Course overview
In this course, you will get an introduction to probability theory, random variables and Markov processes. You will learn how to deal with modelling uncertainty, which has direct real-world application in areas such as genetics, finance and telecommunications. It is also the basis for many other areas in mathematical sciences, including statistics, modern optimisation methods and risk modelling.
Course learning outcomes
- Demonstrate an understanding of basic probability axioms; the rules and moments of discrete and continuous random variables.
- Derive the probability density function (PDF) of transformations of random variables and generate data from various distributions.
- Calculate probabilities and derive the marginal and conditional distributions of bivariate random variables.
- Find equilibrium probablity distributions.
- Calculate probabilities of absorption and expected hitting times for discrete time Markov chains with absorbing states.