在环境数据中应用MCMC对一种新的共轭先验正参数进行推理

F. Nascimento, Wires do Nascimento Moura
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引用次数: 1

摘要

先验分布的选择会对后验分布的结果产生很大的影响,从而影响参数的点估计和区间估计以及各自的预测密度。在参数为正的情况下,伽马分布是最常见的共轭分布,先于大量参数。Bourguignon提出加权林德利(WL)分布作为gamma族共轭参数之前的替代共轭。这项工作包括提出一种在p参数向量分布中对这些参数进行推理的一般方法。用两个例子说明了该方法,其中WL分布和γ分布都共轭到这些族。后验点的估计使用MCMC技术进行采样。与通常的先验分布相比,应用程序的结果显示了使用WL先验分布的优势。
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Inference using MCMC to a new conjugate prior to positive parameters, applied in environmental data
Abstract Choosing the prior distribution may have great impact on the result of the posterior distribution and, consequently, point and interval estimation of parameters and the respective predictive density. In situations where the parameters are positive, the gamma distribution is the most common as a conjugate prior to a wide family of parameters. Bourguignon presented the weighted Lindley (WL) distribution as an alternative conjugate prior to parameters conjugated from the gamma family. This work consists in presenting a general way to perform inference to this parameters in a p-parametric vector distribution. The method is illustrated with two cases where both the WL distribution and the gamma distribution are conjugated to these families. Estimation of posterior points is sampled using MCMC techniques. The results of the applications showed advantage on using the WL prior distribution, compared to results with the usual prior gamma distribution.
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