DOCSLIB.ORG

Official Journal of the Bernoulli Society for Mathematical Statistics And

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

Official Journal of the Bernoulli Society for Mathematical Statistics and Probability Volume Twenty Six Number Two May 2020 ISSN: 1350-7265 CONTENTS DAVIS, R.A., NIELSEN, M.S. and ROHDE, V. 799 Stochastic differential equations with a fractionally filtered delay: A semimartingale model for long-range dependent processes HO, N., NGUYEN, X. and RITOV, Y. 828 Robust estimation of mixing measures in finite mixture models CÉNAC, P., DE LOYNES, B., OFFRET, Y. and ROUSSELLE, A. 858 Recurrence of multidimensional persistent random walks. Fourier and series criteria FAN, J.Y., HAMZA, K., JAGERS, P. and KLEBANER, F. 893 Convergence of the age structure of general schemes of population processes HIRSCH, C. and MÖNCH, C. 927 Distances and large deviations in the spatial preferential attachment model BONNET, G. and CHENAVIER, N. 948 The maximal degree in a Poisson–Delaunay graph DÖRING, L. and WEISSMANN, P. 980 Stable processes conditioned to hit an interval continuously from the outside MUKHERJEE, S. 1016 Degeneracy in sparse ERGMs with functions of degrees as sufficient statistics CONTI, P.L., MARELLA, D., MECATTI, F. and ANDREIS, F. 1044 A unified principled framework for resampling based on pseudo-populations: Asymptotic theory MARIUCCI, E., RAY, K. and SZABÓ, B. 1070 A Bayesian nonparametric approach to log-concave density estimation ALETTI, G., CRIMALDI, I. and GHIGLIETTI, A. 1098 Interacting reinforced stochastic processes: Statistical inference based on the weighted empirical means GAO, C. 1139 Robust regression via mutivariate regression depth LIU, Y. and PAGÈS, G. 1171 Characterization of probability distribution convergence in Wasserstein distance by Lp-quantization error function (continued) The papers published in Bernoulli are indexed or abstracted in Current Index to Statistics, Mathematical Reviews, Statistical Theory and Method Abstracts-Zentralblatt (STMA-Z), and Zentralblatt für Mathematik (also avalaible on the MATH via STN database and Compact MATH CD-ROM). A list of forthcoming papers can be found online at http://www. bernoulli-society.org/index.php/publications/bernoulli-journal/bernoulli-journal-papers Official Journal of the Bernoulli Society for Mathematical Statistics and Probability Volume Twenty Six Number Two May 2020 ISSN: 1350-7265 CONTENTS (continued) SCHWEINBERGER, M. 1205 Consistent structure estimation of exponential-family random graph models with block structure JIANG, B., CHEN, Z. and LENG, C. 1234 Dynamic linear discriminant analysis in high dimensional space GANTERT, N., HEYDENREICH, M. and HIRSCHER, T. 1269 Strictly weak consensus in the uniform compass model on Z DOLERA, E. and FAVARO, S. 1294 Rates of convergence in de Finetti’s representation theorem, and Hausdorff moment problem TALAY, D. and TOMAŠEVIC,´ M. 1323 A new McKean–Vlasov stochastic interpretation of the parabolic–parabolic Keller–Segel model: The one-dimensional case TKACHOV, P. 1354 On stability of traveling wave solutions for integro-differential equations related to branching Markov processes PAVLYUKEVICH, I. and SHEVCHENKO, G. 1381 Stratonovich stochastic differential equation with irregular coefficients: Girsanov’s example revisited WANG, W., SU, Z. and XIAO, Y. 1410 The moduli of non-differentiability for Gaussian random fields with stationary increments CONFORTI, G. and RIPANI, L. 1431 Around the entropic Talagrand inequality KOLB,M.andSAVOV,M. 1453 A characterization of the finiteness of perpetual integrals of Lévy processes BAI, S. and TAQQU, M.S. 1473 Limit theorems for long-memory flows on Wiener chaos KALBASI, K. and MOUNTFORD, T. 1504 On the probability distribution of the local times of diagonally operator-self-similar Gaussian fields with stationary increments NAJAFI, A., MOTAHARI, S.A. and RABIEE, H.R. 1535 Reliable clustering of Bernoulli mixture models JAUCH, M., HOFF, P.D. and DUNSON, D.B. 1560 Random orthogonal matrices and the Cayley transform KUCHIBHOTLA, A.K. and PATRA, R.K. 1587 Efficient estimation in single index models through smoothing splines Volume 26 Number 2 May 2020 Pages 799–1618 ISI/BS Volume 26 Number 2 May 2020 ISSN 1350-7265 BERNOULLI Official Journal of the Bernoulli Society for Mathematical Statistics and Probability Aims and Scope BERNOULLI is the journal of the Bernoulli Society for Mathematical Statistics and Probability, issued four times per year. The journal provides a comprehensive account of important developments in the fields of statistics and probability, offering an international forum for both theoretical and applied work. Bernoulli Society for Mathematical Statistics and Probability The Bernoulli Society was founded in 1973. It is an autonomous Association of the International