论文标题
贝休斯：用于计算样本量和贝叶斯保证的R包装
bayesassurance: An R package for calculating sample size and Bayesian assurance
论文作者
论文摘要
储层计算是预测湍流的有力工具，其简单的架构具有处理大型系统的计算效率。然而，其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间，然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用，该系统模拟了湍流的若干特征。我们表明，使用径向基函数作为非线性投影器，即使只有部分观测并且不知道控制方程，也能稳健地捕捉复杂的系统非线性。最后，我们表明，当测量稀疏、不完整且带有噪声，甚至控制方程变得不准确时，我们的网络仍然可以产生相当准确的预测，从而为实际湍流系统的无模型预测铺平了道路。
We present a bayesassurance R package that computes the Bayesian assurance under various settings characterized by different assumptions and objectives. The package offers a constructive set of simulation-based functions suitable for addressing a wide range of clinical trial study design problems. We provide a detailed description of the underlying framework embedded within each of the power and assurance functions and demonstrate their usage through a series of worked-out examples. Through these examples, we hope to corroborate the advantages that come with using a two-stage generalized structure. We also illustrate scenarios where the Bayesian assurance and frequentist power overlap, allowing the user to address both Bayesian and classical inference problems provided that the parameters are properly defined. All assurance-related functions included in this R package rely on a two-stage Bayesian method that assigns two distinct priors to evaluate the unconditional probability of observing a positive outcome, which in turn addresses subtle limitations that take place when using the standard single-prior approach.
