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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.

非线性校准问题的贝叶斯方法AI 解读

In bioassays, enzyme assays, or radioimmunoassays the concentration response relationships are mostly nonlinear. Very often because of physical restrictions or for biological reasons, the prediction samples cannot be diluted and the determination of very low concentrations is of interest. Thus one cannot be restricted to the “almost linear” part of the response curve and standard linear calibration methods are not applicable. The nonlinear calibration problem is of great…