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.
Committee members: Clara Grazian, Matias Quiroz, Leah South, and Matt Moores
Meet the Bayesian Statistics Section Committee
David Frazier is a 2020 Discovery Early Career Researcher Award (DECRA) fellow in the Department of Econometrics and Business Statistics at Monash University. Frazier’s primary interest is statistical inference in computationally challenging inference problems. Frazier joined the Bayesian section of SSA as a committee member in 2019, and became co-chair of the committee in 2020.
Samantha Low-Choy is an applied statistician, with experience in government and academia. At Griffith University (GU), she convenes an annual program of stats training events balanced with advising across the university in all kinds of stats. Being at GU has nurtured her research that falls at the interface of Bayesian stats with qualitative research methods (incl conceptual models, expert elicitation, priors).
Matt Moores joined the Bayesian section of SSA in 2019 as a committee member. Matt is a Lecturer in Statistical Science at the University of Wollongong. His research interests include functional data analysis of spectroscopy as well as developing scalable Bayesian computation for intractable likelihoods.
Clara joined the Bayesian Section of SSA as a committee member in 2019. She is a Senior Lecturer in Statistics at the School of Mathematics and Statistics of the University of New South Wales, Sydney. Clara's research interests are in Bayesian clustering, copula models and spatio-temporal modelling. She likes working both on theoretical aspects and applications, in particular in environmental sciences, genomics and cybersecurity.
Matias joined the Bayesian Section of SSA as a committee member in 2020. Matias is a Lecturer in Statistics at the School of Mathematical and Physical Sciences in the University of Technology Sydney. He works on developing Bayesian methodology for estimating complex models using large datasets. Find out more about his research on his personal web page: www.matiasquiroz.com.
Leah South is a lecturer in the School of Mathematical Sciences at Queensland University of Technology with research interests in Bayesian computational statistics. Leah is particularly interested in variance reduction techniques, scalable Monte Carlo and approximate Bayesian computation. More information about Leah's research can be found on her Google Scholar profile: https://scholar.google.co.uk/citations?user=kXCyQlAAAAAJ&hl=en&oi=sra.
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