| Language of instruction : English |
| Exam contract: not possible |
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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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Introduction to Bayesian Inference DL (3579)
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4.0 stptn |
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| Degree programme | | Study hours | Credits | P1 SBU | P1 SP | 2nd Chance Exam1 | Tolerance2 | Final grade3 | |
 | second year Master Biostatistics - distance learning | Compulsory | 81 | 3,0 | 81 | 3,0 | Yes | Yes | 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 critically appraise methodology and challenge proposals for and reported results of data analysis. | - 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 of Bayesian inference.
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Bayesian methods, statistical modeling
This course is the successor to the course Introduction to Bayesian Inference. Both courses are based on the book Bayesian Biostatistics of Lesaffre and Lawson (2012, John Wiley & Sons). In the course Concepts of Bayesian Inference some basic Bayesian principles have been introduced and it was taught how to use WinBUGS/OpenBUGS/Jags/Nimble. These skills will be assumed in the present course. New Bayesian topics will be introduced but also the topics introduced in the previous course will be further developed. An example of the latter is that we will explore the role of the prior distribution in more detail, but also that the MCMC techniques will be explained in more technical detail. In this course, Bayesian methods for model selection and model criticism will be treated. In addition, more complex statistical analyses will be tackled e.g. Bayesian approaches for missing data, smoothing, survival analysis, etc.
Contents
Exploring the prior distribution
- The conjugate prior distribution: derivation, further examples and semi-conjugacy
- Expressing ignorance: the noninformative prior, the vague prior, Jeffreys prior, improper priors
- Informative priors: data-based priors, elicitation of prior knowledge, archetypal priors, prior distributions of regression models, modeling priors
Alternatives to Markov Chain Monte Carlo techniques
- INLA
- Hamiltonian Monte Carlo Methods
Hierarchical models
- Poisson-gamma hierarchical model
- Gaussian hierarchical model
- Comparison full Bayesian approach with empirical Bayesian approach
- Bayesian mixed models: linear, generalized linear and non-linear models
- Miscellaneous topics: choice of level 2 variance prior, propriety of posterior, comparison with frequentist approaches
Model building and assessment
- Bayes factor and variants, e.g. pseudo-Bayes factor
- Model selection based on predictive loss functions
- Residual analysis
- Sensitivity analysis
- Posterior predictive checks
- Model expansion techniques
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Collective feedback moment ✔
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Distance learning ✔
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Project ✔
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Period 1 Credits 3,00
| Evaluation method | |
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| Other evaluation method during teaching period | 50 % |
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| Transfer of partial marks within the academic year | ✔ |
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| Off campus online evaluation/exam | ✔ |
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| For the full evaluation/exam | ✔ |
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| Use of study material during evaluation | ✔ |
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| Explanation (English) | Slides and notes |
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| Additional information | The maximum score on the group project is 10. The maximum score on the individual oral exam is 10.
When the student scores less than 5 on the individual oral exam, then one point will be subtracted from the global (group project + individual oral exam) score of the course. When the student scores less than 2,5 on the individual oral exam, then two points will be subtracted from the global score. |
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Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
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| Previously purchased compulsory textbooks |
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Bayesian Biostatistics,E. Lesaffre and A. Lawson,1st,John Wiley & Sons, 2012,9780470018231,for course "Introduction to Bayesian Inference" |
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| Compulsory course material |
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Medical papers on the use of Bayesian methods in clinical trials and epidemiology. For this one needs to consult the scientific literature
Course notes "Bayesian Data Analysis II".
Lesaffre, E. and Lawson, A. Bayesian Biostatistics, John Wiley & Sons, 2012 The course notes are strongly linked to the book and further explanations of topics treated during class can be found in the book.
The R software will be used in this course. |
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| second year Master Bioinformatics - distance learning | Optional | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
| second year Data Science - distance learning | Optional | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
| second year Quantitative Epidemiology - distance learning | Optional | 81 | 3,0 | 81 | 3,0 | Yes | Yes | 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 critically appraise methodology and challenge proposals for and reported results of data analysis. | - 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 = learning outcomes DC = partial outcomes BC = evaluation criteria |
|
|
The student has knowledge of basic concepts of Bayesian inference.
|
|
|
|
Bayesian methods, statistical modeling
This course is the successor to the course Introduction to Bayesian Inference. Both courses are based on the book Bayesian Biostatistics of Lesaffre and Lawson (2012, John Wiley & Sons). In the course Concepts of Bayesian Inference some basic Bayesian principles have been introduced and it was taught how to use WinBUGS/OpenBUGS/Jags/Nimble. These skills will be assumed in the present course. New Bayesian topics will be introduced but also the topics introduced in the previous course will be further developed. An example of the latter is that we will explore the role of the prior distribution in more detail, but also that the MCMC techniques will be explained in more technical detail. In this course, Bayesian methods for model selection and model criticism will be treated. In addition, more complex statistical analyses will be tackled e.g. Bayesian approaches for missing data, smoothing, survival analysis, etc.
Contents
Exploring the prior distribution
- The conjugate prior distribution: derivation, further examples and semi-conjugacy
- Expressing ignorance: the noninformative prior, the vague prior, Jeffreys prior, improper priors
- Informative priors: data-based priors, elicitation of prior knowledge, archetypal priors, prior distributions of regression models, modeling priors
Alternatives to Markov Chain Monte Carlo techniques
- INLA
- Hamiltonian Monte Carlo Methods
Hierarchical models
- Poisson-gamma hierarchical model
- Gaussian hierarchical model
- Comparison full Bayesian approach with empirical Bayesian approach
- Bayesian mixed models: linear, generalized linear and non-linear models
- Miscellaneous topics: choice of level 2 variance prior, propriety of posterior, comparison with frequentist approaches
Model building and assessment
- Bayes factor and variants, e.g. pseudo-Bayes factor
- Model selection based on predictive loss functions
- Residual analysis
- Sensitivity analysis
- Posterior predictive checks
- Model expansion techniques
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|
|
|
|
|
|
|
|
Collective feedback moment ✔
|
|
|
|
Distance learning ✔
|
|
|
|
Project ✔
|
|
|
|
Period 1 Credits 3,00
| Evaluation method | |
|
| Other evaluation method during teaching period | 50 % |
|
|
| Transfer of partial marks within the academic year | ✔ |
|
|
|
|
|
|
| Off campus online evaluation/exam | ✔ |
|
| For the full evaluation/exam | ✔ |
|
|
|
| Additional information | The maximum score on the group project is 10. The maximum score on the individual oral exam is 10.
When the student scores less than 5 on the individual oral exam, then one point will be subtracted from the global (group project + individual oral exam) score of the course. When the student scores less than 2,5 on the individual oral exam, then two points will be subtracted from the global score. |
|
Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
|
|
|
| Previously purchased compulsory textbooks |
| |
Bayesian Biostatistics,E. Lesaffre and A. Lawson,1st,John Wiley & Sons, 2012,9780470018231,for course "Introduction to Bayesian Inference" |
|
|
| Compulsory course material |
| |
Medical papers on the use of Bayesian methods in clinical trials and epidemiology. For this one needs to consult the scientific literature
Course notes "Bayesian Data Analysis II".
Lesaffre, E. and Lawson, A. Bayesian Biostatistics, John Wiley & Sons, 2012 The course notes are strongly linked to the book and further explanations of topics treated during class can be found in the book.
The R software will be 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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