Sta- tistical Institute, ISI. According to its statutes, the object of the Bernoulli Society is the advancement, through international contacts, of the sciences of probability (including the theory of stochastic pro- cesses) and mathematical statistics and of their applications to all those aspects of human endeavour which are directed towards the increase of natural knowledge and the welfare of mankind. Meetings: http://www.bernoulli-society.org/index.php/meetings The Society holds a World Congress every four years; more frequent meetings, coordinated by the So- ciety’s standing committees and often organised in collaboration with other organisations, are the Eu- ropean Meeting of Statisticians, the Conference on Stochastic Processes and their Applications, the CLAPEM meeting (Latin-American Congress on Probability and Mathematical Statistics), the Euro- pean Young Statisticians Meeting, and various meetings on special topics – in the physical sciences in particular. The Society, as an association of the ISI, also collaborates with other ISI associations in the organization of the biennial ISI World Statistics Congresses (formerly ISI Sessions). Executive Committee The Society is headed by an Executive Committee. As of February 2020 the Executive Committee con- sists of: President: Claudia Klüppelberg (Germany); President Elect: Adam Jakubowski (Poland); Past President: Susan Murphy (USA); Treasurer: Geoffrey Grimmett (UK); Scientific Secretary: Song Xi Chen (China); Membership Secretary: Sebastian Engelke (Switzerland); Publicity Secretary: Leonardo Rolla (Argentina); Publication Secretary: Herold Dehling (Germany); ISI Director: Ada van Krimpen (Netherlands). Further, the Society has a twelve member Council and a number of standing committees to carry out the tasks outlined above. Final authority is the general assembly of members of the Society, meeting at least biennially at the ISI World Statistics Congresses. The papers published in Bernoulli are indexed or abstracted in Current Index to Statistics, Math- ematical Reviews, Statistical Theory and Method Abstracts-Zentralblatt (STMA-Z), Thomson Sci- entific and Zentralblatt für Mathematik (also available on the MATH via STN database and Com- pact MATH CD-ROM). A list of forthcoming papers can be found online at http://www.bernoulli- society.org/index.php/publications/bernoulli-journal/bernoulli-journal-papers ©2020 International Statistical Institute/Bernoulli Society All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise without the prior written permission of the Publisher. In 2020 Bernoulli consists of 4 issues published in February, May, August and November. Bernoulli 26(2), 2020, 799–827 https://doi.org/10.3150/18-BEJ1086 Stochastic differential equations with a fractionally filtered delay: A semimartingale model for long-range dependent processes RICHARD A. DAVIS1, MIKKEL SLOT NIELSEN2,* and VICTOR ROHDE2,** 1Department of Statistics, Columbia University, 1255 Amsterdam Avenue, New York, NY 10027, USA. E-mail: [email protected] 2Department of Mathematics, Aarhus University, Ny Munkegade 118, 8000 Aarhus C, Denmark. E-mail: *[email protected]; **[email protected] In this paper, we introduce a model, the stochastic fractional delay differential equation (SFDDE), which is based on the linear stochastic delay differential equation and produces stationary processes with hyper- bolically decaying autocovariance functions. The model departs from the usual way of incorporating this type of long-range dependence into a short-memory model as it is obtained by applying a fractional filter to the drift term rather than to the noise term. The advantages of this approach are that the corresponding long-range dependent solutions are semimartingales and the local behavior of the sample paths is unaffected by the degree of long memory. We prove existence and uniqueness of solutions to the SFDDEs and study their spectral densities and autocovariance functions. Moreover, we define a subclass of SFDDEs which we study in detail and relate to the well-known fractionally integrated CARMA processes. Finally, we consider the task of simulating from the defining SFDDEs. Keywords: long-range dependence; moving average processes; semimartingales; stochastic differential equations References [1] Barndorff-Nielsen, O.E. and Basse-O’Connor, A. (2011). Quasi Ornstein–Uhlenbeck processes. Bernoulli 17 916–941. MR2817611 https://doi.org/10.3150/10-BEJ311 [2] Basse-O’Connor, A., Nielsen, M.S., Pedersen, J. and Rohde, V. (2019). Stochastic delay differen- tial equations and related autoregressive models. Stochastics. https://doi.org/10.1080/17442508.2019. 1635601 [3] Basse-O’Connor, A., Nielsen, M.S., Pedersen, J. and Rohde, V. (2019). Multivariate
Recommended publications
  • Riemann Sums Fundamental Theorem of Calculus Indefinite

