विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| बेयसियन रिग्रेशन× | मार्कोव चेन मोंटे कार्लो (MCMC)× | साधारण न्यूनतम वर्ग (OLS) समाश्रयण× | |
|---|---|---|---|
| क्षेत्र≠ | बायेसियन | बायेसियन | अर्थमिति |
| परिवार≠ | Bayesian methods | Bayesian methods | Regression model |
| उद्भव वर्ष≠ | — | — | 2019 |
| प्रवर्तक≠ | — | — | Wooldridge (textbook treatment); classical least squares |
| प्रकार≠ | Bayesian linear model | Posterior sampling algorithm | Linear regression |
| मौलिक स्रोत≠ | 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 | 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 |
| उपनाम≠ | bayesian linear regression, probabilistic regression, bayesian regresyon | markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| संबंधित≠ | 2 | 3 | 5 |
| सारांश≠ | Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off. | 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). |
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