2024/03/16 by Katie Buchhorn, Buchhorn, Katie, Kerrie Mengersen +5
Computer Science · Decision Sciences · Medicine · #Advanced Statistical Process Monitoring #Applications (stat.AP) #Bayesian Methods and Mixture Models #Data-Driven Disease Surveillance #FOS: Computer and information sciences
paper · pdf · doi:10.48550/arxiv.2403.10791
openalex publication_date 2024/03/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Data collected from arrays of sensors are essential for informed decision-making in various systems. However, the presence of anomalies can compromise the accuracy and reliability of insights drawn from the collected data or information obtained via statistical analysis. This study aims to develop a robust Bayesian optimal experimental design (BOED) framework with anomaly detection methods for high-quality data collection. We introduce a general framework that involves anomaly generation, detection and error scoring when searching for an optimal design. This method is demonstrated using two comprehensive simulated case studies: the first study uses a spatial dataset, and the second uses a spatio-temporal river network dataset. As a baseline approach, we employed a commonly used prediction-based utility function based on minimising errors. Results illustrate the trade-off between predictive accuracy and anomaly detection performance for our method under various design scenarios. An optimal design robust to anomalies ensures the collection and analysis of more trustworthy data, playing a crucial role in understanding the dynamics of complex systems such as the environment, therefore enabling informed decisions in monitoring, management, and response.