This paper provides a comprehensive tutorial for Bayesian practitioners in pharmacometrics using Pumas workflows. We start by giving a brief motivation of Bayesian inference for pharmacometrics highlighting limitations in existing software that Pumas addresses. We then follow by a description of all the steps of a standard Bayesian workflow for pharmacometrics using code snippets and examples. This includes: model definition, prior selection, sampling from the posterior, prior and posterior simulations and predictions, counter-factual simulations and predictions, convergence diagnostics, visual predictive checks, and finally model comparison with cross-validation. Finally, the background and intuition behind many advanced concepts in Bayesian statistics are explained in simple language. This includes many important ideas and precautions that users need to keep in mind when performing Bayesian analysis. Many of the algorithms, codes, and ideas presented in this paper are highly applicable to clinical research and statistical learning at large but we chose to focus our discussions on pharmacometrics in this paper to have a narrower scope in mind and given the nature of Pumas as a software primarily for pharmacometricians.

翻译：本文为使用Pumas工作流程的药理学建模领域的贝叶斯实践者提供了一份全面的教程。我们首先简要阐述了贝叶斯推断在药理学建模中的应用动机，指出了现有软件的局限性，而Pumas能够解决这些问题。随后，我们通过代码片段和示例，详细描述了药理学建模中标准贝叶斯工作流程的所有步骤，包括：模型定义、先验选择、后验采样、先验与后验模拟及预测、反事实模拟与预测、收敛诊断、可视化预测检验，以及最终通过交叉验证进行模型比较。最后，我们用通俗易懂的语言解释了贝叶斯统计学中许多高级概念背后的背景和直观理解，包括用户在进行贝叶斯分析时需要注意的重要思想和防范措施。本文介绍的许多算法、代码和思想广泛适用于临床研究和统计学习，但鉴于Pumas主要面向药理学建模人员的软件特性，我们选择将讨论范围限定在药理学建模领域。