4 citations · 7 across the 20 of their papers we have counts for
22 papers
Spike-based Belief Propagation in Nonlinear Dynamical Systems
Sepideh Adamiat, Hongye Wang, Wouter M. Kouw +1
This paper presents a Bayesian control framework that integrates spike-based dynamics with probabilistic inference for adaptive control. Bayesian inference is widely regarded as a…
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…
Expected free energy as an information constraint on the Bethe Lagrangian
Wouter M. Kouw
Active inference selects actions by minimising an expected free energy functional over predicted futures. However, adding an expectation over yet-unobserved outcomes means the free…
Expected Free Energy-based Informative Path Planning for Robotic Mars Exploration
Ajith Anil Meera, Pablo Lanillos, Wouter Kouw
An autonomous robot efficiently exploring an unknown environment, such as looking for water sources on Mars, faces two simultaneous demands: building an accurate information map wh…
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…
Composing Non-Conjugate Factor Graphs with Closed-Form Variational Inference
Mykola Lukashchuk, Kyrylo Yemets, Wouter M. Kouw +4
Stacking probabilistic building blocks into deeper architectures typically breaks closed-form inference. We show that closed-form inference can be preserved. We identify five facto…
Curvature-aware Expected Free Energy as an Acquisition Function for Bayesian Optimization
Ajith Anil Meera, Wouter Kouw
We propose an Expected Free Energy-based acquisition function for Bayesian optimization to solve the joint learning and optimization problem, i.e., optimize and learn the underlyin…
Gaussian Variational Inference with Non-Gaussian Factors for State Estimation: A UWB Localization Case Study
Andrew Stirling, Mykola Lukashchuk, Dmitry Bagaev +2
This letter extends the exactly sparse Gaussian variational inference (ESGVI) algorithm for state estimation in two complementary directions. First, ESGVI is generalized to operate…
Spike-Timing-Dependent Plasticity for Bernoulli Message Passing
Sepideh Adamiat, Wouter M. Kouw, Bert de Vries
Bayesian inference provides a principled framework for understanding brain function, while neural activity in the brain is inherently spike-based. This paper bridges these two pers…
A Spiking Neural Network Implementation of Gaussian Belief Propagation
Sepideh Adamiat, Wouter M. Kouw, Bert de Vries
Bayesian inference offers a principled account of information processing in natural agents. However, it remains an open question how neural mechanisms perform their abstract operat…
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…
Bayesian autoregression to optimize temporal Matérn kernel Gaussian process hyperparameters
Wouter M. Kouw
Gaussian processes are important models in the field of probabilistic numerics. We present a procedure for optimizing Matérn kernel temporal Gaussian processes with respect to the…
Online Bayesian system identification in multivariate autoregressive models via message passing
T. N. Nisslbeck, Wouter M. Kouw
We propose a recursive Bayesian estimation procedure for multivariate autoregressive models with exogenous inputs based on message passing in a factor graph. Unlike recursive least…
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…
Coupled autoregressive active inference agents for control of multi-joint dynamical systems
Tim N. Nisslbeck, Wouter M. Kouw
We propose an active inference agent to identify and control a mechanical system with multiple bodies connected by joints. This agent is constructed from multiple scalar autoregres…
Planning to avoid ambiguous states through Gaussian approximations to non-linear sensors in active inference agents
Wouter M. Kouw
In nature, active inference agents must learn how observations of the world represent the state of the agent. In engineering, the physics behind sensors is often known reasonably a…
Bayesian grey-box identification of nonlinear convection effects in heat transfer dynamics
Wouter M. Kouw, Caspar Gruijthuijsen, Lennart Blanken +2
We propose a computational procedure for identifying convection in heat transfer dynamics. The procedure is based on a Gaussian process latent force model, consisting of a white-bo…
Information-seeking polynomial NARX model-predictive control through expected free energy minimization
Wouter M. Kouw
We propose an adaptive model-predictive controller that balances driving the system to a goal state and seeking system observations that are informative with respect to the paramet…
Variational Bayes for robust radar single object tracking
Alp Sarı, Tak Kaneko, Lense H. M. Swaenen +1
We address object tracking by radar and the robustness of the current state-of-the-art methods to process outliers. The standard tracking algorithms extract detections from radar i…
Variational message passing for online polynomial NARMAX identification
Wouter Kouw, Albert Podusenko, Magnus Koudahl +1
We propose a variational Bayesian inference procedure for online nonlinear system identification. For each output observation, a set of parameter posterior distributions is updated…
Robust importance-weighted cross-validation under sample selection bias
Wouter M. Kouw, Jesse H. Krijthe, Marco Loog
Cross-validation under sample selection bias can, in principle, be done by importance-weighting the empirical risk. However, the importance-weighted risk estimator produces sub-opt…
MR Acquisition-Invariant Representation Learning
Wouter M. Kouw, Marco Loog, Lambertus W. Bartels +1
Voxelwise classification approaches are popular and effective methods for tissue quantification in brain magnetic resonance imaging (MRI) scans. However, generalization of these ap…