Bayes-optimal conditional inference

The ideal limit of self-supervised learning as modeling the true conditional distribution.

Neighborhood — ranked by edge-count

claim

concept

Simulation objective
implements
The objective of minimizing predictive error on a self-supervised distribution, leading to Bayes-optimal conditional inference.

cosine ≥ 0.65 · no typed edge

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

Bayes-optimal Behaviorconcept0.821
Behavior that minimizes expected free energy under the generative model, balancing exploration and exploitation in a principled manner.
Optimal Bayesian Designframework0.816
Selecting actions to maximize expected information gain.
Variational Bayesian Inferenceframework0.792
A method for approximate Bayesian inference that optimizes a variational lower bound (ELBO) on log evidence.
Active inference achieves Bayes-optimal behavior in non-stationary environments through online belief updating.hypothesis0.785
Tested via FrozenLake experiments; predicts superior performance when environment dynamics change.
Bayesian Inferencemethod0.783
If a system attains a general steady-state, it will appear to behave in a Bayes optimal fashion, both in terms of optimal Bayesian design (exploration) and Bayesian decision theory (exploitation).hypothesis0.780
Corollary 3 in Appendix B derived from steady-state assumptions.
Behavior prescribed by active inference dynamics is approximately Bayes-optimal.claim0.780
Process theory outcomes produce normatively sound decision-making.
Bayesian Decision Theoryframework0.771
Framework for maximizing expected utility under uncertainty.