| 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
(G1, G2, A): A
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| Aim: The course gives an introduction to the analysis of causal networks. It discusses graphical modelling and algorithms for updating the probability distributions. The student should expect to acquire some basic knowledge of the theory and engineering applications of Bayesian networks. By the end of the course, the student will have:
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| Prerequisites: (valid for students admitted to programmes within which the course is offered) A first course in probability theory; TAMS35, TAMS07, or NMAB06 part 1, or equivalent. 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, the Bayesian Paradigm, the Dirichlet distribution, causal networks and d-separation, Bayesian Networks rules of probability and conditional probability, entering evidence, learning, adaptation and tuning, the moral graph, triangulation, junction trees, flow of messages along a junction tree. |
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| Course literature: Finn V. Jensen, Bayesian Networks and Decision Graphs, Springer 2001.(recommended). Timo Koski & John Noble: Twelve lectures on Bayesian Networks 2005, published by the institution. (required). |
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| Examination: | ||||
| One written examination Homework assignments |
3,5 p 0,5 p |
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5 ECTS 1 ECTS |
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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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