A hierarchical Bayesian factor model for multivariate spatially and temporally correlated data is proposed. This method searches scores incorporating a dependence within observations due to both geographical temporal structure it an extension of proposed by Mezzetti (2012) using the results separable covariance matrix spatial panel as in Leorato (2016). Gibbs sampling algorithm implemented samp...

We develop Bayesian model selection via fractional Bayes factors to simultaneously assess spatial dependence and select regressors in Gaussian hierarchical models with intrinsic conditional autoregressive (ICAR) random effects. Selection of covariates structure is difficult, as confounding creates a tension between fixed Researchers have commonly performed separately for effects models. Simulta...

Latent Gaussian models are flexible models that are applied in several statistical applications. When posterior marginals or full conditional distributions in hierarchical Bayesian inference from these models are not available in closed form, Markov chain Monte Carlo methods are implemented. The component dependence of the latent field usually causes increase in computational time and divergenc...