Сравнение на методи

	Bayesian Multiple Correspondence Analysis	Кореспондентен анализ
Област	Статистика	Статистика
Семейство	Latent structure	Latent structure
Година на възникване	2000s–2010s	1984
Създател	Extension of MCA (Benzecri, 1973) with Bayesian inference	Jean-Paul Benzécri; Michael Greenacre
Тип	Bayesian dimension reduction for categorical data	Exploratory multivariate technique for categorical data
Основополагащ източник	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
Други названия	Bayesian MCA, BMCA, Bayesian multiway correspondence analysis, Bayesian categorical dimension reduction	CA, Simple Correspondence Analysis, Reciprocal Averaging, Karşılıklı Uyum Analizi
Свързани	5	2
Резюме	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.