Friston & Penny (2011) — Post hoc Bayesian model selection

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

• Bayesian Model Selectionconcept0.844
  Choosing among candidate models based on model evidence.
• Bayesian model reduction formalises post-hoc hypothesis testing to simplify the generative model.claim0.820
  Definition of Bayesian model reduction, Section 9.1.
• Natural Selection as Bayesian Model Selectionconcept0.814
  Analogy between evolution (model selection) and Bayesian model reduction; but evolution is not curious or insightful
• Bayesian model reduction after 12 trials correctly removes off-diagonal (redundant) parameters from the likelihood array, recovering the true contingency structure.finding0.772
  Validation that BMR correctly identifies and prunes wrong connections in the likelihood mapping
• prior eliminated by Bayesian model reductionconcept0.762
  Bayesian model reduction removes the structural partition prior once evidence shows it is unnecessary, yielding a post-dual agent.
• model selectionconcept0.761
  Comparing models using log-evidence approximated by free energy.
• ByCS: Bayesian Example Selectionframework0.759
  Selection/weighting strategy for ICL demonstrations; in UCCT terms alters context posterior
• Bayesian Model Expansionmethod0.754
  Adding new states or parameters to the generative model if it increases model evidence, enabling concept learning.