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Active inference as a book category covers the theoretical and practical framework that describes perception, action, and learning as processes of probabilistic inference under the free-energy principle. Books in this genre explain how brains, organisms, and artificial agents form and update generative models of the world, minimize prediction error (or variational free energy), and select actions to fulfill expected states; they typically include topics such as Bayesian and variational inference, predictive coding, precision-weighting, planning-as-inference, and computational algorithms for implementation. Works range from mathematical foundations and computational models to empirical neuroscience, clinical applications (e.g., psychiatry, motor control), and robotics/AI demonstrations, and they often engage with philosophical implications and critiques. Intended audiences vary from advanced students and researchers seeking rigorous derivations to practitioners and interdisciplinary readers looking for conceptual overviews and applied case studies.

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