BNMA 2025: 16th Annual Bayesian Network Modelling Association Conference

BNMA 2025: 16th Annual Bayesian Network Modelling Association Conference
Melbourne, Australia
Workshops: September 30th – October 1st, 2025
Conference: October 2nd – 3rd, 2025

Call for Abstracts Opens!

We are excited to announce that the 16th Annual Conference of Bayesian Network Modelling Association (BNMA2025) will be hosted on Parkville campus at the University of Melbourne, and will contain a hybrid in-person/online component. For the theme of this year’s conference, our sights are set on Transparency and Reproducibility in modelling, and in particular, how we can support the rigorous and ethical use of BN modelling (including causal BNs and directed acyclic graphs, DAGs) for impactful decision making and research. Governments and international organisations are increasingly turning to modelling approaches to support policy and decision-making and have begun asking questions about accountability. This year’s theme provides an opportunity to show how BNs can address this, both directly and by example.

As usual, we are looking forward to hearing about traditional and innovative applications of BN models on any topic including environment, health, defence, commerce, education or culture. We would also love to continue expanding our horizons by hearing how BNs can be adapted to take advantage of advances in other techniques.

Keynote speakers

Dr Hannah Fraser

A person smiling at camera

AI-generated content may be incorrect.

Hannah Fraser is a past President of AIMOS (Association of Interdisciplinary Metaresearch and Open Science). She is a meta-researcher, ecologist and Bayesian Network enthusiast working as a Post-Doctoral Researcher at the University of Melbourne (studying meta-research), and the University of Queensland (attempting to conduct ecology research as reproducibly and transparently as possible). The dual role means that she spends time thinking about best practice for research rigor, reproducibility and transparency and sees the challenges of these practices when applied in context.

Talk: Achieving reproducibility and transparency for Bayesian Networks

There is a groundswell movement toward improving the reproducibility and transparency of research. Developing Bayesian Networks is complex and iterative, often involving rounds of stakeholder engagement. This talk will provide context to the need for reproducibility and transparency in science in general, and Bayesian Networks in particular. It will describe some of the difficulties in achieving reproducibility and transparency that are specific to Bayesian Network modelling and propose ways to overcome these.

Dr Martine J. Barons

Martine is a Reader in Statistics and the Director of the Applied Statistics & Risk Unit (AS&RU) the knowledge exchange hub within the University of Warwick Statistics Department. The remit of AS&RU is to encourage and co-ordinate external partnerships which enable the early applications of theoretical, methodological and algorithmic developments made in the department, university and mathematical sciences community.

Martine’s research is focused on decision making under uncertainty and structured expert judgement, with applications in household food security, pollination, digital archives and the energy sector. Martine spoke at COP26 opens in a new window on communicating Climate Risk. Martine has research collaborations with Melbourne University, Monash University, The National Archives and a number of organisations from Business, Industry & Government (BIG).

Talk: Reporting Standards for Bayesian Network Modelling

Reproducibility is a key measure of the veracity of a modelling result or finding. In other research areas, notably in medicine, reproducibility is supported by mandating the inclusion of an agreed set of details into every research publication, facilitating systematic reviews, transparency and reproducibility. Governments and international organisations are increasingly turning to modelling approaches in the development and decision-making for policy and have begun asking questions about accountability in model-based decision making. The ethical issues of relying on modelling that is biased, poorly constructed, constrained by heroic assumptions and not reproducible are multiplied when such models are used to underpin decisions impacting human and planetary well-being. Bayesian Network modelling is used in policy development and decision support across a wide range of domains. In light of the recent trend for governments and other organisations to demand accountability and transparency, we have compiled and tested a reporting checklist for Bayesian Network modelling which will bring the desirable level of transparency and reproducibility to enable models to support decision making and allow the robust comparison and combination of models. The use of this checklist would support the ethical use of Bayesian network modelling for impactful decision making and research.

Key dates

Call for abstracts opens6th June
Call for abstracts closes27th June
Notification of abstract decision11th July
Registration opens20th June
Registration closes2nd September
Workshops30th September – 1st October
Conference2nd – 3rd October

Costs

In Australian dollarsFull memberStudentNon-member
Membership fee$50$0
Conference (in person)$350$150$400
Conference 1 day (in person)$250$100$300
Conference (online)$25$25$100
Two-day workshops$250$100$300

Not yet a member? Register here