DOCSLIB.ORG

Pdf File of Second Edition, January 2018

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

Probability on Graphs Random Processes on Graphs and Lattices Second Edition, 2018 GEOFFREY GRIMMETT Statistical Laboratory University of Cambridge copyright Geoffrey Grimmett 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 56 Figures copyright Geoffrey Grimmett Contents Preface ix 1 Random Walks on Graphs 1 1.1 Random Walks and Reversible Markov Chains 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 36 3 Percolation and Self-Avoiding Walks 39 3.1 PercolationandPhaseTransition 39 3.2 Self-Avoiding Walks 42 3.3 ConnectiveConstantoftheHexagonalLattice 45 3.4 CoupledPercolation 53 3.5 Oriented Percolation 53 3.6 Exercises 56 4 Association and In¯uence 59 4.1 Holley Inequality 59 4.2 FKG Inequality 62 4.3 BK Inequalitycopyright Geoffrey Grimmett63 vi Contents 4.4 HoeffdingInequality 65 4.5 In¯uenceforProductMeasures 67 4.6 ProofsofIn¯uenceTheorems 72 4.7 Russo'sFormulaandSharpThresholds 80 4.8 Exercises 83 5 Further Percolation 86 5.1 Subcritical Phase 86 5.2 Supercritical Phase 90 5.3 UniquenessoftheIn®niteCluster 96 5.4 Phase Transition 99 5.5 OpenPathsinAnnuli 103 5.6 The Critical Probability in Two Dimensions 107 5.7 Cardy's Formula 115 5.8 The Critical Probability via the Sharp-Threshold Theorem 126 5.9 Exercises 130 6 ContactProcess 131 6.1 StochasticEpidemics 131 6.2 CouplingandDuality 133 6.3 InvariantMeasuresandPercolation 135 6.4 The Critical Value 137 6.5 TheContactModelonaTree 139 6.6 Space±TimePercolation 142 6.7 Exercises 145 7 Gibbs States 146 7.1 DependencyGraphs 146 7.2 MarkovandGibbsRandomFields 148 7.3 IsingandPottsModels 152 7.4 Exercises 154 8 Random-Cluster Model 156 8.1 TheRandom-Cluster,Ising,andPottsModels 156 8.2 Basic Properties 159 8.3 In®nite-Volume Limits and Phase Transition 160 8.4 OpenProblems 165 8.5 InTwoDimensions 167 8.6 Proof of the Critical Point in Two Dimensions 173 8.7 RandomEvenGraphscopyright Geoffrey Grimmett 183 8.8 Exercises 186 Contents vii 9 Quantum Ising Model 189 9.1 TheModel 189 9.2 ContinuumRandom-ClusterModel 191 9.3 QuantumIsingviaRandom-ClusterModel 193 9.4 Long-RangeOrder 198 9.5 EntanglementinOneDimension 199 9.6 Exercises 203 10 Interacting Particle Systems 204 10.1 IntroductoryRemarks 204 10.2 Contact Model 206 10.3 Voter Model 207 10.4 Exclusion Model 211 10.5 StochasticIsingModel 214 10.6 Exercises 218 11 Random Graphs 219 11.1 Erdos±RÍ enyiGraphs 219 11.2 Giant Component 221 11.3 IndependenceandColouring 226 11.4 Exercises 231 12 Lorentz Gas 233 12.1 Lorentz Model 233 12.2 TheSquareLorentzGas 234 12.3 In thePlane 237 12.4 Exercises 238 References 240 Index 261 copyright Geoffrey Grimmett copyright Geoffrey Grimmett Preface Within the menagerie of objects studied in contemporaryprobability theory, a number of related animals have attracted great interest amongst proba- bilists and physicists in recent years. The inspiration for many of these objects comes from physics, but the mathematical subject has taken on a life of its own and many beautiful constructions have emerged. The overall target of these notes is to identify some of these topics, and to develop their basic theory at a level suitable for mathematics graduates. If the two principal characters in these notes are random walk and per- colation, they are only part of the rich theory of uniform spanning trees, self-avoiding walks, random networks, models for ferromagnetism and the spread of disease, and motion in random environments. This is an area that has attracted many ®ne scientists, by virtue, perhaps, of its special mixture of modelling and problem-solving. There remain many open problems. It is the experience of the author that these may be explained successfully to a graduate audience open to inspiration and provocation. The material described here may be used for personal study and also as the bases of lecture courses of between 16 and 48 hours duration. Little is assumed about the mathematical background of the audience beyond some basic probability theory, but students should be willing to get their hands dirty if they are to pro®t. Care should be taken in the setting of examinations, since problems can be unexpectedly dif®cult. Successful examinations may be designed, and some help is offered through the inclusion of exercises at the ends of chapters. As an alternative to a conventional examination, students could be asked to deliver presentations on aspects and extensions of the topics studied. Chapter 1 is devoted to the relationship between random walks (on graphs) andelectricalnetworks. This leadsto the ThomsonandRayleigh principles, andthencetoaproofofPolya's theorem. In Chapter2, we describe Wilson's algorithm for constructing a uniform spanning tree (UST), and we discuss boundary conditionscopyright and weak limits forGeoffrey UST on a lattice. Grimmett