论文标题
生成模型：跨学科的观点
Generative Models: An Interdisciplinary Perspective
论文作者
论文摘要
通过将概念理论与观察到的数据联系起来，生成模型可以在复杂情况下支持推理。例如，他们在统计数据范围内和超越统计数据中都发挥了核心作用，为分子生物学，粒子物理学理论建立以及流行病学中的资源分配提供了基础。我们介绍了现代生成模型基础的概率和计算概念，然后分析如何用于为实验设计，迭代模型改进，拟合优点评估和基于代理的模拟提供信息。我们强调生成机制的模块化视图，并讨论如何在新问题环境中灵活地重组它们。我们在整个过程中提供实用的插图，并且可以在https://github.com/krisrs1128/generative_review上获得复制所有示例的代码。最后，我们观察到生成模型中的研究目前是如何在几个活动岛上分裂的，我们重点介绍了纪律十字路口的机会。
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.
