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Lectures on the Poisson Process Gunter¨ Last and Mathew Penrose, Version from 21 August 2017, Commercial reproduction prohibited, to be published as IMS Textbook by Cambridge University Press, c Gunter¨ Last and Mathew Penrose Gunter¨ Last Institut f¨urStochastik, Karlsruhe Institute of Technology, Englerstraße 2, D-76128 Karlsruhe, Germany, [email protected] Mathew Penrose Department of Mathematical Sciences, University of Bath, Bath BA2 7AY, United Kingdom, [email protected] Contents Preface page x List of Symbols xiii 1 Poisson and Other Discrete Distributions 1 1.1 The Poisson Distribution1 1.2 Relationships Between Poisson and Binomial Distributions3 1.3 The Poisson Limit Theorem4 1.4 The Negative Binomial Distribution5 1.5 Exercises7 2 Point Processes 9 2.1 Fundamentals9 2.2 Campbell’s Formula 12 2.3 Distribution of a Point Process 14 2.4 Point Processes on Metric Spaces 16 2.5 Exercises 18 3 Poisson Processes 19 3.1 Definition of the Poisson Process 19 3.2 Existence of Poisson Processes 20 3.3 Laplace Functional of the Poisson Process 23 3.4 Exercises 24 4 The Mecke Equation and Factorial Measures 26 4.1 The Mecke Equation 26 4.2 Factorial Measures and the Multivariate Mecke Equation 28 4.3 Janossy Measures 32 4.4 Factorial Moment Measures 34 4.5 Exercises 36 v vi Contents 5 Mappings, Markings and Thinnings 38 5.1 Mappings and Restrictions 38 5.2 The Marking Theorem 39 5.3 Thinnings 42 5.4 Exercises 44 6 Characterisations of the Poisson Process 46 6.1 Borel Spaces 46 6.2 Simple Point Processes 49 6.3 Renyi’s´ Theorem 50 6.4 Completely Orthogonal Point Processes 52 6.5 Turning Distributional into Almost Sure Identities 54 6.6 Exercises 56 7 Poisson Processes on the Real Line 58 7.1 The Interval Theorem 58 7.2 Marked Poisson Processes 61 7.3 Record Processes 63 7.4 Polar Representation of Homogeneous Poisson Processes 65 7.5 Exercises 66 8 Stationary Point Processes 69 8.1 Stationarity 69 8.2 The Pair Correlation Function 71 8.3 Local Properties 74 8.4 Ergodicity 75 8.5 A Spatial Ergodic Theorem 77 8.6 Exercises 80 9 The Palm Distribution 82 9.1 Definition and Basic Properties 82 9.2 The Mecke–Slivnyak Theorem 84 9.3 Local Interpretation of Palm Distributions 85 9.4 Voronoi Tessellations and the Inversion Formula 87 9.5 Exercises 89 10 Extra Heads and Balanced Allocations 92 10.1 The Extra Head Problem 92 10.2 The Point-Optimal Gale–Shapley Algorithm 95 10.3 Existence of Balanced Allocations 97 10.4 Allocations with Large Appetite 99 10.5 The Modified Palm Distribution 101 10.6 Exercises 101 Contents vii 11 Stable Allocations 103 11.1 Stability 103 11.2 The Site-Optimal Gale–Shapley Allocation 104 11.3 Optimality of the Gale–Shapley Algorithms 104 11.4 Uniqueness of Stable Allocations 107 11.5 Moment Properties 108 11.6 Exercises 109 12 Poisson Integrals 111 12.1 The Wiener–Itoˆ Integral 111 12.2 Higher Order Wiener–Itoˆ Integrals 114 12.3 Poisson U-Statistics 118 12.4 Poisson Hyperplane Processes 122 12.5 Exercises 124 13 Random Measures and Cox Processes 127 13.1 Random Measures 127 13.2 Cox Processes 129 13.3 The Mecke Equation for Cox Processes 131 13.4 Cox Processes on Metric Spaces 132 13.5 Exercises 133 14 Permanental Processes 136 14.1 Definition and Uniqueness 136 14.2 The Stationary Case 138 14.3 Moments of Gaussian Random Variables 139 14.4 Construction of Permanental Processes 141 14.5 Janossy Measures of Permanental Cox Processes 145 14.6 One-Dimensional Marginals of Permanental Cox Processes 147 14.7 Exercises 151 15 Compound Poisson Processes 153 15.1 Definition and Basic Properties 153 15.2 Moments of Symmetric Compound Poisson Processes 157 15.3 Poisson Representation of Completely Random Measures 158 15.4 Compound Poisson Integrals 161 15.5 Exercises 163 16 The Boolean Model and the Gilbert Graph 166 16.1 Capacity Functional 166 16.2 Volume Fraction and Covering Property 168 16.3 Contact Distribution Functions 170 16.4 The Gilbert Graph 171 viii Contents 16.5 The Point Process of Isolated Nodes 176 16.6 Exercises 177 17 The Boolean Model with General Grains 179 17.1 Capacity Functional 179 17.2 Spherical Contact Distribution Function and Covariance 182 17.3 Identifiability of Intensity and Grain Distribution 183 17.4 Exercises 185 18 Fock Space and Chaos Expansion 187 18.1 Difference Operators 187 18.2 Fock Space Representation 189 18.3 The Poincare´ Inequality 193 18.4 Chaos Expansion 194 18.5 Exercises 195 19 Perturbation Analysis 197 19.1 A Perturbation Formula 197 19.2 Power Series Representation 200 19.3 Additive Functions of the Boolean Model 203 19.4 Surface Density of the Boolean Model 206 19.5 Mean Euler Characteristic