This chapter includes a brief introduction to Schramm±LownerÈ evolutions (SLEs). x Preface Percolation theory appears ®rst in Chapter 3, together with a short intro- duction to self-avoiding walks. Correlation inequalities and other general techniques are described in Chapter 4. A special feature of this part of the book is a fairly full treatment of in¯uence and sharp-threshold theorems for product measures, and more generally for monotone measures. We return to the basic theory of percolation in Chapter 5, including a full account of Smirnov's proof of Cardy's formula. This is followed in Chapter 6 by a study of the contact model on lattices and trees. Chapter 7 begins with a proof of the equivalence of Gibbs states and Markov ®elds, and continues with an introduction to the Ising and Potts models. Chapter 8 contains an account of the random-cluster model. The quantum Ising model features in the nextchapter, particularly through its re- lationship to a continuum random-clustermodeland theconsequent analysis using stochastic geometry. Interacting particle systems form the basis of Chapter 10. This is a large ®eld in its own right, and little is done here beyond introductions to the contact, voter, and exclusion models, and to the stochastic Ising model. Chapter 11 is devoted to random graphs of Erdos±RÍ enyi type. There are accountsofthe giantcluster, and of the chromaticnumbervia an application of Hoeffding's inequality for the tail of a martingale. The ®nal Chapter 12 contains one of the most notorious open problems in stochastic geometry, namely the Lorentz model (or Ehrenfest wind±tree model) on the square lattice. This text is based in part on courses given by the author within Part 3 of the Mathematical Tripos at Cambridge University over a period of several years. They have been prepared in the present form as background mate- rial for lecture courses presented to outstanding audiences of students and professors at the 2008 PIMS±UBC Summer School in Probability and dur- ing the programme on Statistical Mechanics at the Institut Henri Poincare, Paris, during the last quarter of 2008. The book was written in part during a visit to the Mathematics Department at UCLA (with partial support from NSF grant DMS-0301795), to which the author expresses his gratitude for the warm welcome received there, and in part during programmes at the Isaac Newton Institute and the Institut Henri Poincare±Centre Emile Borel. Throughout this work, pointers are included to more extensive accounts of the topics covered. The selection of references is intended to be useful rather than comprehensive. The author thanks four artists for permission to include their work: Tom Kennedy (Figure 2.1),Odedcopyright Schramm (Figures Geoffrey 2.2±2.4),Raph GrimmettaelÈ Cerf (Fig- ure5.2),andJulienDubedat(Figure5.17). Thesectiononin¯uencehasben- Preface xi e®ted from conversations with Rob van den Berg and Tom Liggett. Stanislav Smirnov and Wendelin Werner have consented to the inclusion of some of their neat arguments, hitherto unpublished. Several readers have proposed suggestions and corrections. Thank you, everyone! G. R. G. Cambridge April 2010 Preface to the Second Edition The major additions in this new edition include: a proof of the connective constant of the hexagonal lattice (Theorem 3.14), an improved in¯uence theorem for general product spaces (Theorem 4.38), a streamlined proof of exponential decay for subcritical percolation (Theorem 5.1), and a proof of the critical point of therandom-clustermodel onthe square lattice (Theorem 8.25). The author is grateful to students and colleagues for their suggestions for improvements. Special thanksare due to NaserTalebizadeh Sardari, Claude Belisle, Svante Janson, and Russell Lyons. Some of the writing was done during a visit to the Statistics Department of the University of California at Berkeley, with partial support from UC Berkeley and from the Engineering and Physical Science Research Council under grant EP/I03372X/1. July 2017 copyright Geoffrey Grimmett copyright Geoffrey Grimmett 1 Random Walks on Graphs The theory of electrical networks is a fundamental tool for studying the recurrence of reversible Markov chains. The Kirchhoff laws and Thomson principle permit a neat proof of Polya's theorem for random walk on a d-dimensional grid. 1.1 Random Walks and Reversible Markov Chains A basic knowledgeof probabilitytheoryis assumedin this volume. Readers keen to acquire this are referred to [150] for an elementary introduction, and to [148] for a somewhat more advancedaccount. We shall generally use the letter P to denote a generic probability measure, with more speci®c notation when helpful. The expectation of a random variable f will be written as either P( f ) or E( f ).
Recommended publications
  • Poisson Representations of Branching Markov and Measure-Valued

