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Learning from Censored and Dependent Data: The case of Linear Dynamics

By Orestis Plevrakis
Observations from dynamical systems often exhibit irregularities, such as censoring, where values are recorded only if they fall within a certain range. Censoring is ubiquitous in practice, due to saturating sensors, limit-of-detection effects, and image-frame effects. In light of recent developments on learning linear dynamical systems (LDSs), and on censored... Show more
October 6, 2023
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Learning from Censored and Dependent Data: The case of Linear Dynamics
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