Entropy Best Presentation Awards at the 44th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2025)—Winners Announced
We are pleased to announce the winners of the Best Presentation Awards, sponsored by Entropy (ISSN: 1099-4300), at the 44th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2025). This event was held from 14 to 19 December 2025, in Auckland, New Zealand. Congratulations to Vasudev Mittal and Johanna Moser!
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“Confronting the Cosmological Principle (CP)” by Vasudev Mittal
Cosmological Principle (CP) asserts that the universe is isotropic and homogeneous on large scales. It attributes the Cosmic Microwave Background (CMB) thermal dipole to our local peculiar motion, hence giving it the name of kinematic dipole. If this attribution is correct, then all sky surveys of other cosmological probes should show a similar dipole in their distribution throughout the sky. More than forty years ago, researchers postulated the presence of a number count dipole in source distribution (dubbed as the matter dipole) as a test for the CP. However, recent research has found a disagreement between the matter dipole and kinematic dipole, with claims reaching well over 5σ! In this talk, we test the CP by analysing an all-sky survey of quasars. Using Bayesian statistics, we show that a dipole aligned with the CMB is present. However, the amplitude of the dipole is still in tension with our expectations, bringing the validity of the cosmological principle into question. We also discuss some outstanding issues in the domain and possible methods to increase the sophistication of the number count dipole test.
“Parameter learning with physics-consistent Gaussian Processes” by Johanna Moser
The work discusses and compares methods of choosing a prior structure of the Gaussian Process such that the functions generated solve a chosen linear differential equation (PDE or ODE), or effectively speaking: ways of solving linear differential equations while accounting for uncertainty in data and parameters. We also present a python package ("PCGP") that implements those methods.