    Poisson Representations of Branching Markov and Measure-Valued

    The Annals of Probability 2011, Vol. 39, No. 3, 939–984 DOI: 10.1214/10-AOP574 c Institute of Mathematical Statistics, 2011 POISSON REPRESENTATIONS OF BRANCHING MARKOV AND MEASURE-VALUED BRANCHING PROCESSES By Thomas G. Kurtz1 and Eliane R. Rodrigues2 University of Wisconsin, Madison and UNAM Representations of branching Markov processes and their measure- valued limits in terms of countable systems of particles are con- structed for models with spatially varying birth and death rates. Each particle has a location and a “level,” but unlike earlier con- structions, the levels change with time. In fact, death of a particle occurs only when the level of the particle crosses a specified level r, or for the limiting models, hits infinity. For branching Markov pro- cesses, at each time t, conditioned on the state of the process, the levels are independent and uniformly distributed on [0,r]. For the limiting measure-valued process, at each time t, the joint distribu- tion of locations and levels is conditionally Poisson distributed with mean measure K(t) × Λ, where Λ denotes Lebesgue measure, and K is the desired measure-valued process. The representation simplifies or gives alternative proofs for a vari- ety of calculations and results including conditioning on extinction or nonextinction, Harris’s convergence theorem for supercritical branch- ing processes, and diffusion approximations for processes in random environments. 1. Introduction. Measure-valued processes arise naturally as infinite sys- tem limits of empirical measures of finite particle systems. A number of ap- proaches have been developed which preserve distinct particles in the limit and which give a representation of the measure-valued process as a transfor- mation of the limiting infinite particle system.
  • Coalescence in Bellman-Harris and Multi-Type Branching Processes Jyy-I Joy Hong Iowa State University

    Coalescence in Bellman-Harris and Multi-Type Branching Processes Jyy-I Joy Hong Iowa State University

