Understanding parameter differences between analyses employing nested data subsets

Abstract

We provide an analytical argument for understanding the likely nature of parameter shifts between those coming from an analysis of a dataset and from a subset of that dataset, assuming differences are down to noise and any intrinsic variance alone. This gives us a measure against which we can interpret changes seen in parameters and make judgements about the coherency of the data and the suitability of a model in describing those data.

7 pages, 3 figures

References in corpus (2)

• A Detailed Description of the CamSpec Likelihood Pipeline and a Reanalysis of the Planck High Frequency Maps
• Quantifying Suspiciousness Within Correlated Data Sets

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