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Sequentiality
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Mandatory sequentiality bound on the level of programme components
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Following programme components must have been included in your study programme in a previous education period
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Concepts of Probability and Statistics DL (3220)
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5.0 stptn |
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There is no data for this choice.
Change the language, year or choose another item in the dropdown list if it is available.
| Degree programme | | Study hours | Credits | P2 SBU | P2 SP | 2nd Chance Exam1 | Tolerance2 | Final grade3 | |
 | 1st year Master Bioinformatics - distance learning | Compulsory | 108 | 4,0 | 108 | 4,0 | Yes | No | Numerical |  |
| 1st year Master Biostatistics - distance learning | Compulsory | 108 | 4,0 | 108 | 4,0 | Yes | No | Numerical | |
| 1st year Master Data Science - distance learning | Compulsory | 108 | 4,0 | 108 | 4,0 | Yes | No | Numerical | |
| 1st year Master Quantitative Epidemiology - distance learning | Compulsory | 108 | 4,0 | 108 | 4,0 | Yes | No | Numerical | |
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| | | Learning outcomes |
- EC
| The student can handle scientific quantitative research questions, independently, effectively, creatively, and correctly using state-of-the-art design and analysis methodology and software. | | | - DC
| ... correctly using state-of-the-art analysis methodology. | | | - DC
| ... correctly using state-of-the-art software. | - EC
| The student is capable of acquiring new knowledge. | - EC
| The student can work in a multidisciplinary, intercultural, and international team. | - EC
| The student is an effective written and oral communicator, both within their own field as well as across disciplines. | | | - DC
| The student is an effective writer in their own field. |
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| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
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The student has knowledge of basic concepts from probability, statistics and distributions, as well as basic R programming and reporting skills.
The student has knowledge of statistical inference.
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This course will give a broad introduction to basic concepts of Bayesian analysis. Posterior summary measures, predictive distributions and Bayesian hypothesis tests will be contrasted with the frequentist approach. Simulation methods such as Markov chain Monte Carlo (MCMC) enable the Bayesian analysis. An introduction to algorithms like Gibbs sampling and Metropolis-Hastings will be explained and illustrated. Various medical case studies will be considered.
The student should be able to analyse relatively simple problems in a Bayesian way using OpenBugs software. The emphasis in this course is on theoretical background of basic concepts and practical data analysis
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Distance learning ✔
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Project ✔
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Response lecture ✔
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Period 2 Credits 4,00
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| Written evaluaton during teaching periode | 30 % |
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| Transfer of partial marks within the academic year | ✔ |
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| Conditions transfer of partial marks within the academic year | score greater or equal to 10/20 on project |
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| Off campus online evaluation/exam | ✔ |
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| For the full evaluation/exam | ✔ |
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| Additional information | At the exam, a summary of the course notes (5 A4 pages – two-sided) can be used. No other course material is allowed. |
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Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
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| Explanation (English) | Score for project is carried over to the retake exam (if score greater or equal to 10/20). Otherwise, a new project assignment has to be made. |
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| Compulsory textbooks (bookshop) |
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[Bayesian Biostatistics, Lesaffre, E. and Lawson, A., 2012, John Wiley & Sons],[],[],[],[9780470018231],[] |
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| Compulsory course material |
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All course material (handouts, video lectures and exercises) will be available on Blackboard.
The software R is used in this course. |
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1 Education, Examination and Legal Position Regulations art.12.2, section 2. |
| 2 Education, Examination and Legal Position Regulations art.16.9, section 2. |
3 Education, Examination and Legal Position Regulations art.15.1, section 3.
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| Legend |
| SBU : course load | SP : ECTS | N : Dutch | E : English |
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