Сравнение методов

	Сэмплирование Гиббса для пропущенных данных	Аугментация данных
Область	Байесовские методы	Глубокое обучение
Семейство	Bayesian methods	Machine learning
Год появления	1987–1990	2019
Автор метода	Tanner & Wong (data augmentation), Gelfand & Smith (Gibbs sampler)	Connor Shorten & Taghi Khoshgoftaar
Тип	Bayesian computational method	Regularization / data preprocessing technique
Основополагающий источник	Tanner, M. A. & Wong, W. H. (1987). The calculation of posterior distributions by data augmentation. Journal of the American Statistical Association, 82(398), 528–540.	Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of Big Data, 6, 60.
Другие названия	data augmentation Gibbs sampler, Gibbs sampler with data augmentation, Bayesian imputation via Gibbs sampling, MCMC missing data imputation	Training Data Augmentation, Image Augmentation, Veri Artırma, Synthetic Data Augmentation
Связанные	6	2
Сводка	Gibbs sampling with missing data treats unobserved values as additional unknowns alongside model parameters and samples all of them jointly within a Markov chain Monte Carlo loop. The method alternates between drawing the missing values from their conditional distribution given the parameters and drawing the parameters from their conditional distribution given the completed data, producing a posterior over both simultaneously.	Data augmentation is a family of techniques that artificially expands a training dataset by applying label-preserving transformations to existing samples. Originally systematized for image classification tasks, it is now applied broadly across vision, text, audio, and tabular domains. It emerged as a practical answer to the chronic scarcity of labeled data in supervised deep learning and remains a standard preprocessing step in modern neural network pipelines.