46 citations · 83 across the 14 of their papers we have counts for
14 papers
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…
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…
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…
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…
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…
Realising Synthetic Active Inference Agents, Part I: Epistemic Objectives and Graphical Specification Language
Magnus Koudahl, Thijs van de Laar, Bert de Vries
The Free Energy Principle (FEP) is a theoretical framework for describing how (intelligent) systems self-organise into coherent, stable structures by minimising a free energy funct…
Realising Synthetic Active Inference Agents, Part II: Variational Message Updates
Thijs van de Laar, Magnus Koudahl, Bert de Vries
The Free Energy Principle (FEP) describes (biological) agents as minimising a variational Free Energy (FE) with respect to a generative model of their environment. Active Inference…
Estimating the potential to prevent locally acquired HIV infections in a UNAIDS Fast-Track City, Amsterdam
Alexandra Blenkinsop, Mélodie Monod, Ard van Sighem +9
Amsterdam and other UNAIDS Fast-Track cities aim for zero new HIV infections. Utilising molecular and clinical data of the ATHENA observational HIV cohort, our primary aims are to…
Active Inference and Epistemic Value in Graphical Models
Thijs van de Laar, Magnus Koudahl, Bart van Erp +1
The Free Energy Principle (FEP) postulates that biological agents perceive and interact with their environment in order to minimize a Variational Free Energy (VFE) with respect to…
Chance-Constrained Active Inference
Thijs van de Laar, Ismail Senoz, Ayça Özçelikkale +1
Active Inference (ActInf) is an emerging theory that explains perception and action in biological agents, in terms of minimizing a free energy bound on Bayesian surprise. Goal-dire…
A Factor Graph Approach to Automated Design of Bayesian Signal Processing Algorithms
Marco Cox, Thijs van de Laar, Bert de Vries
The benefits of automating design cycles for Bayesian inference-based algorithms are becoming increasingly recognized by the machine learning community. As a result, interest in pr…
A Probabilistic Modeling Approach to Hearing Loss Compensation
Thijs van de Laar, Bert de Vries
Hearing Aid (HA) algorithms need to be tuned ("fitted") to match the impairment of each specific patient. The lack of a fundamental HA fitting theory is a strong contributing facto…