পদ্ধতির তুলনা করুন

	ডিরিচলেট প্রসেস মিক্সচার মডেল (DPMM)	মার্কভ চেইন মন্টি কার্লো (MCMC)
ক্ষেত্র	বেইসীয়	বেইসীয়
পরিবার	Bayesian methods	Bayesian methods
উদ্ভবের বছর	1973	—
প্রবর্তক	Ferguson (1973); mixture model formulation by Lo (1984)	—
ধরন	Nonparametric Bayesian mixture model	Posterior sampling algorithm
মৌলিক উৎস	Ferguson, T. S. (1973). A Bayesian analysis of some nonparametric problems. The Annals of Statistics, 1(2), 209–230.	Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955
অপর নাম	DPMM, DP mixture model, infinite mixture model, Dirichlet process mixture	markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo)
সম্পর্কিত	3	3
সারসংক্ষেপ	The Dirichlet Process Mixture Model (DPMM) is a nonparametric Bayesian clustering method introduced through Ferguson's (1973) Dirichlet process prior that places a probability distribution over distributions. Unlike finite mixture models, the DPMM does not require the analyst to specify the number of clusters in advance; instead it infers the number of components from the data, allowing an effectively unbounded mixture that grows as more observations arrive.	Markov Chain Monte Carlo (MCMC) is a family of computational algorithms for sampling from complex probability distributions, most commonly the posterior distributions that arise in Bayesian inference. Rather than computing posteriors analytically — which is rarely possible for realistic models — MCMC constructs a Markov chain whose stationary distribution is the target posterior and draws dependent samples from it, enabling full probabilistic inference for virtually any model.