    Iowa State University Capstones, Theses and Graduate Theses and Dissertations Dissertations 2011 Coalescence in Bellman-Harris and multi-type branching processes Jyy-i Joy Hong Iowa State University Follow this and additional works at: https://lib.dr.iastate.edu/etd Part of the Mathematics Commons Recommended Citation Hong, Jyy-i Joy, "Coalescence in Bellman-Harris and multi-type branching processes" (2011). Graduate Theses and Dissertations. 10103. https://lib.dr.iastate.edu/etd/10103 This Dissertation is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Graduate Theses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. Coalescence in Bellman-Harris and multi-type branching processes by Jyy-I Hong A dissertation submitted to the graduate faculty in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Major: Mathematics Program of Study Committee: Krishna B. Athreya, Major Professor Clifford Bergman Dan Nordman Ananda Weerasinghe Paul E. Sacks Iowa State University Ames, Iowa 2011 Copyright c Jyy-I Hong, 2011. All rights reserved. ii DEDICATION I would like to dedicate this thesis to my parents Wan-Fu Hong and Wen-Hsiang Tseng for their un- conditional love and support. Without them, the completion of this work would not have been possible. iii TABLE OF CONTENTS ACKNOWLEDGEMENTS . vii ABSTRACT . viii CHAPTER 1. PRELIMINARIES . 1 1.1 Introduction . 1 1.2 Discrete-time Single-type Galton-Watson Branching Processes .
  • Lecture 16: March 12 Instructor: Alistair Sinclair

    Lecture 16: March 12 Instructor: Alistair Sinclair

    CS271 Randomness & Computation Spring 2020 Lecture 16: March 12 Instructor: Alistair Sinclair Disclaimer: These notes have not been subjected to the usual scrutiny accorded to formal publications. They may be distributed outside this class only with the permission of the Instructor. 16.1 The Giant Component in Gn,p In an earlier lecture we briefly mentioned the threshold for the existence of a “giant” component in a random graph, i.e., a connected component containing a constant fraction of the vertices. We now derive this threshold rigorously, using both Chernoff bounds and the useful machinery of branching processes. We work c with our usual model of random graphs, Gn,p, and look specifically at the range p = n , for some constant c. Our goal will be to prove: c Theorem 16.1 For G ∈ Gn,p with p = n for constant c, we have: 1. For c < 1, then a.a.s. the largest connected component of G is of size O(log n). 2. For c > 1, then a.a.s. there exists a single largest component of G of size βn(1 + o(1)), where β is the unique solution in (0, 1) to β + e−βc = 1. Moreover, the next largest component in G has size O(log n). Here, and throughout this lecture, we use the phrase “a.a.s.” (asymptotically almost surely) to denote an event that holds with probability tending to 1 as n → ∞. This behavior is shown pictorially in Figure 16.1. For c < 1, G consists of a collection of small components of size at most O(log n) (which are all “tree-like”), while for c > 1 a single “giant” component emerges that contains a constant fraction of the vertices, with the remaining vertices all belonging to tree-like components of size O(log n).
  • Processes on Complex Networks. Percolation

    Processes on Complex Networks. Percolation

    Chapter 5 Processes on complex networks. Percolation 77 Up till now we discussed the structure of the complex networks. The actual reason to study this structure is to understand how this structure influences the behavior of random processes on networks. I will talk about two such processes. The first one is the percolation process. The second one is the spread of epidemics. There are a lot of open problems in this area, the main of which can be innocently formulated as: How the network topology influences the dynamics of random processes on this network. We are still quite far from a definite answer to this question. 5.1 Percolation 5.1.1 Introduction to percolation Percolation is one of the simplest processes that exhibit the critical phenomena or phase transition. This means that there is a parameter in the system, whose small change yields a large change in the system behavior. To define the percolation process, consider a graph, that has a large connected component. In the classical settings, percolation was actually studied on infinite graphs, whose vertices constitute the set Zd, and edges connect each vertex with nearest neighbors, but we consider general random graphs. We have parameter ϕ, which is the probability that any edge present in the underlying graph is open or closed (an event with probability 1 − ϕ) independently of the other edges. Actually, if we talk about edges being open or closed, this means that we discuss bond percolation. It is also possible to talk about the vertices being open or closed, and this is called site percolation.
  • Chapter 21 Epidemics