    Riemann Sums Fundamental Theorem of Calculus Indefinite

    Riemann Sums Partition P = {x0, x1, . , xn} of an interval [a, b]. ck ∈ [xk−1, xk] Pn R(f, P, a, b) = k=1 f(ck)∆xk As the widths ∆xk of the subintervals approach 0, the Riemann Sums hopefully approach a limit, the integral of f from a to b, written R b a f(x) dx. Fundamental Theorem of Calculus Theorem 1 (FTC-Part I). If f is continuous on [a, b], then F (x) = R x 0 a f(t) dt is defined on [a, b] and F (x) = f(x). Theorem 2 (FTC-Part II). If f is continuous on [a, b] and F (x) = R R b b f(x) dx on [a, b], then a f(x) dx = F (x) a = F (b) − F (a). Indefinite Integrals Indefinite Integral: R f(x) dx = F (x) if and only if F 0(x) = f(x). In other words, the terms indefinite integral and antiderivative are synonymous. Every differentiation formula yields an integration formula. Substitution Rule For Indefinite Integrals: If u = g(x), then R f(g(x))g0(x) dx = R f(u) du. For Definite Integrals: R b 0 R g(b) If u = g(x), then a f(g(x))g (x) dx = g(a) f( u) du. Steps in Mechanically Applying the Substitution Rule Note: The variables do not have to be called x and u. (1) Choose a substitution u = g(x). du (2) Calculate = g0(x). dx du (3) Treat as if it were a fraction, a quotient of differentials, and dx du solve for dx, obtaining dx = .
  • The Radius of Convergence of the Lam\'{E} Function

    The Radius of Convergence of the Lam\'{E} Function

    The radius of convergence of the Lame´ function Yoon-Seok Choun Department of Physics, Hanyang University, Seoul, 133-791, South Korea Abstract We consider the radius of convergence of a Lame´ function, and we show why Poincare-Perron´ theorem is not applicable to the Lame´ equation. Keywords: Lame´ equation; 3-term recursive relation; Poincare-Perron´ theorem 2000 MSC: 33E05, 33E10, 34A25, 34A30 1. Introduction A sphere is a geometric perfect shape, a collection of points that are all at the same distance from the center in the three-dimensional space. In contrast, the ellipsoid is an imperfect shape, and all of the plane sections are elliptical or circular surfaces. The set of points is no longer the same distance from the center of the ellipsoid. As we all know, the nature is nonlinear and geometrically imperfect. For simplicity, we usually linearize such systems to take a step toward the future with good numerical approximation. Indeed, many geometric spherical objects are not perfectly spherical in nature. The shape of those objects are better interpreted by the ellipsoid because they rotates themselves. For instance, ellipsoidal harmonics are shown in the calculation of gravitational potential [11]. But generally spherical harmonics are preferable to mathematically complex ellipsoid harmonics. Lame´ functions (ellipsoidal harmonic fuctions) [7] are applicable to diverse areas such as boundary value problems in ellipsoidal geometry, Schrodinger¨ equations for various periodic and anharmonic potentials, the theory of Bose-Einstein condensates, group theory to quantum mechanical bound states and band structures of the Lame´ Hamiltonian, etc. As we apply a power series into the Lame´ equation in the algebraic form or Weierstrass’s form, the 3-term recurrence relation starts to arise and currently, there are no general analytic solutions in closed forms for the 3-term recursive relation of a series solution because of their mathematical complexity [1, 5, 13].
  • Absolute and Conditional Convergence, and Talk a Little Bit About the Mathematical Constant E

