Wouter Nuijten

BIASlab, TU Eindhoven

Lazy Dynamics

Eindhoven, The Netherlands

Hi, I’m Wouter. I’m a machine learning researcher and engineer working on Bayesian inference. In particular, I work on making Bayesian reasoning efficient, tractable, and scalable enough to run in real-time learning systems.

I’m in the final year of my PhD at BIASlab (Eindhoven University of Technology), where my research centers on variational realizations of Active Inference: framing Active Inference as variational inference so that planning and decision-making can be solved with the same scalable message passing machinery as state estimation and learning.

I build the tools I research with. I am a core developer of RxInfer.jl, an open-source Julia package for reactive Bayesian inference, where I designed and built GraphPPL.jl, the probabilistic programming language powering RxInfer’s model specification. I also created RxEnvironments.jl for designing reactive multi-agent environments, and Gears.jl for fine-grained scheduling in simulation and real-time systems. As Senior Machine Learning Engineer at Lazy Dynamics, I bring these methods to production in industrial settings.

In my free time, I play football and futsal at Totelos, and I really like to cook. When I’m not on the pitch or in the kitchen, I enjoy collecting records, watching films, and playing piano.

Contact

You can contact me at w.w.l.nuijten@tue.nl

selected publications

Active Inference
What Type of Inference Is Active Inference?

Wouter W. L. Nuijten, Mykola Lukashchuk, Thijs Laar, and 1 more author

Accepted at the Conference on Uncertainty in Artificial Intelligence (UAI) 2026 , Jun 2026

Abs DOI arXiv Bib

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 minimization can be written as Variational Free Energy (VFE) minimization on a generative model augmented with epistemic priors. We prove that the VFE of the augmented model can be rewritten as the VFE of the predictive model plus explicit entropy-correction terms, making the EFE contribution transparent. We then show that proper EFE-based planning requires combining these epistemic corrections with a planning correction that turns marginal inference into policy optimization, yielding a full variational characterization of EFE-based planning. This clarifies which corrections are needed for cross-entropy planning and for full EFE-based planning. The same entropy-corrected formulation leads to a detailed message-passing scheme for EFE-based planning together with simpler ablations. Experiments on three grid-world environments show that the planning correction already helps when observations are decisive, whereas the additional observation-side epistemic corrections matter most when observations are merely suggestive.
@misc{nuijten_what_2026, title = {What {{Type}} of {{Inference}} Is {{Active Inference}}?}, author = {Nuijten, Wouter W. L. and Lukashchuk, Mykola and van de Laar, Thijs and de Vries, Bert}, year = {2026}, month = jun, doi = {10.48550/arXiv.2606.04935}, }

Active Inference

Expected Free Energy-Based Planning as Variational Inference

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

Transactions on Machine Learning Research, 2026

Bib

@article{nuijten_expected_2026,
  title = {Expected Free Energy-Based Planning as Variational Inference},
  author = {Nuijten, Wouter W. L. and {van de Laar}, Thijs and {de Vries}, Bert},
  year = {2026},
  journal = {Transactions on Machine Learning Research},
  issn = {2835-8856},
}

Active Inference
A Message Passing Realization of Expected Free Energy Minimization

Wouter W.L. Nuijten, Mykola Lukashchuk, Thijs van de Laar, and 1 more author

In International Workshop on Active Inference, Oct 2025

Marr Best Technical Paper Award Abs DOI arXiv Bib

The paper was awarded the best technical paper award at the International Workshop on Active Inference 2025

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 minimization as Variational Free Energy minimization with epistemic priors, we transform a combinatorial search problem into a tractable inference problem solvable through standard variational techniques. Applying our message passing method to factorized state-space models enables efficient policy inference. We evaluate our method on environments with epistemic uncertainty: a stochastic gridworld and a partially observable Minigrid task. Agents using our approach consistently outperform conventional KL-control agents on these tasks, showing more robust planning and efficient exploration under uncertainty. In the stochastic gridworld environment, EFE-minimizing agents avoid risky paths, while in the partially observable minigrid setting, they conduct more systematic information-seeking. This approach bridges active inference theory with practical implementations, providing empirical evidence for the efficiency of epistemic priors in artificial agents.
@inproceedings{nuijten_message_2025, title = {A Message Passing Realization of Expected Free Energy Minimization}, author = {Nuijten, Wouter W.L. and Lukashchuk, Mykola and {van de Laar}, Thijs and {de Vries}, Bert}, booktitle = {International Workshop on Active Inference}, year = {2025}, month = oct, archiveprefix = {arXiv}, primaryclass = {cs}, doi = {10.48550/arXiv.2508.02197}, }