Use este identificador para citar ou linkar para este item: http://repositorio.ufla.br/jspui/handle/1/15334
Título : Assessing convergence of the Markov chain Monte Carlo method in multivariate case
Autor: Nogueira, Denismar Alves
Safadi, Thelma
Ferreira, Daniel Furtado
Ferreira, Eric Batista
Palavras-chave: Convergence criterion
Gibbs sampler
Bayesian inference
Markov Chain Monte Carlo
Critério de convergência
Amostra de Gibbs
Inferência bayesiana
Cadeia de Markov Monte Carlo
Publicador: Science Publications
Data da publicação: 2012
Referência: NOGUEIRA, D. A. et al. Assessing convergence of the Markov chain Monte Carlo method in multivariate case. Journal of Mathematics and Statistics, [S. l.], v. 8, n. 4, p. 471-480, 2012.
Abstract: The formal convergence diagnosis of the Markov Chain Monte Carlo (MCMC) is made using univariate and multivariate criteria. In 1998, a multivariate extension of the univariate criterion of multiple sequences was proposed. However, due to some problems of that multivariate criterion, an alternative form of calculation was proposed in addition to the two new alternatives for multivariate convergence criteria. In this study, two models were used, one related to time series with two interventions and ARMA (2, 2) error and another related to a trivariate normal distribution, considering three different cases for the covariance matrix. In both the cases, the Gibbs sampler and the proposed criteria to monitor the convergence were used. Results revealed the proposed criteria to be adequate, besides being easy to implement.
URI: http://thescipub.com/abstract/10.3844/jmssp.2012.471.480
repositorio.ufla.br/jspui/handle/1/15334
Idioma: en_US
Aparece nas coleções:DEX - Artigos publicados em periódicos

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