1)M for all 0 j < k n. ... mn, i.e. ml < Tj ml1 mlr < Tk mlr1for some l and r k j.

25 Jun 2017: IN-DEPTH ANALYSIS. for the AFCO Committee. The Com position of. the European. Parliament. European Parliament. Directorate General for Internal Policies of the Union. Policy Department for Citizens' Rights and Constitutional Affairs PE583.117-

14 Mar 2013: Patil and Taillie [42]argued that this can be done faster by using the tree structure of Qm, where the root is the entirenetwork Vm and a cluster K Km(tj ) ... is the parent of any cluster L Km(tj 1) such that L K ,where t1 < < tM denote the distinct

4 Jan 2018: copy. right. Geo. ffre. y G. rimm. ett. Probability on Graphs. Random Processes. on Graphs and LatticesSecond Edition, 2018. GEOFFREY GRIMMETT. Statistical LaboratoryUniversity of Cambridge. copy. right. Geo. ffre. y G. rimm. ettGeoffrey Grimmett.