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Temporal state change Bayesian networks for modeling of evolving multivariate state sequences: model, structure discovery and parameter estimation

, , and . Data Mining and Knowledge Discovery, (2021)

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Temporal state change Bayesian networks for modeling of evolving multivariate state sequences: model, structure discovery and parameter estimation, , and . Data Mining and Knowledge Discovery, (2021)Specification Mining in High dimensional Heterogeneous Data Sets of Large-Scale Distributed Systems.. Technical University of Munich, Germany, (2021)Discovering Groups of Signals in In-Vehicle Network Traces for Redundancy Detection and Functional Grouping., , and . ECML/PKDD (3), volume 11053 of Lecture Notes in Computer Science, page 86-102. Springer, (2018)Context by Proxy: Identifying Contextual Anomalies Using an Output Proxy., , , , and . KDD, page 2059-2068. ACM, (2019)Pre-ignition Detection Using Deep Neural Networks: A Step Towards Data-driven Automotive Diagnostics., , , , and . ITSC, page 176-183. IEEE, (2018)Analysing the driving load on electric vehicles using unsupervised segmentation models as enabler to determine the time of battery replacement and assess driving mileage., , , and . EVER, page 1-7. IEEE, (2018)Temporal state change Bayesian networks for modeling of evolving multivariate state sequences: model, structure discovery and parameter estimation., , and . Data Min. Knowl. Discov., 36 (1): 240-294 (2022)Learning Temporal Specifications from Imperfect Traces Using Bayesian Inference., , , and . DAC, page 96. ACM, (2019)