Empirical Bayes

Statistical framework underlying perceptual inference and learning scheme; enables hierarchical models of sensory generation.

Neighborhood — ranked by edge-count

paper

method

dynamic expectation maximisation (DEM)
implements
A variational approach for dynamic Bayesian inversion of nonlinear causal models, named in this paper.

concept

Hierarchical Models of Sensory Generation
implements
Enable brain to construct dynamic, context-sensitive prior expectations; foundational to perceptual inference scheme.

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.

Variational Bayesmethod0.761
Mathematical framework for approximating posterior beliefs; converts exact Bayesian inference into optimization.
Bayes-optimal Behaviorconcept0.759
Behavior that minimizes expected free energy under the generative model, balancing exploration and exploitation in a principled manner.
Bayesian inversionconcept0.748
Online inversion of nonlinear dynamic causal models using DEM.
Bayesian Mechanicsframework0.744
Bayesian Inferencemethod0.733
Bayesian Cognitive Scienceframework0.724
Umbrella framework for brain-centric Bayesian approaches; contrasted with active inference
Variational Bayesian Inferenceframework0.722
A method for approximate Bayesian inference that optimizes a variational lower bound (ELBO) on log evidence.
Bayesian Surpriseconcept0.720
KL divergence between prior and posterior beliefs; used in visual salience.