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 conduct an empirical analysis of goal prior specification in an expected free energy minimizing grid-world agent, investigating how specific qualities of the goal prior parameters affect the agent’s trajectories.

We present the design of an autoregressive active inference agent in the form of message passing on a factor graph.

We bridge the mathematical and the spike-based perspectives on brain functioning by designing spiking neural networks that simulate Bayesian inference through message passing for Bernoulli messages.

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.