Bayesian Model Selection

Choosing among candidate models based on model evidence.

Neighborhood — ranked by edge-count

concept

Natural Selection as Bayesian Model Selection
related_to
Analogy between evolution (model selection) and Bayesian model reduction; but evolution is not curious or insightful
model selection
related_to
Comparing models using log-evidence approximated by free energy.
Active Inference, Curiosity and Insight (Friston et al., 2017)
uses
The primary source paper being extracted
Counterfactual Hypothesis
associated_with
Ability to entertain competing hypotheses within one inference engine; proposed hallmark of mindful 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.

Friston & Penny (2011) — Post hoc Bayesian model selectionconcept0.844
Source paper for Bayesian model reduction methodology used in structure learning
Bayesian model evidenceconcept0.844
The probability of sensory data under a generative model; negative log evidence is bounded by free energy.
Bayesian Model Expansionmethod0.842
Adding new states or parameters to the generative model if it increases model evidence, enabling concept learning.
ByCS: Bayesian Example Selectionframework0.827
Selection/weighting strategy for ICL demonstrations; in UCCT terms alters context posterior
Bayesian Model Averagingconcept0.813
Predictions formed by averaging over policy-specific beliefs, weighted by policy probabilities.
Bayesian model reduction (method)method0.803
A method for simplifying models by removing parameters that don't contribute; applied to eliminate the self-boundary prior.
Bayesian Model-Based Reinforcement Learningframework0.799
RL variant that maintains beliefs over environment model; compared to active inference using Thompson sampling.
Bayesian Model of Painframework0.798
Conceptualization of pain perception as inference over hidden nociceptive causes, from Eckert et al. 2022