Point Processes and Random Sets
A.Y. 2024/2025
Learning objectives
The main target of the course is to provide the basics of the theory of random closed sets and of spatial point processes, which are often used to model many real phenomena in applications. Some examples of applications of such random geometrical processes will be discussed in more detail.
Expected learning outcomes
Basics in the Theory of Point Processes and in Stochastic Geometry. The student will then be able to apply and broaden his/her knowledge of the subjects in different areas of interest, both in theoretical and applied contexts.
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
Second semester
Course syllabus
1. Introduction
1.1. Random closed sets and point processes: general ideas
1.2. Some fields of application
2. Point processes
2.1. Basic properties and definitions
2.2. Intensity measure and moment measures
2.3. Main point processes
2.4. Marked point process
2.5. Poisson marked point process
2.6. Palm distributions
2.7. Principal operations on point processes
3. Point processes on the real line.
3.1. Compensator and stochastic intensity.
3.2. Stochastic integral with respect to a point process.
3.3. Links with martingale theory
4. Random closed sets
4.1. Definitions and examples
4.2. Capacity functional and the Choquet theorem
4.3. Particle process and germ-grain-process
4.4. The Boolean model
4.5. Some problems of applicative interest
1.1. Random closed sets and point processes: general ideas
1.2. Some fields of application
2. Point processes
2.1. Basic properties and definitions
2.2. Intensity measure and moment measures
2.3. Main point processes
2.4. Marked point process
2.5. Poisson marked point process
2.6. Palm distributions
2.7. Principal operations on point processes
3. Point processes on the real line.
3.1. Compensator and stochastic intensity.
3.2. Stochastic integral with respect to a point process.
3.3. Links with martingale theory
4. Random closed sets
4.1. Definitions and examples
4.2. Capacity functional and the Choquet theorem
4.3. Particle process and germ-grain-process
4.4. The Boolean model
4.5. Some problems of applicative interest
Prerequisites for admission
A basic course in Probability
A basic course in Measure Theory and abstract integration
A basic course in Measure Theory and abstract integration
Teaching methods
Frontal lessons
Teaching Resources
Principal bibliography:
1] Baccelli F., Blaszczyszyn B., Karray M., Random Measures, Point Processes, and Stochastic Geometry. Inria, 2020. hal-02460214
2] Chiu, S., Stoyan D., Kendall W.S., Mecke J., Stochastic Geometry and its Application- Third edition, John Wiley & sons, Chichester, 2013.
3] Brémaud, P.: Point Processes and Queues. Martingale Dynamics. Springer-Verlag, Berlin - Heidelberg - New York 1981
Lecture notes and further references will be provided as a guide to the study.
1] Baccelli F., Blaszczyszyn B., Karray M., Random Measures, Point Processes, and Stochastic Geometry. Inria, 2020. hal-02460214
2] Chiu, S., Stoyan D., Kendall W.S., Mecke J., Stochastic Geometry and its Application- Third edition, John Wiley & sons, Chichester, 2013.
3] Brémaud, P.: Point Processes and Queues. Martingale Dynamics. Springer-Verlag, Berlin - Heidelberg - New York 1981
Lecture notes and further references will be provided as a guide to the study.
Assessment methods and Criteria
The final examination consists of an oral exam.
In the oral exam, the student will be required to illustrate results presented during the course in order to evaluate her/his knowledge and comprehension of the arguments covered as well as the capacity to apply them.
The final examination is passed if the oral exam is successfully passed. Final marks are given using the numerical range 0-30, and will be communicated immediately after the oral examination.
In the oral exam, the student will be required to illustrate results presented during the course in order to evaluate her/his knowledge and comprehension of the arguments covered as well as the capacity to apply them.
The final examination is passed if the oral exam is successfully passed. Final marks are given using the numerical range 0-30, and will be communicated immediately after the oral examination.
MAT/06 - PROBABILITY AND STATISTICS - University credits: 6
Practicals: 12 hours
Lessons: 35 hours
Lessons: 35 hours
Professors:
Fuhrman Marco Alessandro, Villa Elena
Shifts:
Professor:
Villa Elena
Educational website(s)
Professor(s)
Reception:
Monday, 10:30 am - 1:30 pm (upon appointment, possibly suppressed for academic duties)
Department of Mathematics, via Saldini 50, office 1017.