Active inference relies on state-space models to describe the environments that agents sample with their actions. These actions lead to state changes intended to minimize future surprise. We show that surprise minimization relying on Bayesian inference can be achieved by filtering of the sufficient statistic time series of exponential family input distributions, and we propose the hierarchical Gaussian filter (HGF) as an appropriate, efficient, and scalable tool for active inference agents to achieve this.
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