MaxEnt 2025 was held at the University of Auckland from December 14 – 19, 2025. For historical purposes, the programme can be found here. We also have a small collection of slides available below:
- Ali Mohammad-Djafari – Bayesian Physics Informed Neural Networks for Inverse problems (BPINN-IP) and Digital Twins for industrial and biological application
- Atiksh Sah – Modeling African Lion Movements and ecological corridor using MaxEnt and Grid Based clustering
- Brendon Brewer – Bayesian Hierarchical Models and the Maximum Entropy Principle
- Florent Leclerq – Counterfactual-informed adaptive MCMC with conditional normalising flows
- Geoff Nicholls – Bayesian Inference for the Hyperparameters of Generalised Bayesian Inference
- Geoffrey Wolfer – (Combinatorial) Characterization of Exponential Families of Lumpable Stochastic Matrices
- Huimin Qu – Unveiling Gravitational Lenses in Colour: A Bayesian Approach to Multi-band Inference
- Jason McEwen – High-dimensional uncertainty quantification with deep data-driven priors
- Johanna Moser – Parameter learning with physics-consistent Gaussian Processes
- John Skilling – Positive Monte Carlo: a nested sampling primer
- John Skilling – Our Symmetries
- Mahdi Nouraie – Bayesian Stability Selection
- Mali Land-Strykowski – Bayesian Tension Quantification of the Cosmic Dipole Anomaly (PPTX file)
- Richard Fuchs – aim-resolve: Automatic Identification and Modeling for Bayesian Radio Interferometric Imaging
- Ruiting Mao – Inference from Imperfection: Rapid Gravitational Wave Parameter Estimation with Data Gaps in LISA using conditional Flow Matching
- Siyang Li – Moment-generating Function for Bayesian Computation with an application in X-ray source intensity estimation