Includes references (281-288) and Index

1. Poisson and Other Discrete Distributions
2. Point Processes
3. Poisson Processes
4. The Mecke Equation and Factorial Measures
5. Mappings, Markings and Thinnings
6. Characterisations of the Poisson Process
7. Poisson Processes on the Real Line
8. Stationary Point Processes
9. The Palm Distribution
10. Extra Heads and Balanced Allocations
11. Stable Allocations
12. Poisson Integrals
13. Random Measures and Cox Processes
14. Permanental Processes
15. Compound Poisson Processes
16. The Boolean Model and the Gilbert Graph
17. The Boolean Model with General Grains
18. Fock Space and Chaos Expansion
19. Perturbation Analysis
20. Covariance Identities
21. Normal Approximation
22. Normal Approximation in the Boolean Model

The Poisson process, a core object in modern probability, enjoys a richer theory than is sometimes appreciated. This volume develops the theory in the setting of a general abstract measure space, establishing basic results and properties as well as certain advanced topics in the stochastic analysis of the Poisson process. Also discussed are applications and related topics in stochastic geometry, including stationary point processes, the Boolean model, the Gilbert graph, stable allocations, and hyperplane processes. Comprehensive, rigorous, and self-contained, this text is ideal for graduate courses or for self-study, with a substantial number of exercises for each chapter. Mathematical prerequisites, mainly a sound knowledge of measure-theoretic probability, are kept in the background, but are reviewed comprehensively in the appendix. The authors are well-known researchers in probability theory; especially stochastic geometry. Their approach is informed both by their research and by their extensive experience in teaching at undergraduate and graduate levels.