Introductory description

This module is available for students on a course where it is a listed option and as an Unusual Option to students who have completed the prerequisite modules.

Statistics Students:

• ST218 Mathematical Statistics A AND ST219 Mathematical Statistics B; or
• ST228 Mathematical Methods for Statistics and Probability, and ST229 Probability for Mathematical Statistics, and ST230 Mathematical Statistics.

• Non-Statistics Students:
  • ST104 Statistical Laboratory and ST220 Introduction to Mathematical Statistics; or,
  • ST121 Statistical Laboratory. and ST232/ST233 Introduction to Mathematical Statistics.

Module web page

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

• State space modelling
• Bayesian updating of beliefs
• Specifying Dynamic Linear Models
• Updating Dynamic Linear Models, forecasting
• Building Dynamic Linear Models, accommodating external information
• ARIMA models, stationarity

Learning outcomes

By the end of the module, students should be able to:

• Acquire an appreciation of forecasting recurrences and be able to calculate these for special cases.
• Know how to select an appropriate model in simple scenarios.
• Have an acquaintance with the most useful models in the class of DLMs for statistical forecasting in a business environment.
• Know how to intervene in these processes in the light of external information.
• Have an appreciation of diagnostic methods and estimation techniques for this model class.

Indicative reading list

View reading list on Talis Aspire

Subject specific skills

• Demonstrate facility with rigorous statistical methods.

• Evaluate, select and apply appropriate mathematical and/or statistical techniques.

• Demonstrate knowledge of and facility with formal statistical concepts, both explicitly and by applying them to the solution of mathematical problems.

• Create structured and coherent arguments communicating them in oral/written form. 

• Construct logical arguments with clear identification of assumptions and conclusions.

• Reason critically, carefully, and logically.

Transferable skills

• Problem solving: Use rational and logical reasoning to deduce appropriate and well-reasoned conclusions. Retain an open mind, optimistic of finding solutions, thinking laterally and creatively to look beyond the obvious. Know how to learn from failure.

• Self awareness: Reflect on learning, seeking feedback on and evaluating personal practices, strengths and opportunities for personal growth.

• Communication: Present arguments, knowledge and ideas, in a range of formats.

• Professionalism: Prepared to operate autonomously. Aware of how to be efficient and resilient. Manage priorities and time. Self-motivated, setting and achieving goals, prioritising tasks.