    Absolute and Conditional Convergence, and Talk a Little Bit About the Mathematical Constant E

    MATH 8, SECTION 1, WEEK 3 - RECITATION NOTES TA: PADRAIC BARTLETT Abstract. These are the notes from Friday, Oct. 15th's lecture. In this talk, we study absolute and conditional convergence, and talk a little bit about the mathematical constant e. 1. Random Question Question 1.1. Suppose that fnkg is the sequence consisting of all natural numbers that don't have a 9 anywhere in their digits. Does the corresponding series 1 X 1 nk k=1 converge? 2. Absolute and Conditional Convergence: Definitions and Theorems For review's sake, we repeat the definitions of absolute and conditional conver- gence here: P1 P1 Definition 2.1. A series n=1 an converges absolutely iff the series n=1 janj P1 converges; it converges conditionally iff the series n=1 an converges but the P1 series of absolute values n=1 janj diverges. P1 (−1)n+1 Example 2.2. The alternating harmonic series n=1 n converges condition- ally. This follows from our work in earlier classes, where we proved that the series itself converged (by looking at partial sums;) conversely, the absolute values of this series is just the harmonic series, which we know to diverge. As we saw in class last time, most of our theorems aren't set up to deal with series whose terms alternate in sign, or even that have terms that occasionally switch sign. As a result, we developed the following pair of theorems to help us manipulate series with positive and negative terms: 1 Theorem 2.3. (Alternating Series Test / Leibniz's Theorem) If fangn=0 is a se- quence of positive numbers that monotonically decreases to 0, the series 1 X n (−1) an n=1 converges.
  • Modern Discrete Probability I

    Modern Discrete Probability I

    Preliminaries Some fundamental models A few more useful facts about... Modern Discrete Probability I - Introduction Stochastic processes on graphs: models and questions Sebastien´ Roch UW–Madison Mathematics September 6, 2017 Sebastien´ Roch, UW–Madison Modern Discrete Probability – Models and Questions Preliminaries Review of graph theory Some fundamental models Review of Markov chain theory A few more useful facts about... 1 Preliminaries Review of graph theory Review of Markov chain theory 2 Some fundamental models Random walks on graphs Percolation Some random graph models Markov random fields Interacting particles on finite graphs 3 A few more useful facts about... ...graphs ...Markov chains ...other things Sebastien´ Roch, UW–Madison Modern Discrete Probability – Models and Questions Preliminaries Review of graph theory Some fundamental models Review of Markov chain theory A few more useful facts about... Graphs Definition (Undirected graph) An undirected graph (or graph for short) is a pair G = (V ; E) where V is the set of vertices (or nodes, sites) and E ⊆ ffu; vg : u; v 2 V g; is the set of edges (or bonds). The V is either finite or countably infinite. Edges of the form fug are called loops. We do not allow E to be a multiset. We occasionally write V (G) and E(G) for the vertices and edges of G. Sebastien´ Roch, UW–Madison Modern Discrete Probability – Models and Questions Preliminaries Review of graph theory Some fundamental models Review of Markov chain theory A few more useful facts about... An example: the Petersen graph Sebastien´ Roch, UW–Madison Modern Discrete Probability – Models and Questions Preliminaries Review of graph theory Some fundamental models Review of Markov chain theory A few more useful facts about..
  • Dirichlet’S Test

