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Collaborating Authors

 Cutler, Sean


Bayesian Detection of Infrequent Differences in Sets of Time Series with Shared Structure

Neural Information Processing Systems

We present a hierarchical Bayesian model for sets of related, but different, classes of time series data. Our model performs alignment simultaneously across all classes, while detecting and characterizing class-specific differences. During inference themodel produces, for each class, a distribution over a canonical representation ofthe class.