• Corpus ID: 53777331

Geometry of Friston's active inference

@article{Biehl2018GeometryOF,
  title={Geometry of Friston's active inference},
  author={Martin Biehl},
  journal={ArXiv},
  year={2018},
  volume={abs/1811.08241}
}
We reconstruct Karl Friston's active inference and give a geometrical interpretation of it. 

Figures from this paper

References

SHOWING 1-5 OF 5 REFERENCES

Active Inference: A Process Theory

The fact that a gradient descent appears to be a valid description of neuronal activity means that variational free energy is a Lyapunov function for neuronal dynamics, which therefore conform to Hamilton’s principle of least action.

Expanding the Active Inference Landscape: More Intrinsic Motivations in the Perception-Action Loop

This article reconstructs the active inference approach, locate the original formulation within, and show how alternative intrinsic motivations can be used while keeping many of the original features intact, and illustrates the connection to universal reinforcement learning by means of the formalism.

Active inference and epistemic value

A formal treatment of choice behavior based on the premise that agents minimize the expected free energy of future outcomes and ad hoc softmax parameters become the expected (Bayes-optimal) precision of beliefs about, or confidence in, policies.

Pattern Recognition and Machine Learning

This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.

Variational Inference: A Review for Statisticians

Variational inference (VI), a method from machine learning that approximates probability densities through optimization, is reviewed and a variant that uses stochastic optimization to scale up to massive data is derived.