Domenico Laurenza
MCMC methods to approximate conditional predictive distributions
Castellanos, M. E.;
2006-01-01
Abstract
Sampling from conditional distributions is a problem often encountered in statistics when inferences are based on conditional distributions which are not of closed-form. Several Markov chain Monte Carlo (MCMC) algorithms to simulate from them are proposed. Potential problems are pointed out and some suitable modifications are suggested. Approximations based on conditioning sets are also explored. The issues are illustrated within a specific statistical tool for Bayesian model checking, and compared in an example. An example in frequentist conditional testing is also given.File | Dimensione | Formato | |
---|---|---|---|
2006-CSDA-BayarriCastellanosMorales.pdf Solo gestori archivio
Descrizione: Articolo principale
Tipologia: versione editoriale
Dimensione 542.72 kB
Formato Adobe PDF
|
542.72 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.