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SUMMARY:Doing Bayes Seminar
DTSTART;TZID=Europe/London:20260804T140000
DTEND;TZID=Europe/London:20260804T150000
DTSTAMP:20260801T134531Z
UID:c4ba8507-1a8c-f111-8076-7ced8d3b2946
CREATED:20260730T132453Z
DESCRIPTION:Abstract: Bayesian statistics is now a standard tool in statis
 tician and machine learner’s toolkit. However\, the practical applicatio
 n of Bayesian models and methods can still present challenges and\, in tur
 n\, reveal interesting new research directions.\nIn this presentation\, I 
 will discuss some of our attempts to create software products that are fou
 nded on Bayesian methods. The first are interactive data platforms\, the A
 ustralian Cancer Atlas and AusEnHealth\, which have in turn inspired simil
 ar products. The research interests inspired by this work include federate
 d learning\, outlier detection and the development of vulnerability indice
 s. The second are interactive Bayesian network products\, which have inspi
 red careful consideration of priors for Bayesian models and the developmen
 t of LLMs to enhance user engagement.\nThe work that I will discuss has be
 en led by postgraduate students and postdoctoral researchers in the Centre
  for Data Science at QUT\, Australia. I will acknowledge them individually
  in my presentation.\n \nSelected References\nAustralian Cancer Atlas. htt
 ps://atlas.cancer.org.au/\nBon J\, A Bretherton\, K Buchhorn\, S Cramb\, C
  Drovandi\, C Hassan\, A Jenner\, H Mayfield\, J. McGree\, K Mengersen\, A
  Price\, R Salomone\, E Santos-Fernandez\, J Vercelloni & X Wang (2023) Be
 ing Bayesian in the 2020s: opportunities and challenges in the practice of
  modern applied Bayesian statistics. Philosophical Transactions. Series A.
 \nBretherton A\, Bon J\, Warne D\, Mengersen K\, Drovandi C\, (2026) A Pri
 ncipled Approach to Bayesian Transfer Learning\, Bayesian Analysis. To app
 ear.\nDavoudabi M\, et al. (2026) Federated learning moment propagation. I
 n preparation.\nHassan C (2024) Structured Models and Algorithms for Sensi
 tive Data. PhD Thesis\, Queensland University of Technology\, Australia.\n
 Johnson S\, J Toevs\, K Mengersen (2026) Using AI to make Complex Systems 
 less complex. Under review.\nPrice A\, M Rigby\, P Fiévez\, K Mengersen (
 2025) A spatial vulnerability index for environmental health. Ecological I
 ndicators.\nSantos-Fernandez E\, S Denman\, K Mengersen (2025) New Bayesia
 n and deep learning spatio-temporal models can reveal anomalies in sensor 
 data more effectively. Water Research.\nTuyl F\, R Gerlach\, K Mengersen (
 2024) On the Certainty of an Inductive Inference: The Binomial Case. Stati
 stical Science.
LAST-MODIFIED:20260730T132947Z
SPEAKER:Professor Kerrie Mengersen (Queensland University of Technology)
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