Exposure-integrated Gaussian processes allow prediction of both latent stellar signals and instrument-specific binned versions, supporting combination of overlapping EPRV datasets with varying exposure times.
@doi [ ] 10.1086/501068, https://ui.adsabs.harvard.edu/#abs/2006ApJ...638L..51M 638
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dynesty is an open-source Python package for dynamic nested sampling that improves efficiency in Bayesian posterior and evidence estimation compared to MCMC on certain problems.
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Exposure-averaged Gaussian Processes for Combining Overlapping Datasets
Exposure-integrated Gaussian processes allow prediction of both latent stellar signals and instrument-specific binned versions, supporting combination of overlapping EPRV datasets with varying exposure times.
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dynesty: A Dynamic Nested Sampling Package for Estimating Bayesian Posteriors and Evidences
dynesty is an open-source Python package for dynamic nested sampling that improves efficiency in Bayesian posterior and evidence estimation compared to MCMC on certain problems.