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- W4376891027 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=ρXt−1+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." @default.
- W4376891027 created "2023-05-18" @default.
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- W4376891027 date "2023-05-15" @default.
- W4376891027 modified "2023-09-26" @default.
- W4376891027 title "Bayesian autoregressive adaptive refined descriptive sampling algorithm in the Monte Carlo simulation" @default.
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- W4376891027 doi "https://doi.org/10.1080/24754269.2023.2180225" @default.
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