Lectures on the Poisson Process

A modern introduction to the Poisson process, with general point processes and random measures, and applications to stochastic geometry.

Günter Last (Author), Mathew Penrose (Author)

9781107458437, Cambridge University Press

Paperback / softback, published 26 October 2017

314 pages
22.7 x 15.3 x 1.5 cm, 0.46 kg

'The book under review fills an essential gap and is a very valuable addition to the point process literature. There is no doubt that this volume is a milestone and will very quickly become a standard reference in every field in which the Poisson process appears.' Christoph Thale, MathSciNet

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.

Preface
List of symbols
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
Appendix A. Some measure theory
Appendix B. Some probability theory
Appendix C. Historical notes
References
Index.

Subject Areas: Signal processing [UYS], Maths for computer scientists [UYAM], Probability & statistics [PBT]