Module Descriptors
PROBABILITY MODELS
MATH40357
Key Facts
Faculty of Computing, Engineering and Sciences
Level 4
15 credits
Contact
Leader: Emily Raeburn
Email: E.Raeburn@staffs.ac.uk
Hours of Study
Scheduled Learning and Teaching Activities: 36
Independent Study Hours: 114
Total Learning Hours: 150
Assessment
- ASSIGNMENT weighted at 50%
- CLASS-TEST weighted at 50%
Module Details
Module Texts
For background reading only, e.g.
Grinstead & Snell, 1997. Introduction to Probability, AMS, ISBN: 0821807498
Sheldon M. Ross, 2003. Probability Models, Academic Press, ISBN: 0125980612
Grinstead & Snell, 1997. Introduction to Probability, AMS, ISBN: 0821807498
Sheldon M. Ross, 2003. Probability Models, Academic Press, ISBN: 0125980612
Module Special Admissions Requirements
Prior study of CE61014-4 Mathematics and Statistics for Computing Students or CE00819-4 Mathematics for Interactive Computing or equivalent
Module Resources
Excel/SPSS
Module Learning Strategies
Learning Strategies.
Students are required to commit to 150 learning hours of which 36 hours will consist of contact time. There will be 12 hours of lectures and 24 hours of tutorial/practical time. Lectures will provide students with a broad overview of the indicative content and the tutorial/practical sessions will allow students to practise the material covered in the lectures through problem solving.
Students are required to commit to 150 learning hours of which 36 hours will consist of contact time. There will be 12 hours of lectures and 24 hours of tutorial/practical time. Lectures will provide students with a broad overview of the indicative content and the tutorial/practical sessions will allow students to practise the material covered in the lectures through problem solving.
Module Indicative Content
Probability rules and Bayes' Theorem
Solve probability problems using standard statistical distributions such as Binomial, Poisson, Normal and Exponential.
Model univariate and bivariate discrete random variables using appropriate probability distributions.
Hypothesis testing including goodness of fit tests.
Solve probability problems using standard statistical distributions such as Binomial, Poisson, Normal and Exponential.
Model univariate and bivariate discrete random variables using appropriate probability distributions.
Hypothesis testing including goodness of fit tests.
Module Additional Assessment Details
An individual assessment involving the collection and analysis of data (Learning outcomes 1, 2 and 3).
Stage class test, 2 hours (Learning outcomes 1 and 2).