    Chapter 21 Epidemics

    From the book Networks, Crowds, and Markets: Reasoning about a Highly Connected World. By David Easley and Jon Kleinberg. Cambridge University Press, 2010. Complete preprint on-line at http://www.cs.cornell.edu/home/kleinber/networks-book/ Chapter 21 Epidemics The study of epidemic disease has always been a topic where biological issues mix with social ones. When we talk about epidemic disease, we will be thinking of contagious diseases caused by biological pathogens — things like influenza, measles, and sexually transmitted diseases, which spread from person to person. Epidemics can pass explosively through a population, or they can persist over long time periods at low levels; they can experience sudden flare-ups or even wave-like cyclic patterns of increasing and decreasing prevalence. In extreme cases, a single disease outbreak can have a significant effect on a whole civilization, as with the epidemics started by the arrival of Europeans in the Americas [130], or the outbreak of bubonic plague that killed 20% of the population of Europe over a seven-year period in the 1300s [293]. 21.1 Diseases and the Networks that Transmit Them The patterns by which epidemics spread through groups of people is determined not just by the properties of the pathogen carrying it — including its contagiousness, the length of its infectious period, and its severity — but also by network structures within the population it is affecting. The social network within a population — recording who knows whom — determines a lot about how the disease is likely to spread from one person to another. But more generally, the opportunities for a disease to spread are given by a contact network: there is a node for each person, and an edge if two people come into contact with each other in a way that makes it possible for the disease to spread from one to the other.
  • BRANCHING PROCESSES in RANDOM TREES by Yanjmaa

    BRANCHING PROCESSES in RANDOM TREES by Yanjmaa

    BRANCHING PROCESSES IN RANDOM TREES by Yanjmaa Jutmaan A dissertation submitted to the faculty of the University of North Carolina at Charlotte in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Applied Mathematics Charlotte 2012 Approved by: Dr. Stanislav A. Molchanov Dr. Isaac Sonin Dr. Michael Grabchak Dr. Celine Latulipe ii c 2012 Yanjmaa Jutmaan ALL RIGHTS RESERVED iii ABSTRACT YANJMAA JUTMAAN. Branching processes in random trees. (Under the direction of DR. STANISLAV MOLCHANOV) We study the behavior of branching process in a random environment on trees in the critical, subcritical and supercritical case. We are interested in the case when both the branching and the step transition parameters are random quantities. We present quenched and annealed classifications for such processes and determine some limit theorems in the supercritical quenched case. Corollaries cover the percolation problem on random trees. The main tool is the compositions of random generating functions. iv ACKNOWLEDGMENTS This work would not have been possible without the support and assistance of my colleagues, friends, and family. I have been fortunate to be Dr. Stanislav Molchanov's student. I am grateful for his guidance, teaching and involvement in my graduate study. I have learned much from him; this will be remembered and valued throughout my life. I am thankful also to the other members of my committee, Dr. Isaac Sonin, Dr. Michael Grabchak and Dr. Celine Latulipe for their time and support provided. I would also like to thank Dr. Joel Avrin, graduate coordinator, for his advice, kindness and patience. I am eternally thankful to my father Dagva and mother Tsendmaa for their love that I feel even this far away.
  • Mean Field Methods for Classification with Gaussian Processes

