Bayesian Analysis and Decision Theory Spring 2018

A graduate course in Bayesian Analysis and Decision Theory

This course is hosted by the research school ClimBEco and is open for anyone.

Registration is closed

The course has three physical meetings during spring 2018: 13-14 March, 20-21 March, 10-11 April badt_2018_schedule_15feb

This course is worth 5 credits –  Course Syllabus

Bayesian methods are used for inference, modelling of complex data, model calibration, and integration of multiple sources of information, combination of data with expert knowledge, risk and decision analysis. This is a course which mix theory and literature seminars with hands on exercises on Bayesian analysis to learn, predict, quantify uncertainty and make decisions.

The course will be open for anyone who has basic knowledge in probability theory or statistics. Experience in R will be an advantage for the hands on exercises. Methods will be presented in settings where they are applied using simple examples from the field of environmental and risk management. The purpose with the applied focus is to give students basic skills to use these methods, understand what they can be used for, and stimulate the student’s curiosity in learning more about them at a more foundational level.

Upon completion of the course, the student shall:

  • Be able to give an account of the principles behind Bayesian decision analysis,
  • Be able to give an account of the principles behind Bayesian and hierarchical modelling,
  • Be able to give examples of principles to quantify and treat uncertainty in quantitative assessments,
  • Be able to give an account of science theoretic arguments behind principles to quantify and treat uncertainty in knowledge production and decision making

The course consists of lectures, exercises, literature seminars and an individual project covered by three physical meetings and self studies. Case studies will be used cutting across the different parts.

Part I (March 13-14)

Ullrika Sahlin and Niklas Vareman

  • Introduction to decision analysis
  • Bayesian Belief Networks
  • The philosophy of uncertainty

Part II (March 20-21)

Johan Lindström

  • Bayesian Hierarchical Modelling in JAGS and INLA.

Part III (April 10-11)

Ullrika Sahlin, Niklas Vareman and Inari Helle (University of Helsinki)

  • Bayesian decision analysis
  • Decision making robust to uncertainty – in theory and practice
  • Limitations and extensions of Bayesian decision analysis

Part IV. Individual project

The purpose with the individual project is to give the students an opportunity to deepen or apply course content on something that are in their own interest. It can be purely theoretical or more hands-on modelling project. A suggestion is to do a decision analysis where one need to integrate data and expert judgment.

The schedule will be specified so that it is possible to travel back and forth from nearby cities over the day starting at 10 and ending at 15, with a possibility of Q&A between 15 and 16 the first day of every two day sessions.

In addition, course participants are invited to participate for free in the one day conference Bayes@Lund taking place in Lund on April 12th.

This course was given as a summer school 2015 – I have pasted the blog post we had back then

First week completed of the summer school Bayesian Analysis and Decision Theory

For questions contact me Ullrika — ullrika.sahlin [at]

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