Kalman-smoother imputation of data gaps in simulated LISA/Taiji-style observations removes the mass-ratio and spin biases caused by windowing, at lower cost than noise inpainting.
Here the evidence p(d) stays constant for the same set of data throughout our work and is treated as stationary
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A Kalman-smoother based data imputation strategy to data gaps in spaceborne gravitational wave detectors
Kalman-smoother imputation of data gaps in simulated LISA/Taiji-style observations removes the mass-ratio and spin biases caused by windowing, at lower cost than noise inpainting.