Posterior inference, priors and identifiability
Research context
postgraduate concept · established · As of 2026-10-10
Bayesian inference combines likelihood and prior into a posterior conditional on observed data.
Key knowledge
Bayesian inference combines likelihood and prior into a posterior conditional on observed data. If distinct parameter values induce the same observable law, data alone cannot identify them; a prior may select among them without adding observational identification. Credible probability statements concern the declared posterior and are not automatically frequentist coverage guarantees. Sensitivity to priors, model misspecification and computational approximation belong in the inference record.
Conditions and scope
- The prior is a stated probability law and the evidence normalizer is finite/nonzero.
- Population identifiability, finite-sample uncertainty and computational convergence are separate questions.
Sources · 2
References
Sources retain their recorded scope. A source link alone does not establish verification of every statement.
Evidence and review
Author checked this English statement, displayed equation where present and stated hypotheses against these source locations: §§2–4: KL/ELBO, mean field, coordinate ascent and MCMC comparisons; DAG factorization, d-separation, interventions, back-door adjustment and identifiability. The check is bounded to the recorded claims; no complete proof reconstruction, software proof replay or universal literature completeness is claimed.
2026-10-10 · Author source-body/statement/equation/domain check, with graph associations scoped individually. Separate independent content/source/inventory review is required before branch closure.
Entry details
Permanent link to this entry ↗- Record identity
- research-info-prior-posterior-identifiability
- Revision
- research.1
- Structured reference record
- Postgraduate & research reference