Introduction of Bayesian Inference (3562) |
| Credits: 4,0 | | Study load hours: 108 | Period: semester 2 (4sp)  |
| Language of instruction: English | | Exam contract: not possible |
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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 (9767)
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6.0 stptn | |
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The students 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 BUGS language (e.g. JAGS software). The emphasis in this course is on theoretical background of basic concepts and practical data analysis.
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| Compulsory textbooks (bookshop) |
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Textbook 1:
Bayesian Biostatistics, Lesaffre, E. and Lawson, A., 2012, John Wiley & Sons
ISBN: 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. R will be used as software in this course. |
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Lecture ✔
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Project ✔
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Response lecture ✔
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Small group session ✔
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Semester 2 (4,00sp)
| Evaluation method | |
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| Written evaluation during teaching period | 30 % |
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| Transfer of partial marks within the academic year | Yes, with condition |
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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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| Written exam | 70 % |
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| Multiple-choice questions | ✔ |
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| Use of study material during evaluation | ✔ |
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| Explanation (English) | During the exam, you may bring a summary of the course notes (maximum 5 A4 sheets - double-sided) and a calculator. No other material is allowed. |
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| Evaluation conditions (participation and/or pass) | ✔ |
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| Conditions | The student must submit the project in time. |
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| Consequences | If the student does not submit the project in time, then the final course of this course will receive code "N" (not all evaluation components completed). |
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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.
Score for the written exam cannot be carried over to the second chance
exam. |
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Learning outcomes | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
Master of Statistics and Data Science
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- 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 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. | - EC
| The student is capable of acquiring new knowledge. |
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Master of Teaching in Sciences and Technology
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- EC
| 5.4The Educational Master in Science and Technology as a domain expert: the Educational Master has advanced knowledge and understanding of the domain disciplines relevant to the subject didactics. |
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| Included in these programmes | Tolerance3 |
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N
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1st year Master Bioinformatics
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N
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1st year Master Biostatistics
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N
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1st year Master Data Science
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N
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1st year Quantitative Epidemiology
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N
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Exchange Programme Mathematics
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Y
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Exchange Programme Statistics
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Y
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Master of Teaching in Sciences and Technology - Engineering and Technology choice for subject didactics math
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Y
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1 Education, Examination and Legal Position Regulations art.12.2, section 2. |
| 2 Education, Examination and Legal Position Regulations art.15.1, section 3. |
3 Education, Examination and Legal Position Regulations art.16.9, section 2.
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