পদ্ধতির তুলনা করুন

	মার্কভ চেইন মন্টি কার্লো (MCMC)	সাধারণ ন্যূনতম বর্গক্ষেত্র (OLS) রিগ্রেশন
ক্ষেত্র	বেইসীয়	অর্থমিতি
পরিবার	Bayesian methods	Regression model
উদ্ভবের বছর	—	2019
প্রবর্তক	—	Wooldridge (textbook treatment); classical least squares
ধরন	Posterior sampling algorithm	Linear regression
মৌলিক উৎস	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	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
অপর নাম	markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo)	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
সম্পর্কিত	3	5
সারসংক্ষেপ	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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).