<ul><li>Detecting localized density differences in multivariate data is a crucial task in computational science.</li><li>Introducing EagleEye, an anomaly detection method for identifying local density anomalies in multivariate datasets.</li><li>Anomalies are detected by modelling the ordered sequence of each point's neighbors' membership labels as a coin-flipping process.</li><li>EagleEye successfully detects anomalies in synthetic and real-world datasets, including particle decay events in Large Hadron Collider data and changes in temperature fields.</li></ul>

Detecting Localized Density Anomalies in Multivariate Data via Coin-Flip Statistics

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