方法对比

	贝叶斯多重对应分析 (BMCA)	对应分析
领域	统计学	统计学
方法族	Latent structure	Latent structure
起源年份	2000s–2010s	1984
提出者	Extension of MCA (Benzecri, 1973) with Bayesian inference	Jean-Paul Benzécri; Michael Greenacre
类型	Bayesian dimension reduction for categorical data	Exploratory multivariate technique for categorical data
开创性文献	Greenacre, M. & Blasius, J. (Eds.) (2006). Multiple Correspondence Analysis and Related Methods. Chapman & Hall/CRC. ISBN: 978-1584886280	Greenacre, M. J. (1984). Theory and Applications of Correspondence Analysis. Academic Press. ISBN: 978-0-12-299050-2
别名	Bayesian MCA, BMCA, Bayesian multiway correspondence analysis, Bayesian categorical dimension reduction	CA, Simple Correspondence Analysis, Reciprocal Averaging, Karşılıklı Uyum Analizi
相关	5	2
摘要	Bayesian Multiple Correspondence Analysis extends classical MCA by embedding the geometric decomposition of categorical data tables within a Bayesian probabilistic framework, enabling principled uncertainty quantification around category coordinates, dimension selection via marginal likelihood, and incorporation of prior knowledge about variable relationships.	Correspondence Analysis (CA) is an exploratory multivariate technique for visualizing the association structure of a two-way contingency table. Developed systematically by Jean-Paul Benzécri in France during the 1960s–1970s and brought to an English-language audience by Michael Greenacre in 1984, CA decomposes the chi-square statistic of a cross-tabulation to produce a low-dimensional joint display — called a biplot — in which rows and columns are represented as points whose proximities reflect their associations.