Friston’s Free Energy Principle Explained (part 1)
https://jaredtumiel.github.io/blog/2020/08/08/free-energy1.html
By Jared Tumiel. A friendly but rigorous guide to Karl Friston’s Free Energy Principle (FEP).
Covers:
- Bayesian inference
- ’Phenotype’ as a set of viable states
- Entropy and expected surprise
- Kullback-Leibler Divergence
- Recognition- and Generative-densities
- Derivation of the ‘free energy’ term
Goal of the series: Build toward implementing Active Inference under FEP in Python. Draws on statistical mechanics, reinforcement learning, neuroscience, predictive coding, information theory, variational inference, and embodied cognition.