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Articles

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

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Pages 177-187 | Received 13 May 2021, Accepted 02 Feb 2023, Published online: 15 May 2023
 

Abstract

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 Xt=ρXt1+Yt, where 0<ρ<1 and the errors Yt are independent random variables following an exponential distribution of parameter θ. To achieve this, a Bayesian Autoregressive Adaptive Refined Descriptive Sampling (B2ARDS) algorithm is proposed to estimate the parameters ρ and θ 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 Normality error assumption is released to an exponential distribution. The results show that B2ARDS algorithm provides accurate and efficient point estimates.

Disclosure statement

No potential conflict of interest was reported by the author(s).