Data augmentation information

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Data augmentation is a statistical technique which allows maximum likelihood estimation from incomplete data.^[1]^[2] Data augmentation has important applications in Bayesian analysis,^[3] and the technique is widely used in machine learning to reduce overfitting when training machine learning models,^[4] achieved by training models on several slightly-modified copies of existing data.

^ Dempster, A.P.; Laird, N.M.; Rubin, D.B. (1977). "Maximum Likelihood from Incomplete Data Via the EM Algorithm". Journal of the Royal Statistical Society. Series B (Methodological). 39 (1): 1–22. doi:10.1111/j.2517-6161.1977.tb01600.x.
^ Rubin, Donald (1987). "Comment: The Calculation of Posterior Distributions by Data Augmentation". Journal of the American Statistical Association. 82 (398). doi:10.2307/2289460. JSTOR 2289460.
^ Jackman, Simon (2009). Bayesian Analysis for the Social Sciences. John Wiley & Sons. p. 236. ISBN 978-0-470-01154-6.
^ Shorten, Connor; Khoshgoftaar, Taghi M. (2019). "A survey on Image Data Augmentation for Deep Learning". Mathematics and Computers in Simulation. 6. springer: 60. doi:10.1186/s40537-019-0197-0.

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