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

Djoweyda Ghouil ,

Département de Mathématiques, Faculté des Sciences Exactes, Université de Tizi Ouzou, Tizi Ouzou, Algeria; Laboratoire de Mathématiques appliquées, Facult é des Sciences Exactes et Informatique, Université de Jijel, Jijel, Algeria

Megdouda Ourbih-Tari

Institute of Science, University Center of Tipaza, Tipaza, Algeria; Laboratoire de Mathématiques appliquées, Faculté des Sciences Exactes, Université de Bejaia, Bejaia, Algeria

Pages | Received 13 May. 2021, Accepted 02 Feb. 2023, Published online: 15 May. 2023,
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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 , where 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.


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To cite this article: Djoweyda Ghouil & Megdouda Ourbih-Tari (2023) Bayesian autoregressive adaptive refined descriptive sampling algorithm in the Monte Carlo simulation, Statistical Theory and Related Fields, 7:3, 177-187, DOI: 10.1080/24754269.2023.2180225

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