A multi-author overview of machine-learning algorithms proposed for instrument modelling, data analysis, simulation and inference in SKA Cosmic Dawn and Epoch of Reionization science.
Searching for bias and correlations in a Bayesian way
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abstract
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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Machine Learning and the SKA for Cosmic Dawn and the Epoch of Reionization
A multi-author overview of machine-learning algorithms proposed for instrument modelling, data analysis, simulation and inference in SKA Cosmic Dawn and Epoch of Reionization science.