方法对比

	带缺失数据的Metropolis-Hastings算法	数据增强 (Data Augmentation)
领域	贝叶斯	深度学习
方法族	Bayesian methods	Machine learning
起源年份	1953 / 1987	2019
提出者	Metropolis et al. (1953); missing-data extension formalised by Tanner & Wong (1987)	Connor Shorten & Taghi Khoshgoftaar
类型	MCMC sampler with latent-variable augmentation	Regularization / data preprocessing technique
开创性文献	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.
别名	MH with missing data, Metropolis-Hastings data augmentation, MCMC missing data imputation, MH data-augmentation sampler	Training Data Augmentation, Image Augmentation, Veri Artırma, Synthetic Data Augmentation
相关	6	2
摘要	Metropolis-Hastings with missing data treats unobserved values as latent variables and samples them jointly with model parameters inside a single MCMC chain. By augmenting the target distribution to include both parameters and missing values, the algorithm yields properly calibrated posterior inference without discarding incomplete cases or requiring a separate imputation step.	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.