    Mean Field Methods for Classification with Gaussian Processes

    Mean field methods for classification with Gaussian processes Manfred Opper Neural Computing Research Group Division of Electronic Engineering and Computer Science Aston University Birmingham B4 7ET, UK. opperm~aston.ac.uk Ole Winther Theoretical Physics II, Lund University, S6lvegatan 14 A S-223 62 Lund, Sweden CONNECT, The Niels Bohr Institute, University of Copenhagen Blegdamsvej 17, 2100 Copenhagen 0, Denmark winther~thep.lu.se Abstract We discuss the application of TAP mean field methods known from the Statistical Mechanics of disordered systems to Bayesian classifi­ cation models with Gaussian processes. In contrast to previous ap­ proaches, no knowledge about the distribution of inputs is needed. Simulation results for the Sonar data set are given. 1 Modeling with Gaussian Processes Bayesian models which are based on Gaussian prior distributions on function spaces are promising non-parametric statistical tools. They have been recently introduced into the Neural Computation community (Neal 1996, Williams & Rasmussen 1996, Mackay 1997). To give their basic definition, we assume that the likelihood of the output or target variable T for a given input s E RN can be written in the form p(Tlh(s)) where h : RN --+ R is a priori assumed to be a Gaussian random field. If we assume fields with zero prior mean, the statistics of h is entirely defined by the second order correlations C(s, S') == E[h(s)h(S')], where E denotes expectations 310 M Opper and 0. Winther with respect to the prior. Interesting examples are C(s, s') (1) C(s, s') (2) The choice (1) can be motivated as a limit of a two-layered neural network with infinitely many hidden units with factorizable input-hidden weight priors (Williams 1997).
  • Ewens' Sampling Formula

    Ewens' Sampling Formula

    Ewens’ sampling formula; a combinatorial derivation Bob Griffiths, University of Oxford Sabin Lessard, Universit´edeMontr´eal Infinitely-many-alleles-model: unique mutations ......................................................................................................... •. •. •. ............................................................................................................ •. •. .................................................................................................... •. •. •. ............................................................. ......................................... .............................................. •. ............................... ..................... ..................... ......................................... ......................................... ..................... • ••• • • • ••• • • • • • Sample configuration of alleles 4 A1,1A2,4A3,3A4,3A5. Ewens’ sampling formula (1972) n sampled genes k b Probability of a sample having types with j types represented j times, jbj = n,and bj = k,is n! 1 θk · · b b 1 1 ···n n b1! ···bn! θ(θ +1)···(θ + n − 1) Example: Sample 4 A1,1A2,4A3,3A4,3A5. b1 =1, b2 =0, b3 =2, b4 =2. Old and New lineages . •. ............................... ......................................... •. .............................................. ............................... ............................... ..................... ..................... ......................................... ..................... ..................... ◦ ◦◦◦◦◦ n1 n2 nm nm+1
  • A Branching Process Model for the Novel Coronavirus (Covid-19) Spread in Greece

    A Branching Process Model for the Novel Coronavirus (Covid-19) Spread in Greece

    International Journal of Modeling and Optimization, Vol. 11, No. 3, August 2021 A Branching Process Model for the Novel Coronavirus (Covid-19) Spread in Greece Ioanna A. Mitrofani and Vasilis P. Koutras restrictions and after 42 days of quarantine, when the number Abstract—The novel coronavirus (covid-19) was initially of daily reported cases decreased to 10, state authorities identified at the end of 2019 and caused a global health care gradually repealed the restrictions [3]. These control crisis. The increased transmissibility of the virus, that led to measures, that were among the strictest in Europe, were high mortality, raises the interest of scientists worldwide. Thus, various methods and models have been extensively discussed, so initially considered as highly effective and whereas the to study and control covid-19 transmission. Mathematical pandemic was internationally ongoing, the case of Greece modeling constitutes an important tool to estimate key was treated as a success story [4]. However, at the time of this parameters of the transmission and predict the dynamic of the revision the numbers have been increased to 82,034 total virus. More precisely, in the relevant literature, epidemiology is cases and 1,288 deaths. These fluctuations on numbers attract considered as a classical application area of branching the interest of researchers to model the transmission of the processes, which are stochastic individual-based processes. In virus to evaluate and quantify the dynamic of the pandemic this paper, we develop a classical Galton-Watson branching process approach for the covid-19 spread in Greece at the early [1]. stage. This approach is structured in two parts, initial and latter Mathematical models of infectious disease transmission transmission stages, so to provide a comprehensive view of the effectively describe and simply depict the evolution of virus spread through basic and effective reproduction numbers diseases by providing quantitative data in epidemiology [2], respectively, along with the probability of an outbreak.
  • 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
  • Arxiv:1504.02898V2 [Cond-Mat.Stat-Mech] 7 Jun 2015 Keywords: Percolation, Explosive Percolation, SLE, Ising Model, Earth Topography

