পদ্ধতির তুলনা করুন

	Bayesian Multiple Correspondence Analysis	সুপ্ত শ্রেণী বিশ্লেষণ (Latent Class Analysis - LCA)
ক্ষেত্র	পরিসংখ্যান	পরিসংখ্যান
পরিবার	Latent structure	Latent structure
উদ্ভবের বছর	2000s–2010s	1950s–1968
প্রবর্তক	Extension of MCA (Benzecri, 1973) with Bayesian inference	Paul F. Lazarsfeld
ধরন	Bayesian dimension reduction for categorical data	Latent variable / person-centered classification
মৌলিক উৎস	Greenacre, M. & Blasius, J. (Eds.) (2006). Multiple Correspondence Analysis and Related Methods. Chapman & Hall/CRC. ISBN: 978-1584886280	Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61(2), 215–231.
অপর নাম	Bayesian MCA, BMCA, Bayesian multiway correspondence analysis, Bayesian categorical dimension reduction	LCA, latent class model, latent categorical analysis, finite mixture of multinomials
সম্পর্কিত	5	6
সারসংক্ষেপ	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.	Latent class analysis identifies unobserved subgroups — latent classes — within a population by finding patterns of responses across a set of categorical observed indicators. It is the categorical-variable counterpart of cluster analysis, but grounded in an explicit probabilistic model, and is widely used in social, health, and behavioral sciences to discover typologies in survey or diagnostic data.