Modelling of real-life systems as Markov chains, including transient behaviour, limiting behaviour and classification of states, and using the exponential distribution and Poisson processes. Computational topics include generating and sampling random numbers, combinatorial objects and probability functions. Applications may include queueing systems, chemical kinetics, infectious diseases, and statistical physics. Prerequisite: STAT 270 and (MATH 232 or MATH 240), all with a minimum grade of C-. Strongly Recommended: Experience with a computing platform such as R, MATLAB, or Python. Quantitative.
Modelling of real-life systems as Markov chains, including transient behaviour, limiting behaviour and classification of states, and using the exponential distribution and Poisson processes. Computational topics include generating and sampling random numbers, combinatorial objects and probability functions. Applications may include queueing systems, chemical kinetics, infectious diseases, and statistical physics. Prerequisite: STAT 270 and (MATH 232 or MATH 240), all with a minimum grade of C-. Strongly Recommended: Experience with a computing platform such as R, MATLAB, or Python. Quantitative.