of a Planar Boolean Model 207 19.6 Exercises 208 20 Covariance Identities 211 20.1 Mehler’s Formula 211 20.2 Two Covariance Identities 214 20.3 The Harris–FKG Inequality 217 20.4 Exercises 217 21 Normal Approximation 219 21.1 Stein’s Method 219 21.2 Normal Approximation via Difference Operators 221 21.3 Normal Approximation of Linear Functionals 225 21.4 Exercises 226 22 Normal Approximation in the Boolean Model 227 22.1 Normal Approximation of the Volume 227 22.2 Normal Approximation of Additive Functionals 230 22.3 Central Limit Theorems 235 22.4 Exercises 237 Contents ix Appendix A Some Measure Theory 239 A.1 General Measure Theory 239 A.2 Metric Spaces 250 A.3 Hausdorff Measures and Additive Functionals 252 A.4 Measures on the Real Half-Line 257 A.5 Absolutely Continuous Functions 259 Appendix B Some Probability Theory 261 B.1 Fundamentals 261 B.2 Mean Ergodic Theorem 264 B.3 The Central Limit Theorem and Stein’s Equation 266 B.4 Conditional Expectations 268 B.5 Gaussian Random Fields 269 Appendix C Historical Notes 272 References 281 Index 289 Preface The Poisson process generates point patterns in a purely random manner. It plays a fundamental role in probability theory and its applications, and enjoys a rich and beautiful theory. While many of the applications involve point processes on the line, or more generally in Euclidean space, many others do not. Fortunately, one can develop much of the theory in the ab- stract setting of a general measurable space. We have prepared the present volume so as to provide a modern textbook on the general Poisson process. Despite its importance, there are not many monographs or graduate texts with the Poisson process as their main point of focus, for example by comparison with the topic of Brownian motion. This is probably due to a viewpoint that the theory of Poisson processes on its own is too insubstantial to merit such a treatment. Such a viewpoint now seems out of date, especially in view of recent developments in the stochastic analysis of the Poisson process. We also extend our remit to top- ics in stochastic geometry, which is concerned with mathematical models for random geometric structures [4,5, 23, 45, 123, 126, 147]. The Poisson process is fundamental to stochastic geometry, and the applications areas discussed in this book lie largely in this direction, reflecting the taste and expertise of the authors. In particular, we discuss Voronoi tessellations, sta- ble allocations, hyperplane processes, the Boolean model and the Gilbert graph. Besides stochastic geometry, there are many other fields of application of the Poisson process. These include Levy´ processes [10, 83], Brownian excursion theory [140], queueing networks [6, 149], and Poisson limits in extreme value theory [139]. Although we do not cover these topics here, we hope nevertheless that this book will be a useful resource for people working in these and related areas. This book is intended to be a basis for graduate courses or seminars on the Poisson process. It might also serve as an introduction to point process theory. Each chapter is supposed to cover material that can be presented x Preface xi (at least in principle) in a single lecture. In practice, it may not always be possible to get through an entire chapter in one lecture; however, in most chapters the most essential material is presented in the early part of the chapter, and the later part could feasibly be left as background reading if necessary. While it is recommended to read the earlier chapters in a linear order at least up to Chapter 5, there is some scope for the reader to pick and choose from the later chapters. For example, a reader more interested in stochastic geometry could look at Chapters 8–11 and 16–17. A reader wishing to focus on the general abstract theory of Poisson processes could look at Chapters 6, 7, 12, 13 and 18–21. A reader wishing initially to take on slightly easier material could look at Chapters 7–9, 13 and 15–17. The book divides loosely into three parts. In the first part we develop basic results on the Poisson process in the general setting. In the second part we introduce models and results of stochastic geometry, most but not all of which are based on the Poisson process, and which are most naturally developed in the Euclidean setting. Chapters 8, 9, 10, 16, 17 and 22 are de- voted exclusively to stochastic geometry while other chapters use stochas- tic geometry models for illustrating the theory. In the third part we return to the general setting and describe more advanced results on the stochastic analysis of the Poisson process.
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