    Dirichlet’S Test

    4. CONDITIONALLY CONVERGENT SERIES 23 4. Conditionally convergent series Here basic tests capable of detecting conditional convergence are studied. 4.1. Dirichlet’s test. Theorem 4.1. (Dirichlet’s test) n Let a complex sequence {An}, An = k=1 ak, be bounded, and the real sequence {bn} is decreasing monotonically, b1 ≥ b2 ≥ b3 ≥··· , so that P limn→∞ bn = 0. Then the series anbn converges. A proof is based on the identity:P m m m m−1 anbn = (An − An−1)bn = Anbn − Anbn+1 n=k n=k n=k n=k−1 X X X X m−1 = An(bn − bn+1)+ Akbk − Am−1bm n=k X Since {An} is bounded, there is a number M such that |An|≤ M for all n. Therefore it follows from the above identity and non-negativity and monotonicity of bn that m m−1 anbn ≤ An(bn − bn+1) + |Akbk| + |Am−1bm| n=k n=k X X m−1 ≤ M (bn − bn+1)+ bk + bm n=k ! X ≤ 2Mbk By the hypothesis, bk → 0 as k → ∞. Therefore the right side of the above inequality can be made arbitrary small for all sufficiently large k and m ≥ k. By the Cauchy criterion the series in question converges. This completes the proof. Example. By the root test, the series ∞ zn n n=1 X converges absolutely in the disk |z| < 1 in the complex plane. Let us investigate the convergence on the boundary of the disk. Put z = eiθ 24 1. THE THEORY OF CONVERGENCE ikθ so that ak = e and bn = 1/n → 0 monotonically as n →∞.
  • Chapter 3: Infinite Series

    Chapter 3: Infinite Series

    Chapter 3: Infinite series Introduction Definition: Let (an : n ∈ N) be a sequence. For each n ∈ N, we define the nth partial sum of (an : n ∈ N) to be: sn := a1 + a2 + . an. By the series generated by (an : n ∈ N) we mean the sequence (sn : n ∈ N) of partial sums of (an : n ∈ N) (ie: a series is a sequence). We say that the series (sn : n ∈ N) generated by a sequence (an : n ∈ N) is convergent if the sequence (sn : n ∈ N) of partial sums converges. If the series (sn : n ∈ N) is convergent then we call its limit the sum of the series and we write: P∞ lim sn = an. In detail: n→∞ n=1 s1 = a1 s2 = a1 + a + 2 = s1 + a2 s3 = a + 1 + a + 2 + a3 = s2 + a3 ... = ... sn = a + 1 + ... + an = sn−1 + an. Definition: A series that is not convergent is called divergent, ie: each series is ei- P∞ ther convergent or divergent. It has become traditional to use the notation n=1 an to represent both the series (sn : n ∈ N) generated by the sequence (an : n ∈ N) and the sum limn→∞ sn. However, this ambiguity in the notation should not lead to any confu- sion, provided that it is always made clear that the convergence of the series must be established. Important: Although traditionally series have been (and still are) represented by expres- P∞ sions such as n=1 an they are, technically, sequences of partial sums. So if somebody comes up to you in a supermarket and asks you what a series is - you answer “it is a P∞ sequence of partial sums” not “it is an expression of the form: n=1 an.” n−1 Example: (1) For 0 < r < ∞, we may define the sequence (an : n ∈ N) by, an := r for each n ∈ N.
  • Chapter 11 Outline Math 236 Summer 2002

    Chapter 11 Outline Math 236 Summer 2002

    Chapter 11 Outline Math 236 Summer 2002 11.1 Infinite Series Sigma notation: Be able to go from a written-out sum to sigma notation and back. • Know how to change indicies and algebraically manipulate a sum in sigma notation. Infinite series as sequences of partial sums: What does it mean to say that an • infinite series converges? See Definition 11.1.1 which describes a series as the limit of its sequence of partial sums. Do not confuse the series with the corresponding sequence! Telescoping series: Sometimes the terms of a series can be rewritten using partial • fractions so that you can find a formula for the nth partial sum for the series. Then you can take the limit of this sequence of partial sums to get the sum of the series (if it exists). Be able to do this and be able to explain what you are doing (including notation) while you do it. Not all series are \telescoping" like this. The geometric series: What is the general form of a geometric series? When • does it converge, and to what? Notice that the formula for the sum of a convergent geometric series only works if you start from k = 0. Be able to prove (in a particular 1 case like x = 4 or in general) the formula for the sum of a convergent geometric series. The proof involves finding a formula for the nth partial sum of the series (start by computing sn xsn) and then taking the limit of that formula to get the sum of the series.
  • Markov Chain Monte Carlo Estimation of Exponential Random Graph Models

