标题： 整合专家知识与递归贝叶斯推理：空间和时空数据挑战的框架 显示英文标题

标题： Integrating Expert Knowledge and Recursive Bayesian Inference: A Framework for Spatial and Spatio-Temporal Data Challenges

摘要： 专家判断法是一种关键的方法，可用于应对各学科中的数据匮乏问题。 但更重要的是，它还可以通过缓解观测数据的局限性（如不完整性和可靠性问题）来补充大数据分析，从而通过整合不同或冲突的数据源来提升模型估计。本文还概述了在集成嵌套拉普拉斯近似方法中整合先验信息的各种策略，并提出了一种递归方法，允许在新数据到达时进行分析。 本文提出了一个全面的专家判断法方法，特别强调空间和时空背景。具体而言，它介绍了一种基于专家的模型实现类型学，解决了观测数据与专家数据之间不同的支持变化情景。还提供了详细示例，以说明实施专家判断法和递归推理的清晰且可复制的程序。 显示英文摘要

摘要： Expert elicitation is a critical approach for addressing data scarcity across various disciplines. But moreover, it can also complement big data analytics by mitigating the limitations of observational data, such as incompleteness and reliability issues, thereby enhancing model estimates through the integration of disparate or conflicting data sources. The paper also outlines various strategies for integrating prior information within the Integrated Nested Laplace Approximation method and proposes a recursive approach that allows for the analysis of new data as it arrives. This paper presents a comprehensive approach to expert elicitation, with a particular emphasis on spatial and spatio-temporal contexts. Specifically, it introduces a typology of expert-based model implementations that addresses different change of support scenarios between observational and expert data. Detailed examples illustrating clear and replicable procedures for implementing expert elicitation and recursive inference are also presented.