Markov chain Monte Carlo
- Markov chain Monte Carlo
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Les méthodes MCMC (pour Markov chain Monte Carlo) sont une classe de méthodes d'échantillonnage à partir de distributions de probabilité. Ces méthodes se basent sur le parcours de chaînes de Markov qui ont pour lois stationnaires les distributions à échantillonner.
Certaines méthodes utilisent des marches aléatoires sur les chaînes de Markov (Algorithme de Metropolis-Hastings, Échantillonnage de Gibbs (en)), alors que d'autres algorithmes, plus complexes, introduisent des contraintes sur les parcours pour essayer d'accélérer la convergence (Monte Carlo Hybride, Surrelaxation successive)
Ces méthodes sont notamment appliquées dans le cadre de l'inférence bayésienne.
Voir aussi
D'autres échantillonnages de distribution
Bibliographie
(en) Christophe Andrieu, An Introduction to MCMC for Machine Learning, Kluwer Academic Publishers, 2003 [lire en ligne]
Portail des probabilités et des statistiques
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