Course syllabus adopted 2024-02-05 by Head of Programme (or corresponding).
Overview
- Swedish nameBeräkningsmetoder för Bayesiansk statistik
- CodeMVE188
- Credits7.5 Credits
- OwnerMPENM
- 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 20154
- Open for exchange studentsYes
Credit distribution
Module | Sp1 | Sp2 | Sp3 | Sp4 | Summer | Not Sp | Examination dates |
|---|---|---|---|---|---|---|---|
| 0124 Project 2 c Grading: UG | 2 c | ||||||
| 0224 Examination 5.5 c Grading: TH | 5.5 c |
|
In programmes
- MPDSC - DATA SCIENCE AND AI, MSC PROGR, Year 1 (elective)
- MPDSC - DATA SCIENCE AND AI, MSC PROGR, Year 2 (elective)
- MPENM - ENGINEERING MATHEMATICS AND COMPUTATIONAL SCIENCE, MSC PROGR, Year 1 (compulsory elective)
- MPENM - ENGINEERING MATHEMATICS AND COMPUTATIONAL SCIENCE, MSC PROGR, Year 2 (elective)
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
Basic skills in mathematical statistics.Basic skills in scientific programming (for example in Python, Matlab or R) as achieved by completing TMS150 "Stochastic Data Processing and Simulation".
Aim
In Bayesian statistical analysis and decision theory, calculating exact results is often intractable due to the complexity of the involved models and their parameter spaces. This course aims at equipping the student with practical and theoretical skills for utilizing computationally intensive methods to solve such tasks, in particular in the form of stochastic simulations.A special effort will be made to help the student to see the connections and interplay between statistical modeling and applied problem solving, as well as computational and theoretical aspects of the models.
Learning outcomes (after completion of the course the student should be able to)
On successful completion of the course the student will be able to- explain and apply a Bayesian approach to probability inference,
- implement important computational algorithms for Bayesian inference, for example Metropolis-Hastings MCMC,
- make independent and informed decisions about statistical modeling and
computational choices, - present his or her analysis in a structured and pedagogical way.
Content
- Models representing uncertain knowledge
- Constructing Bayesian Networks for modelling uncertainty
- Prediction using basic analytic and numerical methods and with the EM algorithm
- Prediction using sampling methods, for example Markov chain Monte Carlo (MCMC)
- Sequential models, including, e.g., particle filters
- Computation with graphical models
- Variational Bayes approximations
- Approximate Bayesian Computing (ABC)
- Decision theory
Organisation
Lectures and obligatory computer based hand-in assignments.Literature
Literature written for the course, as well as links to some supporting literature, will be available via the course Canvas page
Examination including compulsory elements
Compulsory computer based hand-in assignments. The grade will be based on a written examination at the end of the course.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.