Bayesian autoregressive adaptive refined descriptive sampling algorithm in the Monte Carlo simulation

This paper deals with the Monte Carlo Simulation in a Bayesian framework. It shows the importance of the use of Monte Carlo experiments through refined descriptive sampling within the autoregressive model X-t = ?Xt-1 + Y-t, where 0 < ? < 1 and the errors Yt are independent ran-dom variables following an exponential distribution of parameter ?. To achieve this, a Bayesian Autoregressive Adaptive Refined Descriptive Sampling (B2ARDS) algorithm is proposed to esti-mate the parameters ? and ? of such a model by a Bayesian method. We have used the same prior as the one already used by some authors, and computed their properties when the Nor-mality error assumption is released to an exponential distribution. The results show that B2ARDS algorithm provides accurate and efficient point estimates.