By linking conceptual theories with observed data, generative models can support reasoning in complex situations. They have come to play a central role both within and beyond statistics, providing the basis for power analysis in molecular biology, theory building in particle physics, and resource allocation in epidemiology, for example. We introduce the probabilistic and computational concepts underlying modern generative models and then analyze how they can be used to inform experimental design, iterative model refinement, goodness-of-fit evaluation, and agent based simulation. We emphasize a modular view of generative mechanisms and discuss how they can be flexibly recombined in new problem contexts. We provide practical illustrations throughout, and code for reproducing all examples is available at https://github.com/krisrs1128/generative_review . Finally, we observe how research in generative models is currently split across several islands of activity, and we highlight opportunities lying at disciplinary intersections.

中文翻译：

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生成模型：跨学科的视角

通过将概念理论与观察到的数据联系起来，生成模型可以支持复杂情况下的推理。它们在统计学内外发挥着核心作用，例如为分子生物学中的功率分析、粒子物理学中的理论构建以及流行病学中的资源分配提供基础。我们介绍现代生成模型背后的概率和计算概念，然后分析如何使用它们来指导实验设计、迭代模型细化、拟合优度评估和基于代理的模拟。我们强调生成机制的模块化视图，并讨论如何在新的问题环境中灵活地重新组合它们。我们在全文中提供了实用的插图，并且可以在 https://github.com/krisrs1128/generative_review 上找到用于重现所有示例的代码。最后，我们观察了生成模型的研究目前如何分布在几个活动岛中，并强调了学科交叉领域的机会。