| TAMS22 | Probability Theory and Bayesian Networks, 6 ECTS credits. /Sannolikhetsteori och bayesianska nätverk/ |
||
Prel. scheduled
hours: 56 |
|||
|
Area of Education: Science Subject area: Mathematics |
|||
| Advancement level
(A-D): C
|
|||
| Aim: Introduction to the techniques and algorithms of graphical modelling in engineering and to causal models in probability |
|||
| Prerequisites: (valid for students admitted to programmes within which the course is offered) A first course in probability theory, a first course in statistical inference Note: Admission requirements for non-programme students usually also include admission requirements for the programme and threshhold requirements for progression within the programme, or corresponding. |
|||
| Organisation: Lectures and computing laboratories |
|||
| Course contents: Uncertainty, causal networks and d-separation, rules of probability and conditional probability, Model building, learning, adaptation and tuning, belief updating, junction trees. |
|||
| Course literature: Finn V. Jensen, Bayesian Networks and Decision Graphs, Springer 2001. |
|||
| Examination: | |||
| One written examination Homework assignments |
3,5 p 0,5 p |
||
Course language is Swedish. Department offering the course: MAI. Director of Studies: Eva Enqvist Examiner: John Noble Link to the course homepage at the department Course Syllabus in Swedish |
|||
|
|
 |
|||||
| ||||||