Expert-elicitation method for non-parametric joint priors using normalizing flows

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.

本研究提出一种基于专家咨询的学习非参数联合先验分布的规范化流方法。规范化流是一类生成模型，它能够通过特殊的深度神经网络精确地、单步评估密度函数，并能捕捉复杂的密度函数。在先前提出的基于模拟的框架基础上，本研究对该方法进行了调整和扩展，以适应非参数联合先验。因此，该框架支持开发学习参数和非参数先验，以及模型参数的独立或联合先验的咨询方法。为了评估所提方法的表现，我们进行了四次模拟研究，并展示了一个包含诊断和附加评估工具的评估流程，以支持咨询过程中的每个决策阶段。