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

2025-05-30

AI Summary

Modeling spatial and spatiotemporal data is challenged by scarcity, incomplete observations, and heterogeneity across multiple sources, leading to substantial epistemic uncertainty. Method: This paper proposes a dynamic modeling framework that integrates domain expertise with recursive Bayesian inference. Specifically: (1) it formalizes expert knowledge via a structured expert model enabling typological reasoning and cross-scale prior encoding; (2) it introduces, for the first time, a recursive Bayesian updating mechanism based on integrated nested Laplace approximation (INLA), enabling online, joint assimilation of expert priors and streaming observations. Contribution/Results: Evaluated across spatial epidemiology and environmental modeling case studies, the framework significantly improves predictive accuracy and robustness under data sparsity, demonstrates strong reproducibility and real-time adaptability, and establishes a novel paradigm for spatiotemporal inference in small-sample, high-uncertainty settings.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Addressing data scarcity via expert knowledge integration
• Enhancing model estimates by combining diverse data sources
• Proposing recursive methods for real-time spatial data analysis

Innovation

• Integrates expert knowledge with Bayesian inference
• Uses recursive approach for real-time data analysis
• Combines observational and expert data effectively