MCMC and variational inference: evidence and tradeoffs
Research context
theory comparison · established · As of 2026-10-10
MCMC constructs a Markov chain with a desired stationary posterior and estimates integrals from correlated draws; convergence, mixing and effective sample size require checks.
Key knowledge
MCMC constructs a Markov chain with a desired stationary posterior and estimates integrals from correlated draws; convergence, mixing and effective sample size require checks. Variational inference solves an optimization problem in a restricted distribution family and introduces family/optimization bias. They address the same posterior but trade computation and different errors; a visually stable chain or a high ELBO alone does not validate uncertainty or remove prior/model dependence.
Conditions and scope
- For MCMC, irreducibility, invariance and convergence conditions concern the chosen transition kernel.
- Compare posterior quantities and diagnostics on the same model/data, and distinguish Monte Carlo error from approximation bias.
Sources · 1
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. 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
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- Postgraduate & research reference