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	Gibbs Sampling za poređenje modela	Bayesian Model Averaging
Oblast	Bajesovska statistika	Bajesovska statistika
Porodica	Bayesian methods	Bayesian methods
Godina nastanka	1995	1999
Tvorac	Carlin and Chib	Hoeting, Madigan, Raftery & Volinsky
Tip	Bayesian model selection via MCMC	Bayesian model averaging
Temeljni izvor	Carlin, B. P. & Chib, S. (1995). Bayesian model choice via Markov chain Monte Carlo methods. Journal of the Royal Statistical Society, Series B, 57(3), 473-484.	Hoeting, J. A., Madigan, D., Raftery, A. E. & Volinsky, C. T. (1999). Bayesian Model Averaging: A Tutorial. Statistical Science, 14(4), 382–401.
Drugi nazivi	Gibbs-based model selection, MCMC model comparison via Gibbs, Bayesian model comparison with Gibbs sampling, Gibbs sampler model selection	BMA, Bayesian model combination, Bayesian Model Ortalaması (BMA)
Srodne	3	5
Sažetak	Gibbs sampling for model comparison is a Bayesian MCMC approach that simultaneously samples from the space of competing models and their parameters. By augmenting the Gibbs sampler with a discrete model-index variable, posterior model probabilities and Bayes factors are estimated from the resulting Markov chain without requiring separate runs per model.	Bayesian Model Averaging (BMA), formalised as a tutorial by Hoeting, Madigan, Raftery and Volinsky in 1999, addresses model uncertainty by averaging over all plausible model specifications rather than selecting a single best model. Each candidate model receives a posterior probability that reflects how well it fits the data given a prior, and predictions or coefficient estimates are formed as weighted averages across the entire model space. This approach reduces the bias and overconfidence that arise when a single selected model is treated as the true one.