ASSESSING CONVERGENCE OF THE MARKOV CHAIN MONTE CARLO METHOD IN MULTIVARIATE CASE | Science Publications

Journal of Mathematics and Statistics

Denismar Alves Nogueira¹  Daniel Furtado Ferreira¹  Eric Batista Ferreira¹  Thelma Safadi¹ 

关键词: Convergence Criterion;    Gibbs Sampler;    Bayesian Inference;    Simulation;   

DOI  :  10.3844/jmssp.2012.471.480

学科分类：社会科学、人文和艺术（综合）

来源: Science Publications

【 摘 要 】

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.

【 授权许可】

Unknown