Effects of Burn-In and Thinning Methods on Iteration and Autocorelation in a Model System of Bayesian Structural Equation Models

27726

The flexibility of the Bayesian approach has recently led to a more common use of Monte Carlo Markov Chain (MCMC) methods in Structural Equation Modeling. Some important issues regarding the MCMC methods are autocorrelation between the samples, whether they converge to a posterior distribution or not, and the termination of the chain. In this study, the predicted values of the parameters were used, based on the data of a Bayesian Structural Equation Model obtained by Gibbs Sampling as one of the MCMC methods. Burn-in method was utilized for overcoming the effects of the original parameters on the predicted values and thinning method was used for the independence of these predictions. The effects of burn-in and thinning methods were evaluated and it is concluded that these methods were very effective on iteration and autocorrelation.

12

Bayesian Structural Equation Model , Thining , Burn , in , Iteration

Afyon Kocatepe University Journal Of Science an‎d Engineering

18

Afyon Kocatepe University Journal Of Science an‎d Engineering