concept
active
concept:dirichlet-distributionDirichlet Distribution
Conjugate prior for categorical variables; used for beliefs about likelihood matrix A.
Neighborhood — ranked by edge-count
Papers (1)
paper
Concepts (1)
concept
- Concentration Parametersassociated_withSufficient statistics of Dirichlet priors over likelihood; accumulate as experience is gained; analogous to synaptic efficacy
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.
- Learning rule for updating Dirichlet beliefs about likelihood matrix A by adding outer products of observations and state estimates.
- In active inference, the distribution over goal states; here replaced by the learned self-prior rather than a hand-specified prior
- Probability distribution over discrete states or outcomes.
- Full distribution over tokens 0-9 at first generation step; contains more information than any single sampled token
- The distribution of latent representations produced by the model under unperturbed inputs
- Concept of self as extended and co-constituted by interactions, per Mahāyāna.
- Machine learning generalization when training and test distributions differ; linked to causal invariance.