- Student can reason on stochastic experiments and use axioms of probability theory, events (especially dependence and independence) and their interpretations.
- Student can use combinatorial concepts.
- Student can use (a selection of) discrete and continuous random variables, and apply probability mass functions/probability density functions to calculate probabilistic concepts like expected value, variance, moments, CDF’s, marginal, conditional and joint probability mass/density functions.
- Student can determine the probability mass/density function of a function of one or more random variables.
- Student can apply Bayes’ rule.
- Student can determine covariance and correlation of two random variables
- Student can apply the central limit theorem.
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In probability theory you learn how to model events of chance and you learn some basic techniques to analyse those models. The following topics will be discussed: experiment, sample space and probability, basic combinatorial probability theory; conditional probability and independence; discrete and continuous random variables and their (joint) distributions and moments; the normal distribution and the central limit theorem.
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