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Advanced Stochastic Models (MAST90112)
Graduate courseworkPoints: 12.5Not available in 2017
You’re currently viewing the 2017 version of this subject
About this subject
Overview
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This subject develops the advanced methods of stochastic processes and discusses possible applications of the models discussed in the course. The topics will include: Levy processes, large deviation theory, point processes and stochastic networks.
Intended learning outcomes
After completing this subject students should gain:
- An appreciation of the range and utility of advanced statistical models and a sound knowledge of their analysis using modern statistical methods.
- An appreciation of the computational methods required to fit these models and the ability to interpret the results of an analysis.
Generic skills
In addition to learning specific skills that will assist students in their future careers in science, they will have the opportunity to develop generic skills that will assist them in any future career path. These include:
- problem-solving skills: the ability to engage with unfamiliar problems and identify relevant solution strategies;
- analytical skills: the ability to construct and express logical arguments and to work in abstract or general terms to increase the clarity and efficiency of analysis;
- collaborative skills: the ability to work in a team;
- time-management skills: the ability to meet regular deadlines while balancing competing commitments.
Last updated: 3 November 2022