We propose an expert-elicitation method for learning non-parametric joint prior distributions using normalizing flows. Normalizing flows are a class of generative models that enable exact, single-step density evaluation and can capture complex density functions through specialized deep neural networks. Building on our previously introduced simulation-based framework, we adapt and extend the methodology to accommodate non-parametric joint priors. Our framework thus supports the development of elicitation methods for learning both parametric and non-parametric priors, as well as independent or joint priors for model parameters. To evaluate the performance of the proposed method, we perform four simulation studies and present an evaluation pipeline that incorporates diagnostics and additional evaluation tools to support decision-making at each stage of the elicitation process.

翻译：我们提出了一种利用归一化流学习非参数联合先验分布的专家启发式方法。归一化流是一类生成模型，能够实现精确的单步密度评估，并通过专门的深度神经网络捕获复杂的密度函数。在我们先前引入的基于模拟的框架基础上，我们调整并扩展了该方法以适应非参数联合先验。因此，我们的框架支持开发用于学习参数化和非参数化先验的启发式方法，以及模型参数的独立或联合先验。为了评估所提方法的性能，我们进行了四项模拟研究，并提出了一个评估流程，该流程整合了诊断工具和额外的评估工具，以支持在启发过程的每个阶段进行决策。