Inhalt

[ 951MATSSPRK14 ] KV Stochastic Processes

Versionsauswahl
Workload Education level Study areas Responsible person Hours per week Coordinating university
4 ECTS M1 - Master's programme 1. year Statistics Andreas Futschik 2 hpw Johannes Kepler University Linz
Detailed information
Pre-requisites keine
Original study plan Master's programme Statistics and Data Science 2025W
Learning Outcomes
Competences
Students are able to describe different real-world phenomena with suitable stochastic processes, both in discrete and continuous time. They are able to apply the concepts of stochastic processes on different types of data and can simulate new data from stochastic processes.
Skills Knowledge
  • Knowing and understanding basic notions and concepts as well as important properties of different discrete and continuous time stochastic processes. (k1,k2)
  • Applying mathematical results of stochastic processes to solve related problems. (k3)
  • Analyzing, applying, and comparing different types of stochastic processes. (k3,k4)
  • Implementing stochastic processes, based on relevant features, to simulate new data and to verify/investigate properties of interest. (k3-k6)
  • Notion, typology, and applications of stochastic processes
  • Different types of stochastic processes
  • Properties of discrete and continuous time stochastic processes
  • Transition probabilities and matrices
  • Long term behavior (e.g., absorption, stationary distributions, etc.)
  • Simulation of stochastic processes
Criteria for evaluation Exam, presentation of solved homeworks.
Methods Lecture, discussion of homeworks.
Language English
Study material Wagner H. Skriptum Stochastische Prozesse und Zeitreihenanalyse

Bailey N.T.J. (1964). The Elements of Stochastic Processes

Changing subject? No
Corresponding lecture 5MSSPKV: KV Stochastische Prozesse und Zeitreihenmodellierung (4 ECTS)
On-site course
Maximum number of participants 40
Assignment procedure Assignment according to priority