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全条件分布近似正态时的联合后验大样本近似AI 解读

Modern Bayesian statistical methods, such as Gibbs and Metropolis–Hastings sampling, were developed to liberate statisticians from the necessity of making large-sample assumptions and to facilitate the numerical approximation of problems that had previously been analytically intractable. Counter to this trend, we develop a method for constructing asymptotic joint posterior approximations based on models with k blocks of parameters and where the corresponding properly…

用 Pólya 树混合建模回归误差AI 解读

We model the error distribution in the standard linear model as a mixture of absolutely continuous Polya trees constrained to have median 0. By considering a mixture, we smooth out the partitioning effects of a simple Polya tree and the predictive error density has a derivative everywhere except 0. The error distribution is centered around a standard parametric family of distributions and thus may be viewed as a generalization of standard models in which important,…