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Bayesian Network Scan Statistics for Multivariate Pattern Detection
We review three recently proposed scan statistic methods for multivariate pattern detection. Each method models the relationship between multiple observed and hidden variables using a Bayesian network structure, drawing inferences about the underlying pattern type and the affected subset of the data. We first discuss the multivariate Bayesian scan statistic (MBSS) proposed by Neill and Cooper (2008) . MBSS is a stream-based event surveillance framework that detects and characterizes eventsdoi:10.1007/978-0-8176-4749-0_11 fatcat:tjx6q357vfgbndo4itj6ohmuca