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Bayesian hypothesis testing in machine learning

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2015) 9286 199-202
DOI: 10.1007/978-3-319-23461-8_13
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Abstract

Most hypothesis testing in machine learning is done using the frequentist null-hypothesis significance test, which has severe drawbacks. We review recent Bayesian tests which overcome the drawbacks of the frequentist ones.

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Corani, G., Benavoli, A., Mangili, F., & Zaffalon, M. (2015). Bayesian hypothesis testing in machine learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9286, pp. 199–202). Springer Verlag. https://doi.org/10.1007/978-3-319-23461-8_13

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