| Course Description: |
This course introduces students to the theory of basic discrete and continuous time Markov processes and also Gaussian processes including Brownian motion and related processes. Topics include: Review of random variable characterisations, including cumulative distribution functions, probability density and mass functions, moment generating functions, joint, marginal and conditional distributions and conditional expectations and variances; Markov chains, including state-space decomposition, first-step analysis and determination of stationary and steady state distributions; Markov jump process theory, including embedded Markov chains, homogeneous and inhomogeneous Poisson processes and birth and death processes; Gaussian processes, including Brownian motion, geometric Brownian motion, Brownian bridges, integrated Brownian motion and White Noise. |