35363 Stochastic Models
6cp; 4hpwRequisite(s): (35151 Introduction to Statistics OR 26134 Business Statistics OR 37151 Introduction to Statistics) AND (35101 Introduction to Linear Dynamical Systems OR 37131 Introduction to Linear Dynamical Systems)) OR ((33230 Mathematical Modelling 2 OR 33290 Statistics and Mathematics for Science)
These requisites may not apply to students in certain courses. See access conditions.
Description
Stochastic models allow many situations involving uncertainty to be analysed. This subject provides students with the knowledge required to use such models successfully in practice. Students acquire experience in using state-of-the-art commercial software for simulation, Markov decision process methods and various queuing models. Topics covered include Markov chains, Poisson processes, the birth-and-death process, and non-birth-and-death queuing models. The simulation component of the subject includes: pseudorandom number generation and corresponding statistical tests; evaluation of integrals using Monte Carlo simulation; generation of continuous and discrete random variables, including inverse transform technique, convolution method, and acceptance-rejection technique.
Typical availability
Autumn session, City campus
Detailed subject description.
Fee information
Information to assist with determining the applicable fee type can be found at Understanding fees.
- Commonwealth-supported students: view subject fees at Fees Search: Commonwealth-supported
- Postgraduate domestic fee-paying students: fees are charged according to the course enrolled in; refer to Domestic Fees Search: Postgraduate and Research
- International students: fees are charged according to the course enrolled in; refer to International Fees Search
- Subject EFTSL: 0.125