This paper deals with the Monte Carlo Simulation in a Bayesian framework.It shows the impor-tance of the use of Monte Carlo experiments through refined descriptive sampling within the autoregressive model Xt=ρXt-1+Yt...This paper deals with the Monte Carlo Simulation in a Bayesian framework.It shows the impor-tance of the use of Monte Carlo experiments through refined descriptive sampling within the autoregressive model Xt=ρXt-1+Yt,where 0<ρ<1 and the errors Yt are independent ran-dom variables following an exponential distribution of parameterθ.To achieve this,a Bayesian Autoregressive Adaptive Refined Descriptive Sampling(B2ARDS)algorithm is proposed to esti-mate the parametersρandθof 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.展开更多
文摘This paper deals with the Monte Carlo Simulation in a Bayesian framework.It shows the impor-tance of the use of Monte Carlo experiments through refined descriptive sampling within the autoregressive model Xt=ρXt-1+Yt,where 0<ρ<1 and the errors Yt are independent ran-dom variables following an exponential distribution of parameterθ.To achieve this,a Bayesian Autoregressive Adaptive Refined Descriptive Sampling(B2ARDS)algorithm is proposed to esti-mate the parametersρandθof 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.
