After taking this course
- You know the three goals of statistical inference, and you can demonstrate examples of each of these.
- You understand why in Bayesian inference some parameters cannot be computed exactly, and you know when and how to use approximate solutions like Markov chain Monte Carlo.
- You can model realistic problems using (hierarchies of) common probability distributions.
- You can use probabilistic programming tools like JAGS to fit a Bayesian model to real data, and draw conclusions based on this.
- You can compare different models and find the model that best explains your data (for the Bayesian definition of ‘best’).
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In science, but also in our day-to-day life, we have to come to terms with the fact that we can never know everything. That means that we are inherently uncertain about the way things are – whether it is recognizing who the person across the street is, or deciding which theoretical model best describes the results of our study. When we acknowledge this uncertainty, the claims we make become accompanied by probabilities. These can either reflect the relative number of times some event occurs (e.g. the number of times a coin comes up heads or tails, divided by the number of coin flips), or our subjective belief in the event (e.g. I believe this coin lands heads up is twice as often as tails up).
The first interpretation of probability is known as frequentist statistics, and this topic will be studied in course SOW-BKI138 . Here, we explore the second interpretation of probability, which is associated with Bayesian statistics. Bayesian statistics tells you how to describe and update your beliefs about the world using a simple yet powerful mathematical framework.
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Knowledge and skills as taught in Probability Theory *SOW-BKI137) and Calculus (SOW-BKI104) is necessary in order to successfully pass this course.
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The final grade for the Bayesian Statistics course consists of:
- 50% written exam: Your exam grade must be >= 5.5.
- 40% large assignment, and
- 10% weekly assignments.
Please note there is only a resit opportunity for the exam.
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The course grade is graded based on
1. Weekly exercises to be made in groups of 2. Average grade weighs 10% of final grade
2. Four large exercises, each weighing 10% of the final grade
3. An individual written exam, weighing 50% of the final grade
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