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删失回归数据的后验计算AI 解读

This article describes the computation of and sampling from the posterior density for censored regression problems with normal and generalized log-gamma errors. The data augmentation algorithm (Tanner and Wong 1987) is facilitated in the normal error case because of the form of the augmented posterior. In the generalized log-gamma context, this simplicity is absent. The work of Sweeting (1981) is used as a motivation to develop an importance sampling scheme to sample from an…

EM 算法的蒙特卡洛实现与简易数据增广算法AI 解读

The first part of this article presents the Monte Carlo implementation of the E step of the EM algorithm. Given the current guess to the maximizer of the posterior distribution, latent data patterns are generated from the conditional predictive distribution. The expected value of the augmented log-posterior is then updated as a mixture of augmented log-posteriors, mixed over the generated latent data patterns (multiple imputations). In the M step of the algorithm, this…

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通过数据增广计算最高后验密度区域的概率含量与边界AI 解读

This note considers the computation of the content and the boundary of the highest posterior density region in the context of data augmentation. The notion of the highest posterior density region and the data augmentation algorithm are reviewed. The sampling-based approach for calculating the content and the boundary of the highest posterior density region is then presented. The method is illustrated in the context of right-censored regression data.