Course syllabus adopted 2021-02-12 by Head of Programme (or corresponding).
Overview
- Swedish nameStokastiska processer och Bayesiansk inferens
- CodeMVE550
- Credits7.5 Credits
- OwnerTKTEM
- Education cycleSecond-cycle
- Main field of studyMathematics
- DepartmentMATHEMATICAL SCIENCES
- GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail
Course round 1
- Teaching language English
- Application code 59125
- Block schedule
- Open for exchange studentsYes
Credit distribution
Module | Sp1 | Sp2 | Sp3 | Sp4 | Summer | Not Sp | Examination dates |
|---|---|---|---|---|---|---|---|
| 0118 Examination 6 c Grading: TH | 0 c | 6 c | 0 c | 0 c | 0 c | 0 c |
|
| 0218 Written and oral assignments 1.5 c Grading: UG | 0 c | 1.5 c | 0 c | 0 c | 0 c | 0 c |
In programmes
- MPDSC - DATA SCIENCE AND AI, MSC PROGR, Year 1 (compulsory)
- TKAUT - AUTOMATION AND MECHATRONICS ENGINEERING, Year 3 (elective)
- TKITE - SOFTWARE ENGINEERING, Year 3 (elective)
- TKTEM - ENGINEERING MATHEMATICS, Year 2 (compulsory)
Examiner
Petter Mostad- Professor, Applied Mathematics and Statistics, Mathematical Sciences
Eligibility
General entry requirements for Master's level (second cycle)Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.
Specific entry requirements
English 6 (or by other approved means with the equivalent proficiency level)Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.
Course specific prerequisites
A basic course in mathematical statisticsAim
Building on a first course in mathematical statistics, this course is meant to give knowledge about a larger inventory of stochastic models, in particular stochastic processes, and greater knowledge about Bayesian inference, in particular in the context of these models. Together, this knowledge should give a solid basis for practical application and prediction using stochastic processes in connection with data analysis, and for further studies in statistics and probability theory.Learning outcomes (after completion of the course the student should be able to)
After completion of the course the student should be able to use certain basic stochastic processes as models for real phenomena, and adapt these models using observed data. The student should be able to make predictions from the models, both using theoretical properties and using computer based simulation. The student should be able to make computer based inference using MCMC for certain simple models, and in general understand and apply the Bayesian inferential paradigm.
Content
Discrete time Markov chains. Branching processes. Basic principles for Bayesian inference, using discretization and conjugate priors. Hidden Markov models (HMM). Monte Carlo integration and Markov chain Monte Carlo (MCMC) simulation. Poisson processes. Time-continuous Markov chains. Introduction to Brownian motion.
Organisation
Lectures and excercise classes. Obligatory assignments.
Literature
Dobrow: Introduction to Stochastic Processes with R. (Available at Chalmers as e-book). Wiley 2016.
Compendium. Lecture Notes.Examination including compulsory elements
Written exam. Obligatory assignments.
The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers on educational support due to disability.