MCMC za usporedbu modela
MCMC za usporedbu modela koristi Markovljeve Monte Carlo algoritme za procjenu marginalnih vjerojatnosti i Bayesovih faktora potrebnih za formalnu usporedbu konkurentskih statističkih modela. Tehnike poput reverzibilnog-skoka MCMC (reversible-jump MCMC) i uzorkovanja mosta (bridge sampling) omogućuju istraživanje prostora modela različitih dimenzionalnosti, omogućujući potpuno Bayesovu selekciju i usrednjavanje modela.
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Method map
The neighbourhood of related methods — select a node to explore.
Izvori
- Green, P. J. (1995). Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 82(4), 711–732. DOI: 10.1093/biomet/82.4.711 ↗
- Meng, X.-L., & Wong, W. H. (1996). Simulating ratios of normalizing constants via a simple identity: A theoretical exploration. Statistica Sinica, 6(4), 831–860. link ↗
Kako citirati ovu stranicu
ScholarGate. (2026, June 3). Markov Chain Monte Carlo for Bayesian Model Comparison. ScholarGate. https://scholargate.app/hr/bayesian/mcmc-for-model-comparison
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Approximate Bayesian ComputationSimulacija↔ compare
- Bayesian Model AveragingBayesovska statistika↔ compare
- Gibbs uzorkovanjeBayesovska statistika↔ compare
- Hamiltonian Monte CarloBayesovska statistika↔ compare
- Markovova lančana Monte Carlo (MCMC)Bayesovska statistika↔ compare
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