BNMA 2025: 16th Annual Bayesian Network Modelling Association Conference
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.
- Submit your abstract (by 27th June)
- Register (opening 20th June)
- Workshop program (coming)
- Conference program (coming)
- Venue (coming)
- Bring your own BN session
- Travel grants
- Code of conduct and meeting rules
Keynote speakers
Dr Hannah Fraser
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 opens | 6th June |
| Call for abstracts closes | 27th June |
| Notification of abstract decision | 11th July |
| Registration opens | 20th June |
| Registration closes | 2nd September |
| Workshops | 30th September – 1st October |
| Conference | 2nd – 3rd October |
Costs
| In Australian dollars | Full member | Student | Non-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