The authors introduce an RJMCMC framework with prior, independence, and moment-matching proposals for non-conjugate ddCRP models, validated via simulation and Old Faithful eruption data.
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2026 3representative citing papers
A metric learning method is introduced to learn distance metrics that best capture conditional anomaly patterns in instance-based detection.
Instance-based conditional anomaly detection with optimized distance metrics detects unusual patient-management decisions in two real-world medical datasets.
citing papers explorer
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Bayesian Inference for Non-Conjugate Distance Dependent Chinese Restaurant Process Models
The authors introduce an RJMCMC framework with prior, independence, and moment-matching proposals for non-conjugate ddCRP models, validated via simulation and Old Faithful eruption data.
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Distance metric learning for conditional anomaly detection
A metric learning method is introduced to learn distance metrics that best capture conditional anomaly patterns in instance-based detection.
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Conditional anomaly detection methods for patient-management alert systems
Instance-based conditional anomaly detection with optimized distance metrics detects unusual patient-management decisions in two real-world medical datasets.