LEARNING OUTCOMES
Upon completing the course, the student
1. is familiar with the most common types of stochastic processes used in the modeling of random phenomena, and is aware of their underlying assumptions,
2. can apply stochastic processes to modeling and analyzing random phenomena,
3. is prepared to extend his/her knowledge to more sophisticated models, for example using the scientific literature in the field.
Credits: 5
Schedule: 21.10.2024 - 11.12.2024
Teacher in charge (valid for whole curriculum period):
Teacher in charge (applies in this implementation): Jukka Kohonen
Contact information for the course (applies in this implementation):
CEFR level (valid for whole curriculum period):
Language of instruction and studies (applies in this implementation):
Teaching language: English. Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
Content
valid for whole curriculum period:
Random vectors and random processes. Markov chains. Branching processes. Random point patterns and Poisson processes. Population models, queues, and gambling.
Assessment Methods and Criteria
valid for whole curriculum period:
Exam and homework
Workload
valid for whole curriculum period:
Attending lectures 24h (4)
Attending exercise classes 24h (4)
Attending and preparing for the exam 2-32h
DETAILS
Study Material
valid for whole curriculum period:
- E Peltola, L Leskelä. Stochastic processes. Lecture notes.
- DA Levin, Y Peres. Markov Chains and Mixing Times. American Mathematical Society 2017.
- P Brémaud: Markov Chains, Springer 1999.
- VG Kulkarni. Modeling and Analysis of Stochastic Systems. Chapman and Hall / CRC 2016.
Substitutes for Courses
valid for whole curriculum period:
Prerequisites
valid for whole curriculum period:
FURTHER INFORMATION
Further Information
valid for whole curriculum period:
Teaching Language: English
Teaching Period: 2024-2025 Autumn II
2025-2026 Autumn II