- 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.
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.