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改善贝叶斯推断参数化的诊断图AI 解读

A major problem with the Bayesian analysis of statistical models is that the computation of posterior and predictive summaries typically requires the numerical evaluation of complicated multivariate integrals. The accuracy and efficiency of these computations are usually sensitive to the choice of parameterization for the statistical model. This paper describes a graphical method which can be used both for assessing a proposed parameterization and choosing a new improved…

用于改进贝叶斯推断参数化的诊断图AI 解读

A major problem with the Bayesian analysis of statistical models is that the computation of posterior and predictive summaries typically requires the numerical evaluation of complicated multivariate integrals. The accuracy and efficiency of these computations are usually sensitive to the choice of parameterization for the statistical model. This paper describes a graphical method which can be used both for assessing a proposed parameterization and choosing a new improved…

用 Gibbs 抽样展示正态数据模型的贝叶斯推断AI 解读

The use of the Gibbs sampler as a method for calculating Bayesian marginal posterior and predictive densities is reviewed and illustrated with a range of normal data models, including variance components, unordered and ordered means, hierarchical growth curves, and missing data in a crossover trial. In all cases the approach is straightforward to specify distributionally and to implement computationally, with output readily adapted for required inference summaries.