Paper-Conference

We present a probabilistic numerical procedure for optimizing Matérn-class temporal Gaussian processes with respect to the kernel covariance function’s hyperparameters based on Bayesian autoregression.

We propose a recursive Bayesian estimation procedure for multivariate autoregressive models with exogenous inputs based on message passing in a factor graph

We propose an active inference agent, consisting of multiple scalar autoregressive model-based agents coupled by virtue of sharing memories, to learn and control a mechanical system with multiple bodies connected by joints.

We describe a Bayesian controller for a cart-pole system, where the entire computational process consists of online Bayesian inference executed by message passing in factor graphs.

For expected free energy minimization, we show that Gaussian approximations that are sensitive to the curvature of the measurement function, such as a second-order Taylor approximation, produce a state-dependent ambiguity term. This induces a preference over states, based on how accurately the state can be inferred from the observation.

We propose a computational procedure for identifying convection in heat transfer dynamics of motion control systems, using a Gaussian Process Latent Force Model.

We present a Bayesian identification procedure for polynomial NARMAX models based on message passing on a factor graph.

We address the robustness of the current state-of-the-art radar object trackers and propose a modification by modelling process noise with a distribution that has heavier tails than a Gaussian.

We propose a variational message passing algorithm for estimating coeffcients of a polynomial NARMAX model, which outperforms a recursive least-squares estimator.

Often, in practice, the data distribution at test time differs, to a smaller or larger extent, from that of the original training data. …