A step-by-step tutorial on active inference and its application to empirical data

DOI10.1016/j.jmp.2021.102632zbMath1484.91352OpenAlexW4226023986MaRDI QIDQ2116075

Christopher J. Whyte, Karl J. Friston, Ryan N. Smith

Publication date: 16 March 2022

Published in: Journal of Mathematical Psychology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmp.2021.102632

zbMATH Keywords

learningBayesian inferencemachine learningdecision-makingcomputational neuroscienceactive inference

Mathematics Subject Classification ID

Markov and semi-Markov decision processes (90C40) Cognitive psychology (91E10) Psychophysics and psychophysiology; perception (91E30)

Related Items (2)

The free energy principle made simpler but not too simple ⋮ Reward Maximization Through Discrete Active Inference

Uses Software

• PRMLT

Cites Work

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• Whence the Expected Free Energy?
• Active Inference: Demystified and Compared
• Realizing Active Inference in Variational Message Passing: The Outcome-Blind Certainty Seeker
• The Discrete and Continuous Brain: From Decisions to Movement—And Back Again
• Active Inference: A Process Theory
• Active Inference, Curiosity and Insight
• An Essentially Complete Class of Admissible Decision Functions

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