Bayesian Statistics

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.

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.  


Chair: Chris Drovandi (

Committee members: David Frazier, Clara Grazian,  Sama Low-Choy , Matt Moores and  Sophie Zaloumis

Meet the Bayesian Statistics Section Committee (under construction).

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Further Information

Complex problems solved using Bayesian methods

Recommended Reading

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