有些模型是有用的，但我们如何知道哪些有用？迈向统一的贝叶斯模型分类法
Some models are useful, but how do we know which ones? Towards a unified Bayesian model taxonomy
AI总结：
针对贝叶斯模型定义与评估问题，提出PAD分类法定义四类模型，并给出十个效用维度及决策树以全面评估模型。
摘要：
概率（贝叶斯）建模在几乎所有定量科学和工业领域中的应用激增。这一发展由多种因素共同推动，包括更好的概率估计算法、灵活的软件、计算能力的增强以及人们对概率学习益处的日益认识。然而，一个原则性的贝叶斯模型构建工作流程远未完善，许多挑战仍然存在。为了促进原则性贝叶斯工作流程的未来研究和应用，我们提出并回答了我们认为贝叶斯建模的两个基本问题，即（a）“什么是贝叶斯模型？”和（b）“什么构成一个好的贝叶斯模型？”。作为第一个问题的答案，我们提出了PAD模型分类法，它定义了四种基本的贝叶斯模型类型，每种类型代表所有（已知或未知）变量的假定联合分布（P）、后验逼近器（A）和训练数据（D）的某种组合。作为第二个问题的答案，我们提出了十个效用维度，根据这些维度我们可以整体评估贝叶斯模型，即（1）因果一致性，（2）参数可恢复性，（3）预测性能，（4）公平性，（5）结构保真度，（6）简约性，（7）可解释性，（8）收敛性，（9）估计速度，以及（10）鲁棒性。此外，我们提出了两个示例效用决策树，描述了根据驱动模型构建和测试的推断目标，效用之间的层级结构和权衡。
英文摘要：
Probabilistic (Bayesian) modeling has experienced a surge of applications in almost all quantitative sciences and industrial areas. This development is driven by a combination of several factors, including better probabilistic estimation algorithms, flexible software, increased computing power, and a growing awareness of the benefits of probabilistic learning. However, a principled Bayesian model building workflow is far from complete and many challenges remain. To aid future research and applications of a principled Bayesian workflow, we ask and provide answers for what we perceive as two fundamental questions of Bayesian modeling, namely (a) "What actually is a Bayesian model?" and (b) "What makes a good Bayesian model?". As an answer to the first question, we propose the PAD model taxonomy that defines four basic kinds of Bayesian models, each representing some combination of the assumed joint distribution of all (known or unknown) variables (P), a posterior approximator (A), and training data (D). As an answer to the second question, we propose ten utility dimensions according to which we can evaluate Bayesian models holistically, namely, (1) causal consistency, (2) parameter recoverability, (3) predictive performance, (4) fairness, (5) structural faithfulness, (6) parsimony, (7) interpretability, (8) convergence, (9) estimation speed, and (10) robustness. Further, we propose two example utility decision trees that describe hierarchies and trade-offs between utilities depending on the inferential goals that drive model building and testing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
