02407 Stochastic Processes
|Taught under open university|
Scope and form:
Lecture, exercises and computer work.
Duration of Course:
Type of assessment:
Not applicable together with:
General course objectives:
(Generally) To learn to formulate and analyse relatively simple dynamic probabilistic models. (Especially) To be acquainted with some models of this type which have proved practically usable.
|A student who has met the objectives of the course will be able to:|
- Differentiate between different types of stochastic processes, and determine which model class that is most relevant for a certain dynamic phenomenon
- Simulate realizations of a Markov- or renewal process
- Classify states of a Markov process and the process itself
- Determine invariant distributions in Markov processes
- Determine simple time varying transition probabilities in Markov processes
- Formulate and solve equations for time to absorption or expected time to absorption in Markov chains.
- Formulate discrete time Markov processes, which arise from different sampling techniques in continuous time processes
- Identify and analyze important special cases of Markov processes, e.g. birth and death processes and fundamental queueing systems
The topics are: Markov chains in discrete and continous time, renewal and Markov renewal processes.
The course is a good supplement for courses in statistics, operations research, time series analysis, image analysis, and control theory.
Green challenge participation:
Please contact the teacher for information on whether this course gives the student the opportunity to prepare a project that may participate in DTU´s Study Conference on sustainability, climate technology, and the environment (GRØN DYST). More information
|, 305, 119, (+45) 4525 3397,
|02 Department of Informatics and Mathematical Modeling|
Registration Sign up:
|queuing theory, stochastic processes, Markov chains, renewal processes|
April 27, 2012|
See course in DTU Course base