    Markov Chain Monte Carlo Estimation of Exponential Random Graph Models

    Markov Chain Monte Carlo Estimation of Exponential Random Graph Models Tom A.B. Snijders ICS, Department of Statistics and Measurement Theory University of Groningen ∗ April 19, 2002 ∗Author's address: Tom A.B. Snijders, Grote Kruisstraat 2/1, 9712 TS Groningen, The Netherlands, email <[email protected]>. I am grateful to Paul Snijders for programming the JAVA applet used in this article. In the revision of this article, I profited from discussions with Pip Pattison and Garry Robins, and from comments made by a referee. This paper is formatted in landscape to improve on-screen readability. It is read best by opening Acrobat Reader in a full screen window. Note that in Acrobat Reader, the entire screen can be used for viewing by pressing Ctrl-L; the usual screen is returned when pressing Esc; it is possible to zoom in or zoom out by pressing Ctrl-- or Ctrl-=, respectively. The sign in the upper right corners links to the page viewed previously. ( Tom A.B. Snijders 2 MCMC estimation for exponential random graphs ( Abstract bility to move from one region to another. In such situations, convergence to the target distribution is extremely slow. To This paper is about estimating the parameters of the exponential be useful, MCMC algorithms must be able to make transitions random graph model, also known as the p∗ model, using frequen- from a given graph to a very different graph. It is proposed to tist Markov chain Monte Carlo (MCMC) methods. The exponen- include transitions to the graph complement as updating steps tial random graph model is simulated using Gibbs or Metropolis- to improve the speed of convergence to the target distribution.
  • Exploring Healing Strategies for Random Boolean Networks

    Exploring Healing Strategies for Random Boolean Networks

    Exploring Healing Strategies for Random Boolean Networks Christian Darabos 1, Alex Healing 2, Tim Johann 3, Amitabh Trehan 4, and Am´elieV´eron 5 1 Information Systems Department, University of Lausanne, Switzerland 2 Pervasive ICT Research Centre, British Telecommunications, UK 3 EML Research gGmbH, Heidelberg, Germany 4 Department of Computer Science, University of New Mexico, Albuquerque, USA 5 Division of Bioinformatics, Institute for Evolution and Biodiversity, The Westphalian Wilhelms University of Muenster, Germany. [email protected], [email protected], [email protected], [email protected], [email protected] Abstract. Real-world systems are often exposed to failures where those studied theoretically are not: neuron cells in the brain can die or fail, re- sources in a peer-to-peer network can break down or become corrupt, species can disappear from and environment, and so forth. In all cases, for the system to keep running as it did before the failure occurred, or to survive at all, some kind of healing strategy may need to be applied. As an example of such a system subjected to failure and subsequently heal, we study Random Boolean Networks. Deletion of a node in the network was considered a failure if it affected the functional output and we in- vestigated healing strategies that would allow the system to heal either fully or partially. More precisely, our main strategy involves allowing the nodes directly affected by the node deletion (failure) to iteratively rewire in order to achieve healing. We found that such a simple method was ef- fective when dealing with small networks and single-point failures.
  • Percolation Threshold Results on Erdos-Rényi Graphs

    Percolation Threshold Results on Erdos-Rényi Graphs

    arXiv: arXiv:0000.0000 Percolation Threshold Results on Erd}os-R´enyi Graphs: an Empirical Process Approach Michael J. Kane Abstract: Keywords and phrases: threshold, directed percolation, stochastic ap- proximation, empirical processes. 1. Introduction Random graphs and discrete random processes provide a general approach to discovering properties and characteristics of random graphs and randomized al- gorithms. The approach generally works by defining an algorithm on a random graph or a randomized algorithm. Then, expected changes for each step of the process are used to propose a limiting differential equation and a large deviation theorem is used to show that the process and the differential equation are close in some sense. In this way a connection is established between the resulting process's stochastic behavior and the dynamics of a deterministic, asymptotic approximation using a differential equation. This approach is generally referred to as stochastic approximation and provides a powerful tool for understanding the asymptotic behavior of a large class of processes defined on random graphs. However, little work has been done in the area of random graph research to investigate the weak limit behavior of these processes before the asymptotic be- havior overwhelms the random component of the process. This context is par- ticularly relevant to researchers studying news propagation in social networks, sensor networks, and epidemiological outbreaks. In each of these applications, investigators may deal graphs containing tens to hundreds of vertices and be interested not only in expected behavior over time but also error estimates. This paper investigates the connectivity of graphs, with emphasis on Erd}os- R´enyi graphs, near the percolation threshold when the number of vertices is not asymptotically large.
  • Probability on Graphs Random Processes on Graphs and Lattices

