1 citations · 1 across the 13 of their papers we have counts for
13 papers
Renormalising Generative Models for Active Inference: Foundations, Derivations, and Verification
Karim Zaghw, Andrew Pashea, Marc Pritsch +3
Active inference offers a unified framework for perception, learning, and action, but scaling discrete active-inference models to rich spatial and temporal domains remains difficul…
A Factor Graph Approach to Scalable Multi-Output Gaussian Process Regression
Wouter W. L. Nuijten, Esther G. van Pelt, Albert Podusenko +2
Multi-output Gaussian process regression scales cubically in the number of observations times outputs, and dense kernel-matrix methods need bespoke handling whenever different outp…
Sophisticated Policies from Epistemic Priors
Wouter W. L. Nuijten, Bert de Vries
Sophisticated Inference is a variant of active inference often associated with recursive belief modeling and tree search. We argue that its central computational role is simpler: w…
What Type of Inference is Active Inference?
Wouter W. L. Nuijten, Mykola Lukashchuk, Thijs van de Laar +1
Active inference casts decision-making as inference, with the Expected Free Energy (EFE) unifying goal-directed and information-seeking behavior. Recent work showed that EFE minimi…
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…
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…
Active Inference is a Subtype of Variational Inference
Wouter W. L. Nuijten, Mykola Lukashchuk
Automated decision-making under uncertainty requires balancing exploitation and exploration. Classical methods treat these separately using heuristics, while Active Inference unifi…
Message passing-based inference in an autoregressive active inference agent
Wouter M. Kouw, Tim N. Nisslbeck, Wouter L. N. Nuijten
We present the design of an autoregressive active inference agent in the form of message passing on a factor graph. Expected free energy is derived and distributed across a plannin…
A Message Passing Realization of Expected Free Energy Minimization
Wouter W. L. Nuijten, Mykola Lukashchuk, Thijs van de Laar +1
We present a message passing approach to Expected Free Energy (EFE) minimization on factor graphs, based on the theory introduced in arXiv:2504.14898. By reformulating EFE minimiza…
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…
Reactive Environments for Active Inference Agents with RxEnvironments.jl
Wouter W. L. Nuijten, Bert de Vries
Active Inference is a framework that emphasizes the interaction between agents and their environment. While the framework has seen significant advancements in the development of ag…
Node Classification in Random Trees
Wouter W. L. Nuijten, Vlado Menkovski
We propose a method for the classification of objects that are structured as random trees. Our aim is to model a distribution over the node label assignments in settings where the…
Automating Model Comparison in Factor Graphs
Bart van Erp, Wouter W. L. Nuijten, Thijs van de Laar +1
Bayesian state and parameter estimation have been automated effectively in a variety of probabilistic programming languages. The process of model comparison on the other hand, whic…