Probability Models (G1100)
Probability Models
Module G1100
Module details for 2022/23.
15 credits
FHEQ Level 6
Module Outline
Short revision of probability theory
Expectation and conditional expectation
Convergence of random variables, in particular laws of large numbers, moment generating functions, central limit theorem.
Stochastic processes in discrete time in particular Markov chains, including random walk, martingales in discrete time, Doob's optional stopping theorem, martingale convergence theorem.
Module learning outcomes
Setting up probability spaces, events and random variables to solve real-life probability problems.
Manipulating distributions, densities, sums of random variables, basic random processes and Markov chains with applications.
Understanding and using the Laws of Large Numbers and the Central Limit Theorem, with an eye to statistics and probability modelling.
Acquire and rediscover set-theoretical and calculus skills in the context of probabilistic manipulations.
Type | Timing | Weighting |
---|---|---|
Computer Based Exam | Semester 1 Assessment | 80.00% |
Coursework | 20.00% | |
Coursework components. Weighted as shown below. | ||
Portfolio | T1 Week 11 | 40.00% |
Problem Set | T1 Week 5 | 15.00% |
Problem Set | T1 Week 10 | 15.00% |
Problem Set | T1 Week 8 | 15.00% |
Problem Set | T1 Week 3 | 15.00% |
Timing
Submission deadlines may vary for different types of assignment/groups of students.
Weighting
Coursework components (if listed) total 100% of the overall coursework weighting value.
Term | Method | Duration | Week pattern |
---|---|---|---|
Autumn Semester | Lecture | 2 hours | 11111111111 |
Autumn Semester | Lecture | 1 hour | 11111111111 |
How to read the week pattern
The numbers indicate the weeks of the term and how many events take place each week.
Dr Vladislav Vysotskiy
Assess convenor
/profiles/406081
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