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贝叶斯空间模型中的联合留组交叉验证AI 解读

Cross-validation (CV) is a widely-used method of predictive assessment based on repeated model fits to different subsets of the available data. CV is applicable in a wide range of statistical settings. However, in cases where data are not exchangeable, the design of CV schemes should account for suspected correlation structures within the data. CV scheme designs include the selection of left-out blocks and the choice of scoring function for evaluating predictive performance.…

ARR2 先验：贝叶斯自回归模型的灵活预测先验AI 解读

We present the ARR2 prior, a joint prior over the auto-regressive components in Bayesian time-series models and their induced $R^2$. Compared to other priors designed for times-series models, the ARR2 prior allows for flexible and intuitive shrinkage. We derive the prior for pure auto-regressive models, and extend it to auto-regressive models with exogenous inputs, and state-space models. Through both simulations and real-world modelling exercises, we demonstrate the…

投影预测推断的进展AI 解读

The concepts of Bayesian prediction, model comparison, and model selection have developed significantly over the last decade. As a result, the Bayesian community has witnessed a rapid growth in theoretical and applied contributions to building and selecting predictive models. Projection predictive inference in particular has shown promise to this end, finding application across a broad range of fields. It is less prone to over-fitting than naïve selection based purely on…

以迭代筛选支持贝叶斯建模中的多宇宙分析AI 解读

When building statistical models for Bayesian data analysis tasks, required and optional iterative adjustments and different modelling choices can give rise to numerous candidate models. In particular, checks and evaluations throughout the modelling process can motivate changes to an existing model or the consideration of alternative models to ultimately obtain models of sufficient quality for the problem at hand. Additionally, failing to consider alternative models can lead…

含外生回归变量的贝叶斯自回归模型：交叉验证与模型选择AI 解读

Bayesian cross-validation (CV) is a popular method for predictive model assessment that is simple to implement and broadly applicable. A wide range of CV schemes is available for time series applications, including generic leave-one-out (LOO) and K-fold methods, as well as specialized approaches intended to deal with serial dependence such as leave-future-out (LFO), h-block, and hv-block. Existing large-sample results show that both specialized and generic methods are…

贝叶斯层级堆叠：某些模型在某些区域有用AI 解读

Stacking is a widely used model averaging technique that asymptotically yields optimal predictions among linear averages. We show that stacking is most effective when model predictive performance is heterogeneous in inputs, and we can further improve the stacked mixture with a hierarchical model. We generalize stacking to Bayesian hierarchical stacking. The model weights are varying as a function of data, partially-pooled, and inferred using Bayesian inference. We further…