Bayesian Statistics

Chair: Chris Drovandi (

Assistant Chair: currently vacant (to apply for the position please contact the Chair)

The Bayesian Statistics section encourages the development and application of Bayesian methodology in a variety of fields, and inter-disciplinary collaboration. There has been growing interest in Bayesian methods, as it provides a statistical inference procedure with rigorous uncertainty quantification and a principled manner for incorporating prior information. Bayesian methods are becoming increasingly accessible through advancements in modern Bayesian computing and the availability of software packages with an expanding range of functionality.  More recently, Bayesian methods are being harnessed to improve and increase the capabilities of machine learning algorithms.

Throughout history, Bayesian methods have helped solve complex problems that were unsolvable by other means1,2:

  • During World War II, they were used to decode the German Enigma cipher and turn the tides of war
  • The US Navy used them to search for a missing H-bomb and locate Soviet subs
  • Insurance actuaries used them to set insurance rates
  • They were used in court to demonstrate the innocence of Captain Dreyfus
  • RAND corporation used them to assess the likelihood of a nuclear accident
  • Harvard and Chicago researchers used them to verify the authorship of the Federalist Papers
  • In medicine they were used to first establish the link between smoking and lung cancer

Currently, 300 SSA members (43% of all SSA members) are subscribed to the Bayesian Statistics section and the associated bayes-info mailing list, which we use to post updates regarding Bayesian events and news. The Section has organised and promoted various workshops, short courses and seminars held across Australia. The Section has also sponsored visits to Australia for internationally renowned Bayesian researchers to facilitate knowledge-gain and new collaborations.  Of particular note is the active Bayesian Discussion Group and book reading associated with the Queensland Branch.

To join the Bayesian statistics section and mailing list please log into your membership profile and tick the relevant box. 


1.      A History of Bayes’ Theorem. URL:

2.    McGrayne, S. The Theory That Would Not Die

Recommended Reading
  • Banerjee S, Carlin B, Gelfand A. Hierarchical modeling and analysis for spatial data: Chapman & Hall; 2004
  • Elliot P, Cuzick J, English D, Stern R. Geographical & Environmental Epidemiology. New York: Oxford University Press; 1992
  • Gelman A, Carlin JB, Stern HS, Rubin DB. Bayesian Data Analysis, 2nd edition: Chapman and Hall; 2003
  • Besag J, York J, Mollie A. Bayesian image restoration, with two applications in spatial statistics. Annals of the Institute of Statistical Mathematics. 1991;43(1):1-59
  • Kass RE, Raftery AE. Bayes factors. Journal of the American Statistical Association; June 1995; 90(430):773
  • Chib S. Marginal likelihood from the Gibbs output. Journal of the American Statistical Association; Dec 1995(90):432

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