| TAMS22 | Probability Theory and Bayesian Networks, 6 ECTS credits. /Sannolikhetsteori och bayesianska nätverk/ |
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Prel. scheduled
hours: 56 |
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Area of Education: Science Subject area: Mathematics |
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| Advancement level
(A-D): C
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| Aim: Introduction to the techniques and algorithms of graphical modelling in engineering and to causal models in probability |
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| 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. |
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| Organisation: Lectures and computing laboratories |
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| Course contents: Uncertainty, causal networks and d-separation, rules of probability and conditional probability, Model building, learning, adaptation and tuning, belief updating, junction trees. |
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| Course literature: Finn V. Jensen, Bayesian Networks and Decision Graphs, Springer 2001. Timo Koski & John Noble: Twelve lectures on Bayesian Networks 2005, published of the institution. |
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| Examination: | |||
| One written examination Homework assignments |
3,5 p 0,5 p |
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Course language is Swedish/under certain circumstances the course might be offered in English. Department offering the course: MAI. Director of Studies: Eva Enqvist Examiner: John Noble Course Syllabus in Swedish |
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