Searching for bias and correlations in a Bayesian way

Alexandre Posada (U. Aix-Marseille; Caroline Heneka (Dark Cosmology Ctr.); CPT); Inst. Phys.); ITP); Luca Amendola (U. Heidelberg; Valerio Marra (U. Fed. Rio de Janeiro

Searching for bias and correlations in a Bayesian way

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arxiv 1407.2531 v1 pith:BWIG6L7Q submitted 2014-07-09 astro-ph.CO astro-ph.HE

Searching for bias and correlations in a Bayesian way

Caroline Heneka (Dark Cosmology Ctr.) , Alexandre Posada (U. Aix-Marseille , CPT) , Valerio Marra (U. Fed. Rio de Janeiro , Inst. Phys.) , Luca Amendola (U. Heidelberg , ITP) This is my paper

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A range of Bayesian tools has become widely used in cosmological data treatment and parameter inference (see Kunz, Bassett & Hlozek (2007), Trotta (2008), Amendola, Marra & Quartin (2013)). With increasingly big datasets and higher precision, tools that enable us to further enhance the accuracy of our measurements gain importance. Here we present an approach based on internal robustness, introduced in Amendola, Marra & Quartin (2013) and adopted in Heneka, Marra & Amendola (2014), to identify biased subsets of data and hidden correlation in a model independent way.

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