Сравнение методов

	Байесовский множественный соответственный анализ (BMCA)	Множественный анализ соответствий (MCA)
Область	Статистика	Статистика
Семейство	Latent structure	Latent structure
Год появления	2000s–2010s	2006
Автор метода	Extension of MCA (Benzecri, 1973) with Bayesian inference	Greenacre & Blasius
Тип	Bayesian dimension reduction for categorical data	Multivariate exploratory ordination
Основополагающий источник	Greenacre, M. & Blasius, J. (Eds.) (2006). Multiple Correspondence Analysis and Related Methods. Chapman & Hall/CRC. ISBN: 978-1584886280	Greenacre, M., & Blasius, J. (Eds.). (2006). Multiple Correspondence Analysis and Related Methods. Chapman & Hall/CRC. ISBN: 978-1-58488-628-0
Другие названия	Bayesian MCA, BMCA, Bayesian multiway correspondence analysis, Bayesian categorical dimension reduction	MCA, Homogeneity Analysis, Multiple Nominal Component Analysis, Çoklu Uyum Analizi
Связанные	5	2
Сводка	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.	Multiple Correspondence Analysis (MCA) is a multivariate ordination technique designed to explore and visualize associations among three or more categorical variables simultaneously. By mapping both observations and variable categories onto a shared low-dimensional space, MCA reveals hidden structure in nominal or ordinal survey data. The method was comprehensively systematized and extended by Michael Greenacre and Jorg Blasius in their 2006 edited volume, building on earlier geometric data analysis traditions developed in France by Jean-Paul Benzecri during the 1960s and 1970s.