(Mathematics: Probability theory)
M1. …explain and apply the use of elementary probability theory, such as combinatoric probability theory, conditional probability, independence;
M2. …explain and apply probability distributions of one or more random variables, (discrete) conditional probabilities, and compute expectation, variance, and correlation coefficient;
M3. …explain and apply basic discrete and continuous distributions, including binomial, geometric, Poisson, uniform, exponential and normal distributions.
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Sample space, event, axioms of Kolmogorov, combinatorial probability, conditional probability, Bayes` rule, independence, random variable, expectation, variance, standard deviation, density, (cumulative) distribution function and distributions: binomial, hypergeometric, geometric, Poisson, exponential, uniform and normal, joint and conditional distributions, correlation, the distribution of the sum and the mean of independent variables, the Central Limit Theorem and its applications such as the normal approximation of binomial probabilities.
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