- Lecturer
- Associate Professor K. Borovkov, Melbourne, Semester 2.
- Syllabus
- Basic concepts of the theory of stochastic processes. Finite dimensional
distributions and path properties. Convergence of stochastic processes and
Skorokhod theorem. Theory of martingales with applications. Processes with
independent increments. Markov processes. Applications to modelling
throughout the course.
- Prerequisites
- Probability Theory. Random variables; Sample Spaces; Probability; Moments;
Distribution Functions; Chebyshev's Inequality; Conditional Distributions;
Conditional Expectations; Characteristic Functions; Laplace Transforms;
Generating Functions.
Generic
skills
- References
References
- Ross, S.M. (1996) A Stochastic
Processes, Wiley.
- GRimmett, G.R. and Stirzaker, D.R. (1981) Probability and Random Processes,
Clarendon Press.
Back to list of elective components
Last updated: 30 October 2002.