2012/07/04 by Mark Chavira, David L. Allen, Chavira, Mark +3
Computer Science · Decision Sciences · #Anomaly Detection Techniques and Applications #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #Data Quality and Management #FOS: Computer and information sciences
paper · pdf · doi:10.48550/arxiv.1207.1372
openalex publication_date 2012/07/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We define the notion of compiling a Bayesian network with evidence and provide a specific approach for evidence-based compilation, which makes use of logical processing. The approach is practical and advantageous in a number of application areas-including maximum likelihood estimation, sensitivity analysis, and MAP computations-and we provide specific empirical results in the domain of genetic linkage analysis. We also show that the approach is applicable for networks that do not contain determinism, and show that it empirically subsumes the performance of the quickscore algorithm when applied to noisy-or networks.