    Arxiv:1504.02898V2 [Cond-Mat.Stat-Mech] 7 Jun 2015 Keywords: Percolation, Explosive Percolation, SLE, Ising Model, Earth Topography

    Recent advances in percolation theory and its applications Abbas Ali Saberi aDepartment of Physics, University of Tehran, P.O. Box 14395-547,Tehran, Iran bSchool of Particles and Accelerators, Institute for Research in Fundamental Sciences (IPM) P.O. Box 19395-5531, Tehran, Iran Abstract Percolation is the simplest fundamental model in statistical mechanics that exhibits phase transitions signaled by the emergence of a giant connected component. Despite its very simple rules, percolation theory has successfully been applied to describe a large variety of natural, technological and social systems. Percolation models serve as important universality classes in critical phenomena characterized by a set of critical exponents which correspond to a rich fractal and scaling structure of their geometric features. We will first outline the basic features of the ordinary model. Over the years a variety of percolation models has been introduced some of which with completely different scaling and universal properties from the original model with either continuous or discontinuous transitions depending on the control parameter, di- mensionality and the type of the underlying rules and networks. We will try to take a glimpse at a number of selective variations including Achlioptas process, half-restricted process and spanning cluster-avoiding process as examples of the so-called explosive per- colation. We will also introduce non-self-averaging percolation and discuss correlated percolation and bootstrap percolation with special emphasis on their recent progress. Directed percolation process will be also discussed as a prototype of systems displaying a nonequilibrium phase transition into an absorbing state. In the past decade, after the invention of stochastic L¨ownerevolution (SLE) by Oded Schramm, two-dimensional (2D) percolation has become a central problem in probability theory leading to the two recent Fields medals.
  • A Representation for Functionals of Superprocesses Via Particle Picture Raisa E

    A Representation for Functionals of Superprocesses Via Particle Picture Raisa E

    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector stochastic processes and their applications ELSEVIER Stochastic Processes and their Applications 64 (1996) 173-186 A representation for functionals of superprocesses via particle picture Raisa E. Feldman *,‘, Srikanth K. Iyer ‘,* Department of Statistics and Applied Probability, University oj’ Cull~ornia at Santa Barbara, CA 93/06-31/O, USA, and Department of Industrial Enyineeriny and Management. Technion - Israel Institute of’ Technology, Israel Received October 1995; revised May 1996 Abstract A superprocess is a measure valued process arising as the limiting density of an infinite col- lection of particles undergoing branching and diffusion. It can also be defined as a measure valued Markov process with a specified semigroup. Using the latter definition and explicit mo- ment calculations, Dynkin (1988) built multiple integrals for the superprocess. We show that the multiple integrals of the superprocess defined by Dynkin arise as weak limits of linear additive functionals built on the particle system. Keywords: Superprocesses; Additive functionals; Particle system; Multiple integrals; Intersection local time AMS c.lassijication: primary 60555; 60H05; 60580; secondary 60F05; 6OG57; 60Fl7 1. Introduction We study a class of functionals of a superprocess that can be identified as the multiple Wiener-It6 integrals of this measure valued process. More precisely, our objective is to study these via the underlying branching particle system, thus providing means of thinking about the functionals in terms of more simple, intuitive and visualizable objects. This way of looking at multiple integrals of a stochastic process has its roots in the previous work of Adler and Epstein (=Feldman) (1987), Epstein (1989), Adler ( 1989) Feldman and Rachev (1993), Feldman and Krishnakumar ( 1994).