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concept:prior-eliminated-by-bayesian-model-reductionprior eliminated by Bayesian model reduction
Bayesian model reduction removes the structural partition prior once evidence shows it is unnecessary, yielding a post-dual agent.
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Papers (1)
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- Formal description of the transition to post-duality.
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- post-dual agent with unconstrained QRF deploymentsassociated_withAn agent that no longer imposes a fixed self-environment partition; its reference frame choices are free from dualistic constraints.
Related by similarity (8)
cosine ≥ 0.65 · no typed edgeEntities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.
- A method for simplifying models by removing parameters that don't contribute; applied to eliminate the self-boundary prior.
- Validation that BMR correctly identifies and prunes wrong connections in the likelihood mapping
- Formal mechanism by which the separation prior sigma is pruned from the generative model
- Bayesian model reduction formalises post-hoc hypothesis testing to simplify the generative model.claim0.794Definition of Bayesian model reduction, Section 9.1.
- Choosing among candidate models based on model evidence.
- The elimination of the self-environment partition prior is proposed as a formal model of the Buddhist realisation of emptiness.
- Source paper for Bayesian model reduction methodology used in structure learning
- Adding new states or parameters to the generative model if it increases model evidence, enabling concept learning.