Please read our student and staff community guidance on COVID-19
Skip to main content Skip to navigation

ST337 Bayesian Forecasting and Intervention

Lecturer(s): Dr Jeremie Houssineau

Important: If you decide to take ST337 you cannot then take ST405. Bear this in mind when planning your module selection. Recall: an integrated Masters student must take at least 120 CATS of level 4+ modules over their 3rd & 4thyears.

Prerequisite(s): Either ST218/219 Mathematical Statistics A&B or ST220 Introduction to Mathematical Statistics

This module runs in Term 2.

Rationale: Forecasting is a vital prerequisite to decision making. This course is concerned with the theory and practice of short-term forecasting, using both data and subjective information. The course focuses on Dynamic Linear Models (DLM). DLM’s are a class of Bayesian Forecasting Models which generalise linear regression models and static statistical linear models. The course offers a very powerful fundamental probabilistic approach to forecasting, controlling and learning about uncertain commercial, financial, economic, production, environmental and medical dynamic systems.


  • 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

The theory will be illustrated by real examples from industry, marketing, finance, government, agriculture etc.

Books: Printed course notes will be available.

  • Mike West & Jeff Harrison, “Bayesian Forecasting and Dynamic Models”, 1997 (2nd edn.) Springer - Verlag.
  • Andy Pole, Mike West & Jeff Harrison, “Applied Bayesian Forecasting and Time Series Analysis”, 1994 Chapman and Hall.

Assessment: 100% by 2-hour examination.

Examination Period: Summer