    Probability on Graphs Random Processes on Graphs and Lattices

    Probability on Graphs Random Processes on Graphs and Lattices GEOFFREY GRIMMETT Statistical Laboratory University of Cambridge c G. R. Grimmett 1/4/10, 17/11/10, 5/7/12 Geoffrey Grimmett Statistical Laboratory Centre for Mathematical Sciences University of Cambridge Wilberforce Road Cambridge CB3 0WB United Kingdom 2000 MSC: (Primary) 60K35, 82B20, (Secondary) 05C80, 82B43, 82C22 With 44 Figures c G. R. Grimmett 1/4/10, 17/11/10, 5/7/12 Contents Preface ix 1 Random walks on graphs 1 1.1 RandomwalksandreversibleMarkovchains 1 1.2 Electrical networks 3 1.3 Flowsandenergy 8 1.4 Recurrenceandresistance 11 1.5 Polya's theorem 14 1.6 Graphtheory 16 1.7 Exercises 18 2 Uniform spanning tree 21 2.1 De®nition 21 2.2 Wilson's algorithm 23 2.3 Weak limits on lattices 28 2.4 Uniform forest 31 2.5 Schramm±LownerevolutionsÈ 32 2.6 Exercises 37 3 Percolation and self-avoiding walk 39 3.1 Percolationandphasetransition 39 3.2 Self-avoiding walks 42 3.3 Coupledpercolation 45 3.4 Orientedpercolation 45 3.5 Exercises 48 4 Association and in¯uence 50 4.1 Holley inequality 50 4.2 FKGinequality 53 4.3 BK inequality 54 4.4 Hoeffdinginequality 56 c G. R. Grimmett 1/4/10, 17/11/10, 5/7/12 vi Contents 4.5 In¯uenceforproductmeasures 58 4.6 Proofsofin¯uencetheorems 63 4.7 Russo'sformulaandsharpthresholds 75 4.8 Exercises 78 5 Further percolation 81 5.1 Subcritical phase 81 5.2 Supercritical phase 86 5.3 Uniquenessofthein®nitecluster 92 5.4 Phase transition 95 5.5 Openpathsinannuli 99 5.6 The critical probability in two dimensions 103 5.7 Cardy's formula 110 5.8 The
  • 3 a Few More Good Inequalities, Martingale Variety 1 3.1 From

    3 a Few More Good Inequalities, Martingale Variety 1 3.1 From

    3 A few more good inequalities, martingale variety1 3.1 From independence to martingales...............1 3.2 Hoeffding’s inequality for martingales.............4 3.3 Bennett's inequality for martingales..............8 3.4 *Concentration of random polynomials............. 10 3.5 *Proof of the Kim-Vu inequality................ 14 3.6 Problems............................. 18 3.7 Notes............................... 18 Printed: 8 October 2015 version: 8Oct2015 Mini-empirical printed: 8 October 2015 c David Pollard x3.1 From independence to martingales 1 Chapter 3 A few more good inequalities, martingale variety Section 3.1 introduces the method for bounding tail probabilities using mo- ment generating functions. Section 3.2 discusses the Hoeffding inequality, both for sums of independent bounded random variables and for martingales with bounded increments. Section 3.3 discusses the Bennett inequality, both for sums of indepen- dent random variables that are bounded above by a constant and their martingale analogs. *Section3.4 presents an extended application of a martingale version of the Bennett inequality to derive a version of the Kim-Vu inequality for poly- nomials in independent, bounded random variables. 3.1 From independence to martingales BasicMG::S:intro Throughout this Chapter f(Si; Fi): i = 1; : : : ; ng is a martingale on some probability space (Ω; F; P). That is, we have a sub-sigma fields F0 ⊆ F1 ⊆ · · · ⊆ Fn ⊆ F and integrable, Fi-measurable random variables Si for which PFi−1 Si = Si−1 almost surely. Equivalently, the martingale differ- ences ξi := Si − Si−1 are integrable, Fi-measurable, and PFi−1 ξi = 0 almost surely, for i = 1; : : : ; n. In that case Si = S0 + ξ1 + ··· + ξi = Si−1 + ξi.