collaborators

5 papers

math.ST2026

Reformulation Invariance and the Axiomatic Foundations of Inference

Raphaël Trésor, Thijs van de Laar, Bert de Vries

Maximum entropy, Bayesian updating, and exponential-family estimation are all instances of a common inference principle: selecting the measure or distribution that minimizes a dive…

cs.AI2026

Expected Free Energy-based Planning as Variational Inference

Wouter W. L. Nuijten, Thijs van de Laar, Bert de Vries

Planning under uncertainty requires agents to balance goal achievement with information gathering. Active inference addresses this through the Expected Free Energy (EFE), a cost fu…

cs.SD2026

A Probabilistic Generative Model for Spectral Speech Enhancement

Marco Hidalgo-Araya, Raphaël Trésor, Bart Van Erp +3

Speech enhancement in hearing aids remains a difficult task in nonstationary acoustic environments, mainly because current signal processing algorithms rely on fixed, manually tune…

math.ST2025

Resolution of the Borel-Kolmogorov Paradox via the Maximum Entropy Principle

Raphaël Trésor, Mykola Lukashchuk

This paper presents a rigorous resolution of the Borel-Kolmogorov paradox using the Maximum Entropy Principle. We construct a metric-based framework for Bayesian inference that uni…

stat.ML2025

Expected Free Energy-based Planning as Variational Inference

Bert de Vries, Wouter Nuijten, Thijs van de Laar +13

We address the problem of planning under uncertainty, where an agent must choose actions that not only achieve desired outcomes but also reduce uncertainty. Traditional methods oft…